Search

Purpose: The liver segments divided by Couinaud classification method are used to understand the functional anatomy of liver, which is significant in hepatic resection surgery. In Couinaud classification method, each third‐order branch of the portal vein (PV) defines the supplied territory of a corresponding liver segment. However, the accuracies of the reconstruction and classification of PV are affected by the complicated structure of the vein. The purpose of this paper is to develop a separation and classification method that can accurately extract the liver segments. Methods: In this paper, a multiple feature‐based method is proposed to obtain liver segments. Because the portal and hepatic veins usually connect in the vessel segmentation result, the PV is first completely separated based on the different strategies for minimal node cut using fused features of topology and appearance. Meanwhile, all bifurcation nodes of PV are detected. The bifurcation nodes are initial ordered through their linkages to classify the branches. Then, the feature of the vascular topology is used to refine the orders of bifurcation nodes. The bifurcation nodes with the refined orders classify the branches between them, and the third‐order branches of PV are obtained. The liver segments are eventually obtained through the third‐order branches. Results: The separation and classification in the proposed method are evaluated on the CT and MR datasets. The average values of Dice, Jaccard, Recall, and Precision obtained by the proposed method are 93.00%, 87.90%, 93.47%, and 93.19%, respectively. Compared with the state‐of‐the‐art methods, the separation results obtained by the proposed method are more accurate. The branches of PV are classified based on the separation result. According to the third‐order branches, eight liver segments correspond to the different functional areas are precisely extracted. Conclusions: The proposed method achieves a high accuracy for the liver segment extraction. And the extracted liver segments are significant for the preplanning of resection surgery. [ABSTRACT FROM AUTHOR]

Purpose: Liver disease causes significant morbidity and mortality worldwide. Liver imaging plays an essential role in the noninvasive liver disease evaluation because of the limitation of liver biopsy. This paper aims to image in vivo liver with thermoacoustic imaging (TAI) and demonstrate this liver imaging technique with a cross‐validation method. Methods: The imaging system composed of a large aperture antenna and a flexible transducer array was used, and we performed the position calibration using the delay and sum algorithm. The localization wire was utilized in the cross‐validation of in vivo liver TAI. Results: We successfully validated in vivo liver TAI. In vivo images of different liver lobes without labels were observed. The imaging depth reached about 4 cm. Conclusions: TAI has the potential to image the liver and provide useful dielectric properties of the liver tissue. This study realized the first in vivo liver TAI, suggesting its prospect in detecting liver disease noninvasively. [ABSTRACT FROM AUTHOR]

Purpose: Phantom-based objective image quality assessment methods are widely used in the medical physics community. For a filtered backprojection (FBP) reconstruction-based linear or quasilinear imaging system, the use of this methodology is well justified. Many key image quality metrics acquired with phantom studies can be directly applied to in vivo human subject studies. Recently, a variety of image quality metrics have been investigated for model-based iterative image reconstruction (MBIR) methods and several novel characteristics have been discovered in phantom studies. However, the following question remains unanswered: can certain results obtained from phantom studies be generalized to in vivo animal studies and human subject studies? The purpose of this paper is to address this question. Methods: One of the most striking results obtained from phantom studies is a novel power-law relationship between noise variance of MBIR (σ²) and tube current-rotation time product (mAs): σ² ∝ (mAs)-0.4 [K. Li et al., "Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance," Med. Phys. 41, 041906 (15pp.) (2014)]. To examine whether the same power-law works for in vivo cases, experimental data from two types of in vivo studies were analyzed in this paper. All scans were performed with a 64-slice diagnostic CT scanner (Discovery CT750 HD, GE Healthcare) and reconstructed with both FBP and a MBIR method (Veo, GE Healthcare). An Institutional Animal Care and Use Committee-approved in vivo animal study was performed with an adult swine at six mAs levels (10-290). Additionally, human subject data (a total of 110 subjects) acquired from an IRB-approved clinical trial were analyzed. In this clinical trial, a reduced-mAs scan was performed immediately following the standard mAs scan; the specific mAs used for the two scans varied across human subjects and were determined based on patient size and clinical indications. The measurements of σ² were performed at different mAs by drawing regions-of-interest (ROIs) in the liver and the subcutaneous fat. By applying a linear least-squares regression, the β values in the power-law relationship σ² ∝ (mAs)-β were measured for the in vivo data and compared with the value found in phantom experiments. Results: For the in vivo swine study, an exponent of &#946 = 0.43 was found for MBIR, and the coefficient of determination (R²) for the corresponding least-squares power-law regression was 0.971. As a reference, the β and R² values for FBP were found to be 0.98 and 0.997, respectively, from the same study, which are consistent with the well-known σ² ?∝ (mAs)-1.0 relationship for linear CT systems. For the human subject study, the measured β values for the MBIR images were 0.41?±0.12 in the liver and 0.37?±0.12 in subcutaneous fat. In comparison, the β values for the FBP images were 1.04±0.10 in the liver and 0.97±0.12 in subcutaneous fat. The β values of MBIR and FBP obtained from the in vivo studies were found to be statistically equivalent to the corresponding β values from the phantom study within an equivalency interval of [-0.1, 0.1] (p < 0.05); across MBIR and FBP, the difference in β was statistically significant (p < 0.05). Conclusions: Despite the nonlinear nature of the MBIR method, the power-law relationship, σ² ẙ (mAs)-0.4, found from phantom studies can be applied to in vivo animal and human subject studies. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel probability-based sorting method capable of generating multiple breathing cycles of 4D-MRI images and to evaluate performance of this new method by comparing with conventional phase-based methods in terms of image quality and tumor motion measurement. Methods: Based on previous findings that breathing motion probability density function (PDF) of a single breathing cycle is dramatically different from true stabilized PDF that resulted from many breathing cycles, it is expected that a probability-based sorting method capable of generating multiple breathing cycles of 4D images may capture breathing variation information missing from conventional single-cycle sorting methods. The overall idea is to identify a few main breathing cycles (and their corresponding weightings) that can best represent the main breathing patterns of the patient and then reconstruct a set of 4D images for each of the identified main breathing cycles. This method is implemented in three steps: (1) The breathing signal is decomposed into individual breathing cycles, characterized by amplitude, and period; (2) individual breathing cycles are grouped based on amplitude and period to determine the main breathing cycles. If a group contains more than 10% of all breathing cycles in a breathing signal, it is determined as a main breathing pattern group and is represented by the average of individual breathing cycles in the group; (3) for each main breathing cycle, a set of 4D images is reconstructed using a result-driven sorting method adapted from our previous study. The probability-based sorting method was first tested on 26 patients' breathing signals to evaluate its feasibility of improving target motion PDF. The new method was subsequently tested for a sequential image acquisition scheme on the 4D digital extended cardiac torso (XCAT) phantom. Performance of the probability-based and conventional sorting methods was evaluated in terms of target volume precision and accuracy as measured by the 4D images, and also the accuracy of average intensity projection (AIP) of 4D images. Results: Probability-based sorting showed improved similarity of breathing motion PDF from 4D images to reference PDF compared to single cycle sorting, indicated by the significant increase in Dice similarity coefficient (DSC) (probability-based sorting, DSC = 0.89±0.03, and single cycle sorting, DSC = 0.83±0.05, p-value <0.001). Based on the simulation study on XCAT, the probability-based method outperforms the conventional phase-based methods in qualitative evaluation on motion artifacts and quantitative evaluation on tumor volume precision and accuracy and accuracy of AIP of the 4D images. Conclusions: In this paper the authors demonstrated the feasibility of a novel probability-based multicycle 4D image sorting method. The authors' preliminary results showed that the new method can improve the accuracy of tumor motion PDF and the AIP of 4D images, presenting potential advantages over the conventional phase-based sorting method for radiation therapy motion management. [ABSTRACT FROM AUTHOR]

Purpose: Percutaneous ablation under MRI-guidance allows treating otherwise inoperable liver tumors locally using a catheter probe. However, manually placing the probe is an error-prone and time consuming task that requires a considerable amount of training. The aim of this paper was to present a pneumatically actuated robotic instrument that can assist clinicians in MRI-guided percutaneous intervention of the liver and to assess its functionality in a clinical setting. The robot positions a needle-guide inside the MRI scanner bore and assists manual needle insertions outside the bore. Methods: The robot supports double oblique insertions that are particularly challenging for less experienced clinicians. Additionally, the system employs only standard imaging sequences and can therefore be used on different MRI scanners without requiring prior integration. The repeatability and the accuracy of the robot were evaluated with an optical tracking system. The functionality of the robot was assessed in an initial pilot study on two patients that underwent MRI-guided laser ablation of the liver. Results: The robot positioned the needle-guide in a repeatable manner with a mean error of 0.35 mm and a standard deviation of 0.32 mm. The mean position error corresponding to the needle tip, measured for an equivalent needle length of 195 mm over 25 fixed points, was 2.5 mm with a standard deviation of 1.2 mm. The pilot study confirmed that the robot does not interfere with the equipment used for MRI-guided laser ablation and does not visibly affect the MR images. The robot setup integrated seamlessly within the established clinical workflow. The robot-assisted procedure was successfully completed on two patients, one of which required a complex double oblique insertion. For both patients, the insertion depth and the tumor size were within the range reported for previous MRI-guided percutaneous interventions. A third patient initially enrolled in the pilot study and was considerably heavier than the others, preventing the use of the robot and requiring several freehand insertion attempts. Conclusions: The robot repeatability and accuracy are appropriate for liver tumors normally treated with MRI-guided ablation. The results of the pilot study endorse the clinical use of the robot in its current form: the robot is fully functional and MRI-compatible in a clinical setting and is suitable for double-oblique needle insertions. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors proposed a new 3D registration algorithm, 3D-scale invariant feature transform (SIFT)-Flow, for multiatlas-based liver segmentation in computed tomography (CT) images. Methods: In the registration work, the authors developed a new registration method that takes advantage of dense correspondence using the informative and robust SIFT feature. The authors computed the dense SIFT features for the source image and the target image and designed an objective function to obtain the correspondence between these two images. Labeling of the source image was then mapped to the target image according to the former correspondence, resulting in accurate segmentation. In the fusion work, the 2D-based nonparametric label transfer method was extended to 3D for fusing the registered 3D atlases. Results: Compared with existing registration algorithms, 3D-SIFT-Flow has its particular advantage in matching anatomical structures (such as the liver) that observe large variation/deformation. The authors observed consistent improvement over widely adopted state-of-the-art registration methods such as ELASTIX, ANTS, and multiatlas fusion methods such as joint label fusion. Experimental results of liver segmentation on the MICCAI 2007 Grand Challenge are encouraging, e.g., Dice overlap ratio 96.27%±0.96% by our method compared with the previous state-of-the-art result of 94.90%±2.86%. Conclusions: Experimental results show that 3D-SIFT-Flow is robust for segmenting the liver from CT images, which has large tissue deformation and blurry boundary, and 3D label transfer is effective and efficient for improving the registration accuracy. [ABSTRACT FROM AUTHOR]

Purpose: This paper describes a method to achieve consistent clinical image quality in 18F-FDG scans accounting for patient habitus, dose regimen, image acquisition, and processing techniques. Methods: Oncological PET/CT scan data for 58 subjects were evaluated retrospectively to derive analytical curves that predict image quality. Patient noise equivalent count rate and coefficient of variation (CV) were used as metrics in their analysis. Optimized acquisition protocols were identified and prospectively applied to 179 subjects. Results: The adoption of different schemes for three body mass ranges (<60 kg, 60-90 kg, >90 kg) allows improved image quality with both point spread function and ordered-subsets expectation maximization-3D reconstruction methods. The application of this methodology showed that CV improved significantly (p < 0.0001) in clinical practice. Conclusions: Consistent oncological PET/CT image quality on a high-performance scanner was achieved from an analysis of the relations existing between dose regimen, patient habitus, acquisition, and processing techniques. The proposed methodology may be used by PET/CT centers to develop protocols to standardize PET/CT imaging procedures and achieve better patient management and cost-effective operations. [ABSTRACT FROM AUTHOR]

Purpose: In image-guided radio frequency ablation for liver cancer treatment, pre- and post-interventional CT images are typically used to verify the treatment success of the therapy. In current clinical practice, the tumor zone in the diagnostic, preinterventional images is mentally or manually mapped to the ablation zone in the post-interventional images to decide success of the treatment. However, liver deformation and differences in image quality as well as in texture of the ablation zone and the tumor area make the mental or manual registration a challenging task. Purpose of this paper is to develop an automatic framework to register the pre-interventional image to the post-interventional image. Methods: The authors propose a registration approach enabling a nonrigid deformation of the tumor to the ablation zone, while keeping locally rigid deformation of the tumor area. The method was evaluated on CT images of 38 patient datasets from Erasmus MC. The evaluation is based on Dice coefficients of the liver segmentation on both the pre-interventional and post-interventional images, and mean distances between the liver segmentations. Additionally, residual distances after registration between corresponding landmarks and local mean surface distance in the images were computed. Results: The results show that rigid registration gives a Dice coefficient of 87.9%, a mean distance of the liver surfaces of 5.53 mm, and a landmark error of 5.38 mm, while non-rigid registration with local rigid deformation has a Dice coefficient of 92.2%, a mean distance between the liver segmentation boundaries near the tumor area of 3.83 mm, and a landmark error of 2.91 mm, where a part of this error can be attributed to the slice spacing in the authors' CT images. Conclusions: This method is thus a promising tool to assess the success of RFA liver cancer treatment. [ABSTRACT FROM AUTHOR]

Purpose: This work is a preliminary study of a stationary cardiac SPECT system. The goal of this research is to propose a stationary cardiac SPECT system using segmented slant-hole collimators and to perform computer simulations to test the feasibility. Compared to the rotational SPECT, a stationary system has a benefit of acquiring temporally consistent projections. The most challenging issue in building a stationary system is to provide sufficient projection view-angles. Methods: A GATE (GEANT4 application for tomographic emission) Monte Carlo model was developed to simulate a two-detector stationary cardiac SPECT that uses segmented slant-hole collimators. Each detector contains seven segmented slant-hole sections that slant to a common volume at the rotation center. Consequently, 14 view-angles over 180 were acquired without any gantry rotation. The NCAT phantom was used for data generation and a tailored maximum-likelihood expectation-maximization algorithm was used for image reconstruction. Effects of limited number of view-angles and data truncation were carefully evaluated in the paper. Results: Simulation results indicated that the proposed segmented slant-hole stationary cardiac SPECT system is able to acquire sufficient data for cardiac imaging without a loss of image quality, even when the uptakes in the liver and kidneys are high. Seven views are acquired simultaneously at each detector, leading to 5-fold sensitivity gain over the conventional dual-head system at the same total acquisition time, which in turn increases the signal-to-noise ratio by 19%. The segmented slant-hole SPECT system also showed a good performance in lesion detection. In our prototype system, a short hole-length was used to reduce the dead zone between neighboring collimator segments. The measured sensitivity gain is about 17-fold over the conventional dual-head system. Conclusions: The GATE Monte Carlo simulations confirm the feasibility of the proposed stationary cardiac SPECT system with segmented slant-hole collimators. The proposed collimator consists of combined parallel and slant holes, and the image on the detector is not reduced in size. [ABSTRACT FROM AUTHOR]

Purpose: For a given imaging task and patient size, the optimal selection of x-ray tube potential (kV) and tube current-rotation time product (mAs) is pivotal in achieving the maximal radiation dose reduction while maintaining the needed diagnostic performance. Although contrast-to-noise (CNR)-based strategies can be used to optimize kV/mAs for computed tomography (CT) imaging systems employing the linear filtered backprojection (FBP) reconstruction method, a more general framework needs to be developed for systems using the nonlinear statistical model-based iterative reconstruction (MBIR) method. The purpose of this paper is to present such a unified framework for the optimization of kV/mAs selection for both FBP- and MBIR-based CT systems. Methods: The optimal selection of kV and mAs was formulated as a constrained optimization problem to minimize the objective function, Dose(kV,mAs), under the constraint that the achievable detectability index d'(kV,mAs) is not lower than the prescribed value of d' for a given imaging task. Since it is difficult to analytically model the dependence of d' on kV and mAs for the highly nonlinear MBIR method, this constrained optimization problem is solved with comprehensive measurements of Dose(kV,mAs) and d'(kV,mAs) at a variety of kV-mAs combinations, after which the overlay of the dose contours and d' contours is used to graphically determine the optimal kV-mAs combination to achieve the lowest dose while maintaining the needed detectability for the given imaging task. As an example, R' for a 17 mm hypoattenuating liver lesion detection task was experimentally measured with an anthropomorphic abdominal phantom at four tube potentials (80, 100, 120, and 140 kV) and fifteen mA levels (25 and 50-700) with a sampling interval of 50 mA at a fixed rotation time of 0.5 s, which corresponded to a dose (CTDIvol) range of [0.6, 70] mGy. Using the proposed method, the optimal kV and mA that minimized dose for the prescribed detectability level of d'R = 16 were determined. As another example, the optimal kV and mA for an 8 mm hyperattenuating liver lesion detection task were also measured using the developed framework. Both an in vivo animal and human subject study were used as demonstrations of how the developed framework can be applied to the clinical work flow. Results: For the first task, the optimal kV and mAs were measured to be 100 and 500, respectively, for FBP, which corresponded to a dose level of 24 mGy. In comparison, the optimal kV and mAs for MBIR were 80 and 150, respectively, which corresponded to a dose level of 4 mGy. The topographies of the iso-d' map and the iso-CNR map were the same for FBP; thus, the use of d'- and CNR-based optimization methods generated the same results for FBP. However, the topographies of the iso-d' and iso-CNR map were significantly different in MBIR; the CNR-based method overestimated the performance of MBIR, predicting an overly aggressive dose reduction factor. For the second task, the developed framework generated the following optimization results: for FBP, kV = 140, mA= 350, dose = 37.5 mGy; for MBIR, kV = 120, mA= 250, dose = 18.8 mGy. Again, the CNR-based method overestimated the performance of MBIR. Results of the preliminary in vivo studies were consistent with those of the phantom experiments. Conclusions: A unified and task-driven kV/mAs optimization framework has been developed in this work. The framework is applicable to both linear and nonlinear CT systems such as those using the MBIR method. As expected, the developed framework can be reduced to the conventional CNR-based kV/mAs optimization frameworks if the system is linear. For MBIR-based nonlinear CT systems, however, the developed task-based kV/mAs optimization framework is needed to achieve the maximal dose reduction while maintaining the desired diagnostic performance. [ABSTRACT FROM AUTHOR]

Purpose: Four-dimensional magnetic resonance imaging (4DMRI) is an emerging technique in radiotherapy treatment planning for organ motion quantification. In this paper, the authors present a novel 4DMRI retrospective image-based sorting method, providing reduced motion artifacts than using a standard monodimensional external respiratory surrogate. Methods: Serial interleaved 2D multislice MRI data were acquired from 24 liver cases (6 volunteers + 18 patients) to test the proposed 4DMRI sorting. Image similarity based on mutual information was applied to automatically identify a stable reference phase and sort the image sequence retrospectively, without the use of additional image or surrogate data to describe breathing motion. Results: The image-based 4DMRI provided a smoother liver profile than that obtained from standard resorting based on an external surrogate. Reduced motion artifacts were observed in image-based 4DMRI datasets with a fitting error of the liver profile measuring 1.2±0.9 mm (median ± interquartile range) vs 2.1±1.7 mm of the standard method. Conclusions: The authors present a novel methodology to derive a patient-specific 4DMRI model to describe organ motion due to breathing, with improved image quality in 4D reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: Apparent diffusion coefficient (ADC), derived from diffusion-weighted magnetic resonance images (DW-MRI), measures the motion of water molecules in vivo and can be used to quantify tumor response to therapy. The accurate measurement of ADC can be adversely affected by organ motion and imaging artifacts. In this paper, the authors' goal was to develop an automated method for reducing artifacts and thereby improve the accuracy of ADC measurements in moving organs such as liver. Methods: The authors developed a novel method of computing ADC with fewer artifacts, through simultaneous image segmentation and iterative registration (SSIR) of multiple b-value DW-MRI. The authors' approach reduces artifacts by automatically finding the best possible alignment between the individual b-value images and a reference DW image using a sequence of transformations. It selects such a sequence by an iterative choice of b-value DW images based on the accuracy of their alignment with the reference DW image. The authors' approach quantifies the accuracy of alignment between a pair of images using modified Hausdroff distance computed between the structures of interest. The structures of interest are identified by a user through strokes drawn in one or more slices in the reference DW image, which are then volumetrically segmented using GrowCut. The same structures are segmented in the remaining b-value images by transforming the user-drawn strokes through registration. The ADC values are computed from all the aligned b-value images. The images are aligned by using affine registration followed by deformable B-spline registration with cubic B-spline resampling. Results: The authors compared the results of ADC computed using their approach with ADC computed (a) without registration and (b) with basic affine registration of all b-value images to a chosen reference. The authors' approach was the most effective in reducing artifacts compared to the other two methods. It resulted in a mean artifact ratio (fraction of voxels in a structure with negative ADC over total number of voxels in the structure) of 2.7% versus 5.4% for affine registration and 32% for no registration for >200 tumors. The authors' approach also resulted in the lowest median standard deviation in the computed mean ADC for all tumors [0.05,0.09,0.07,0.58] compared to those from affine image registration [0.02, 0.14, 0.58, 0.79] and no image registration [0.64, 0.83, 0.83, 1.09] on tests where random displacement [8,10,12,16] pixels were introduced in multiple trials in the b-value images. Conclusions: The authors developed a novel approach for reducing artifacts in ADC maps through simultaneous registration and segmentation of multiple b-value DW images. The authors' method explicitly employs a registration quality metric to align images. When compared to basic affine and no image registrations, the authors' approach produces registrations of greater accuracy with lowest artifact ratio and median standard deviation of the computed mean ADC values for a wide range of displacements. [ABSTRACT FROM AUTHOR]

Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets. Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance. Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases. Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images. [ABSTRACT FROM AUTHOR]

Purpose: Pulsed RF (PRF) is a nonablative technique for treating neuropathic pain. Bipolar PRF application is currently aimed at creating a "strip lesion" to connect the electrode tips; however, the electrical and thermal performance during bipolar PRF is currently unknown. The objective of this paper was to study the temperature and electric field distributions during bipolar PRF. Methods: The authors developed computer models to study temperature and electric field distributions during bipolar PRF and to assess the possible ablative thermal effect caused by the accumulated temperature spikes, along with any possible electroporation effects caused by the electrical field. The authors also modeled the bipolar ablative mode, known as bipolar Continuous Radiofrequency (CRF), in order to compare both techniques. Results: There were important differences between CRF and PRF in terms of electrical and thermal performance. In bipolar CRF: (1) the initial temperature of the tissue impacts on temperature progress and hence on the thermal lesion dimension; and (2) at 37℃, 6-min of bipolar CRF creates a strip thermal lesion between the electrodes when these are separated by a distance of up to 20 mm. In bipolar PRF: (1) an interelectrode distance shorter than 5 mm produces thermal damage (i.e., ablative effect) in the intervening tissue after 6 min of bipolar RF; and (2) the possible electroporation effect (electric fields higher than 150 kV m-1) would be exclusively circumscribed to a very small zone of tissue around the electrode tip. Conclusions: The results suggest that (1) the clinical parameters considered to be suitable for bipolar CRF should not necessarily be considered valid for bipolar PRF, and vice versa; and (2) the ablative effect of the CRF mode is mainly due to its much greater level of delivered energy than is the case in PRF, and therefore at same applied energy levels, CRF, and PRF are expected to result in same outcomes in terms of thermal damage zone dimension. [ABSTRACT FROM AUTHOR]

Purpose: In multiphase contrast-enhanced magnetic resonance imaging (CE-MRI), liver segmentation is an important preprocessing step for the computer-aided evaluation of liver disease. However, because of the liver's irregular shape, proximity to surrounding organs, and large intensity variation, and peripheral contrast enhancement in the kidney, liver segmentation has been very challenging. This paper presents a novel hybrid active contour model and overall procedures that are specific to liver segmentation. Methods: The authors introduced an edge-function-scaled (weighted) region-based active contour model (ESRAC) and utilization of registered, multiphase sequences to address leakage-to-kidney and oversegmentation problems. The model incorporated weighted regional information with a compactly supported edge map, computed from a combination of images obtained during arterial and delayed phases, and it was coupled with a geodesic edge term. To cope with signal-inhomogeneity on MRI, all of the axial slices were partitioned into eight sectors with an angular span of 45°, centered on the inferior vena cava, each in the superior and inferior regions and the regional information regarding ESRAC was computed for each partition, henceforth the so-called partitioned ESRAC (p-ESRAC). Initialization of the active contour was performed by thresholding with a range of [200, +8) and simple morphological operation during the delayed phase. At the end, to fill the holes in the segmented images caused by high gradients around the vasculature, noise, or outstanding texture features, iterative morphological operations were performed until convergence. The authors compared the segmentation accuracy of p-ESRAC to that with geodesic active contour, region-based active contour, geodesic active region, and localized region-based active contour using quantitative and visual assessments. Results: In three-dimensional experimental studies conducted on 30 subjects (14 normal or benign cases and 16 malignant cases), compared with other active contours, p-ESRAC achieved the highest dice similarity coefficients of 93.9 ± 1.6% (normal) and 91.6 ± 2.2% (malignant), respectively. In addition, p-ESRAC resulted in the lowest false positive rates of 4.5 ± 3.2% (normal) and 7.9 ± 3.0% (malignant), demonstrating it to be the most effective in reducing oversegmentation. The partition scheme improved segmentation accuracy by 5.4 ± 9.2% (normal) and 22.2 ± 27.6% (malignant) of the true segmentation that was missed by ESRAC. Visual assessment confirmed that p-ESRAC prevented leakage of the segmentation results of the liver into the kidney. Conclusions: A novel active-contour model was developed, allowing for accurate liver segmentation on multiphase CE-MRI, with conditions that include signal inhomogeneity and weak boundary conditions. Such a technique could be useful for applications that involve computeraided diagnosis of liver disease. [ABSTRACT FROM AUTHOR]

Purpose: Two-dimensional ultrasound (2D US) imaging is commonly used for diagnostic and intraoperative guidance of interventional liver procedures; however, 2D US lacks volumetric information that may benefit interventional procedures. Over the past decade, three-dimensional ultrasound (3D US) has been developed to provide the missing spatial information. 3D US image acquisition is mainly based on mechanical, electromagnetic, and freehand tracking of conventional 2D US transducers, or 2D array transducers available on high-end machines. These approaches share many problems during clinical use for interventional liver imaging due to lack of flexibility and compatibility with interventional equipment, limited field-of-view (FOV), and significant capital cost compared to the benefits they introduce. In this paper, a novel system for mechanical 3D US scanning is introduced to address these issues. Methods: The authors have developed a handheld mechanical 3D US system that incorporates mechanical translation and tilt sector sweeping of any standard 2D US transducer to acquire 3D images. Each mechanical scanning function can be operated independently or may be combined to allow for a hybrid wide FOV acquisition. The hybrid motion mode facilitates registration of other modalities (e.g., CT or MRI) to the intraoperative 3D US images by providing a larger FOV in which to acquire anatomical information. The tilting mechanism of the developed mover allows image acquisition in the intercostal rib space to avoid acoustic shadowing from bone. The geometric and volumetric scanning validity of the 3D US system was evaluated on tissue mimicking US phantoms for different modes of operation. Identical experiments were performed on a commercially available 3D US system for direct comparison. To replicate a clinical scenario, the authors evaluated their 3D US system by comparing it to CT for measurement of angle and distance between interventional needles in different configurations, similar to those used for percutaneous ablation of liver tumors. Results: The mean geometrical hybrid 3D reconstruction error measured from scanning of a known string phantom was less than 1 mm in two directions and 2.5 mm in the scanning direction, which was comparable or better than the same measurements obtained from a commercially available 3D US system. The error in volume measurements of spherical phantom models depended on depth of the object. For a 20 cm3 model at a depth of 15 cm, a standard depth for liver imaging, the mean error was 3.6% ± 4.5% comparable to the 2.3% ± 1.8% error for the 3D US commercial system. The error in 3D US measurement of the tip distance and angle between two microwave ablation antennas inserted into the phantom was 0.9 ± 0.5 mm and 1.1° ± 0.7°, respectively. Conclusions: A 3D US system with hybrid scanning motions for large field-of-view 3D abdominal imaging has been developed and validated. The superior spatial information provided by 3D US might enhance image-guidance for percutaneous interventional treatment of liver malignancies. The system has potential to be integrated with other liver procedures and has application in other abdominal organs such as kidneys, spleen, or adrenals. [ABSTRACT FROM AUTHOR]

Purpose: In image-guided surgery (IGS) intraoperative image acquisition of tissue shape, motion, and morphology is one of the main challenges. Recently, time-of-flight (ToF) cameras have emerged as a new means for fast range image acquisition that can be used for multimodal registration of the patient anatomy during surgery. The major drawbacks of ToF cameras are systematic errors in the image acquisition technique that compromise the quality of the measured range images. In this paper, we propose a calibration concept that, for the first time, accounts for all known systematic errors affecting the quality of ToF range images. Laboratory and in vitro experiments assess its performance in the context of IGS. Methods: For calibration the camera-related error sources depending on the sensor, the sensor temperature and the set integration time are corrected first, followed by the scene-specific errors, which are modeled as function of the measured distance, the amplitude and the radial distance to the principal point of the camera. Accounting for the high accuracy demands in IGS, we use a custom-made calibration device to provide reference distance data, the cameras are calibrated too. To evaluate the mitigation of the error, the remaining residual error after ToF depth calibration was compared with that arising from using the manufacturer routines for several state-of-the-art ToF cameras. The accuracy of reconstructed ToF surfaces was investigated after multimodal registration with computed tomography (CT) data of liver models by assessment of the target registration error (TRE) of markers introduced in the livers. Results: For the inspected distance range of up to 2 m, our calibration approach yielded a mean residual error to reference data ranging from 1.5 ± 4.3 mm for the best camera to 7.2 ± 11.0 mm. When compared to the data obtained from the manufacturer routines, the residual error was reduced by at least 78% from worst calibration result to most accurate manufacturer data. After registration of the CT data with the ToF data, the mean TRE ranged from 3.7 ± 2.1 mm for point-based and 5.7 ± 1.9 mm for surface-based registration for the best camera to 6.2 ± 3.4 and 11.1 ± 2.8 mm, respectively. Compared to data provided by the manufacturer, the mean TRE decreased by 8%-60% for point-based and by 18%-74% for surface-based registration. Conclusions: Using the proposed calibration approach improved the measurement accuracy of all investigated ToF cameras. Although evaluated in the context of intraoperative image acquisition, the proposed calibration procedure can easily be applied to other medical applications using ToF cameras, such as patient positioning or respiratory motion tracking in radiotherapy. [ABSTRACT FROM AUTHOR]

Purpose: In yttrium-90 (90Y) microsphere brachytherapy (radioembolization) of unresectable liver cancer, posttherapy 90Y bremsstrahlung single photon emission computed tomography (SPECT) has been used to document the distribution of microspheres in the patient and to help predict potential side effects. The energy window used during projection acquisition can have a significant effect on image quality. Thus, using an optimal energy window is desirable. However, there has been great variability in the choice of energy window due to the continuous and broad energy distribution of 90Y bremsstrahlung photons. The area under the receiver operating characteristic curve (AUC) for the ideal observer (IO) is a widely used figure of merit (FOM) for optimizing the imaging system for detection tasks. The IO implicitly assumes a perfect model of the image formation process. However, for 90Y bremsstrahlung SPECT there can be substantial model-mismatch (i.e., difference between the actual image formation process and the model of it assumed in reconstruction), and the amount of the model-mismatch depends on the energy window. It is thus important to account for the degradation of the observer performance due to model-mismatch in the optimization of the energy window. The purpose of this paper is to optimize the energy window for 90Y bremsstrahlung SPECT for a detection task while taking into account the effects of the model-mismatch. Methods: An observer, termed the ideal observer with model-mismatch (IO-MM), has been proposed previously to account for the effects of the model-mismatch on IO performance. In this work, the AUC for the IO-MM was used as the FOM for the optimization. To provide a clinically realistic object model and imaging simulation, the authors used a background-known-statistically and signal-known-statistically task. The background was modeled as multiple compartments in the liver with activity parameters independently following a Gaussian distribution; the signal was modeled as a tumor with a Gaussian-distributed activity parameter located randomly with equal probability at one of three positions. The IO test statistics (i.e., likelihood ratios) were estimated using Markov-chain Monte Carlo methods. The authors realistically modeled human anatomy using a digital phantom code, and realistically simulated 90Y bremsstrahlung SPECT imaging with a clinical SPECT system and typical imaging parameters using a previously validated Monte Carlo bremsstrahlung simulation method. Model-mismatch was included by modeling image formation process in the calculation of IO test statistics using an analytic modeling method previously developed for quantitative 90Y bremsstrahlung SPECT. To demonstrate the effects of the model-mismatch on the detection task, the authors optimized the energy window both with and without model-mismatch included in the IO. Results: For all the energy windows, the AUC values for the IO-MM were smaller than that for the IO. The optimal windows for the IO-MM and the IO were 80-180 and 60-400 keV, respectively. Conclusions: The authors have demonstrated the degradation of the ideal performance due to model-mismatch and optimized the energy window for 90Y bremsstrahlung SPECT for detection tasks by accounting for the effects of the model-mismatch. The obtained optimal window was much narrower when taking into account the model-mismatch and similar to that obtained previously for estimation tasks. [ABSTRACT FROM AUTHOR]

Purpose: To improve the accuracy and the robustness of the segmentation in living donor liver transplantation (LDLT) surgery planning system, the authors present a new segmentation framework that addresses challenges induced by the complex shape variations of patients' livers with cancer. It is designed to achieve the accurate and robust segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors in the LDLT surgery planning system. Methods: The segmentation framework proposed in this paper includes two important modules: (1) The robust shape prior modeling for liver, in which the sparse shape composition (SSC) model is employed to deal with the complex variations of liver shapes and obtain patient-specific liver shape priors. (2) The integration of the liver shape prior with a minimally supervised segmentation algorithm to achieve the accurate segmentation of hepatic parenchyma, portal veins, hepatic veins, and tumors simultaneously. The authors apply this segmentation framework to our previously developed LDLT surgery planning system to enhance its accuracy and robustness when dealing with complex cases of patients with liver cancer. Results: Compared with the principal component analysis, the SSC model shows a great advantage in handling the complex variations of liver shapes. It also effectively excludes gross errors and outliers that appear in the input shape and preserves local details for specific patients. The proposed segmentation framework was evaluated on the clinical image data of liver cancer patients, and the average symmetric surface distance for hepatic parenchyma, portal veins, hepatic veins, and tumors was 1.07 ± 0.76, 1.09 ± 0.28, 0.92 ± 0.35 and 1.13 ± 0.37 mm, respectively. The Hausdorff distance for these four tissues was 7.68, 4.67, 4.09, and 5.36 mm, respectively. Conclusions: The proposed segmentation framework improves the robustness of the LDLT surgery planning system remarkably when dealing with complex clinical liver shapes. The SSC model is able to handle non-Gaussian errors and preserve local detail information of the input liver shape. As a result, the proposed framework effectively addresses the problems caused by the complex shape variations of livers with cancer. Our framework not only obtains accurate segmentation results for healthy persons and common patients, but also shows high robustness when dealing with specific patients with large variations of liver shapes in complex clinical environments. [ABSTRACT FROM AUTHOR]

Background: Understanding the changes occurring in biological tissue during thermal ablation is at the heart of many current challenges in both therapy and medical imaging research. Purpose: The objective of this work is to quantitatively interpret the scattering response of human liver samples, before and after thermal ablation. We report acoustic measurements performed involving n = 21 human liver samples. Thermal ablation is achieved at temperatures between 45 and 80°C and quantification of the irreversible changes in acoustic attenuation and Backscattering Coefficient (BSC) is reported, with a particular attention to the latter. Methods: Both attenuation coefficient and BSCs were measured in the frequency range from 10 to 52 MHz. Scans were performed before heating and after cooling down. Attenuation coefficients were calculated using spectral difference method and BSC estimated using the reference phantom method. Results: Strong increases of attenuation coefficients and BSCs with heating temperature were observed. Quantitative ultrasonic parameters obtained with the polydisperse structure factor model (poly‐SFM)are compared to histological observations and seen to be close to hepatocyte mean diameter (HMD). Conclusions: The results presented in this study provide a description of the impact of thermal ablation in human liver tissue on acoustic attenuation and the BSC. For the first time, quantitative agreement between the Effective Scatterer Diameter (ESD) estimated from BSC and HMD was shown, highlighting the important role of cellular network in the scattering response of the medium. This core result is an important step toward the determination of the nature of scattering sources in biological tissues. [ABSTRACT FROM AUTHOR]

Purpose: We aimed to evaluate respiratory impacts on static and respiratory gated (RG) 99mTc‐MAA SPECT in terms of respiratory motion (RM) blur, attenuation correction (AC), and volume‐of‐interest (VOI) segmentation on lung shunt faction (LSF) and tumor‐to‐normal liver ratio (TNR) estimation for liver radioembolization therapy planning. Methods: The XCAT phantom was used to simulate a population of 300 phantoms, modeling various anatomical variations, tumor characteristics, RM amplitudes, LSFs, and TNRs. One hundred and twenty noisy projections of average activity maps near end‐expiration (End‐EX) and whole respiratory cycle were simulated analytically, modeling attenuation and geometric collimator‐detector‐response (GCDR). The OS‐EM algorithm was employed for reconstruction, modeling AC, and GCDR. RM effect was evaluated for static SPECT, while AC and VOI mismatch effects were investigated independently and together for static and RG SPECT utilizing one gate, that is, End‐EX. LSF and TNR errors were measured based on the ground truth. Lesions with different characteristics were also investigated for static and RG SPECT. Results: RM overestimates LSF and underestimates TNR. The VOI mismatch caused the largest errors in both RG and static SPECT for LSF and TNR estimation, reaching 160% and −52% correspondingly with extremely mismatched VOIs for RG SPECT, even larger than those for static SPECT. With matched AC and VOIs, RG SPECT has better performance than static SPECT. Larger TNR errors are associated with tumors of smaller sizes and higher TNR for static SPECT. Conclusions: The VOI segmentation mismatch has a stronger impact, followed by RM and AC in static 99mTc‐MAA SPECT/CT. This effect is more pronounced for RG SPECT. When VOI masks are derived from a matched CT, RG SPECT is generally superior to static SPECT for LSF and TNR estimation. The performance of RG SPECT could be worse than static SPECT when a mismatched CT is used for segmentation. [ABSTRACT FROM AUTHOR]

Purpose: Colorectal cancer is the third most common form of cancer in the United States, and up to 60% of these patients develop liver metastasis. While hepatic resection is the curative treatment of choice, only 20% of patients are candidates at the time of diagnosis. While percutaneous thermal ablation (PTA) has demonstrated 24%–51% overall 5‐year survival rates, assurance of sufficient ablation margin delivery (5 mm) can be challenging, with current methods of 2D distance measurement not ensuring 3D minimum margin. We hypothesized that biomechanical model‐based deformable image registration (DIR) can reduce spatial uncertainties and differentiate local tumor progression (LTP) patients from LTP‐free patients. Methods: We retrospectively acquired 30 patients (16 LTP and 14 LTP‐free) at our institution who had undergone PTA and had a contrast‐enhanced pre‐treatment and post‐ablation CT scan. Liver, disease, and ablation zone were manually segmented. Biomechanical model‐based DIR between the pre‐treatment and post‐ablation CT mapped the gross tumor volume onto the ablation zone and measured 3D minimum delivered margin (MDM). An in‐house cone‐tracing algorithm determined if progression qualitatively collocated with insufficient 5 mm margin achieved. Results: Mann–Whitney U test showed a significant difference (p < 0.01) in MDM from the LTP and LTP‐free groups. A total of 93% (13/14) of patients with LTP had a correlation between progression and missing 5 mm of margin volume. Conclusions: Biomechanical DIR is able to reduce spatial uncertainty and allow measurement of delivered 3D MDM. This minimum margin can help ensure sufficient ablation delivery, and our workflow can provide valuable information in a clinically useful timeframe. [ABSTRACT FROM AUTHOR]

Purpose: In vivo range and dose verification based on proton‐induced acoustics (protoacoustics) is potentially a useful tool for proton therapy. Built upon our previous study with two‐dimensional reconstruction, the time reversal (TR) method was extended to three‐dimensional (3D) and evaluated at two treatment sites (head and liver) through simulation, with the emphasis on a number of aspects such as increased spatial coverage, computational workload, and signal interference among slices. Methods: Two mono‐energetic pencil beams were modeled in each site. The k‐Wave toolbox was used to investigate the propagation and TR reconstruction of acoustic waves. The performance was quantitatively assessed based on mean square error (MSE) for dose verification and Bragg peak localization error (ΔBP) for range verification, with regard to five parameters: number of sensors, sampling duration, sampling timestep, spill time, and noise level. Results: The respective impacts of five parameters are examined. Under the optimum setting, the achievable ΔBP can be limited within 1 voxel (voxel size: 3 × 3 × 3 mm3) and the achievable MSE can be limited below 0.02, for the head case (56 sensors) and the liver case (204 sensors), respectively. Conclusions: The feasibility of range and dose verification utilizing the 3D TR method is demonstrated, as the very first step. In spite of several challenges unique to the 3D case (spatial coverage, computational workload, and signal interference among slices, etc.), promising performance is found and can be further improved through optimizing the deployment of sensors. The proposed approach may find potential use in several applications: beam diagnostics, in vivo dosimetry, and treatment monitoring. [ABSTRACT FROM AUTHOR]

Purpose: Current radiotherapy planning procedures are generally designed based on anatomical information only and use computed tomography (CT) images that do not incorporate organ‐functional information. In this study, we developed a method for estimating liver elasticity using the finite element method (FEM) and four‐dimensional CT (4DCT) images acquired during radiotherapy planning, and we subsequently evaluated its feasibility as a biomarker for liver fibrosis. Materials and methods: Twenty patients who underwent 4DCT and ultrasound‐based transient elastography (UTE) were enrolled. All patients had chronic liver disease or cirrhosis. Liver elasticity measurements of the UTE were performed on the right lobe of the patient's liver in 20 patients. The serum biomarkers of the aspartate aminotransferase (AST)‐to‐platelet ratio index (APRI) and fibrosis‐4 index (FIB‐4) were available in 18 of the 20 total patients, which were measured within 1 week after undergoing 4DCT. The displacement between the 4DCT images obtained at the endpoints of exhalation and inspiration was determined using the actual (via deformable image registration) and simulated (via FEM) respiration‐induced displacement. The elasticity of each element of the liver model was optimized by minimizing the error between the actual and simulated respiration‐induced displacement. Then, each patient's estimated liver elasticity was defined as the mean Young's modulus of the liver's right lobe and that of the whole liver using the estimated elasticity map. The estimated liver elasticity was evaluated for correlations with the elasticity obtained via UTE and with two serum biomarkers (APRI and FIB‐4). Results: The mean ± standard deviation (SD) of the errors between the actual and simulated respiration‐induced displacement in the liver model was 0.54 ± 0.33 mm. The estimated liver's right lobe elasticity was statistically significantly correlated with the UTE (r = 0.87, P < 0.001). Furthermore, the estimated whole liver elasticity was statistically significantly correlated with the UTE (r = 0.84, P < 0.001), APRI score (r = 0.62, P = 0.005), and FIB‐4 score (r = 0.54, P = 0.021). Conclusion: In this study, liver elasticity was estimated through FEM‐based simulation and actual respiratory‐induced liver displacement obtained from 4DCT images. Furthermore, we assessed that the estimated elasticity of the liver's right lobe was strongly correlated with the UTE. Therefore, the estimated elasticity has the potential to be a feasible imaging biomarker for assessing liver fibrosis using only 4DCT images without additional inspection or equipment costs. Because our results were derived from a limited sample of 20 patients, it is necessary to evaluate the accuracy of elasticity estimation for each liver segment on larger groups of biopsied patients to utilize liver elasticity information for radiotherapy planning. [ABSTRACT FROM AUTHOR]

Purpose: The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision‐making and treatment planning. The contours of liver and liver tumors are currently obtained by manual labeling, which is time‐consuming and subjective. Computer‐aided segmentation methods have been widely used in the segmentation of liver and liver tumors. However, due to the diversity of shape, volume, and image intensity, the segmentation is still a difficult task. In this study, we present a Spatial Feature Fusion Convolutional Network (SFF‐Net) to automatically segment liver and liver tumors from CT images. Methods: First, we extract side‐outputs at each convolutional block in SFF‐Net to make full use of multiscale features. Second, skip‐connections are added in the down‐sampling phase, therefore, the spatial information can be efficiently transferred to later layers. Third, we present feature fusion blocks (FFBs) to merge spatial features and high‐level semantic features from early layers and later layers, respectively. Finally, a fully connected 3D conditional random fields (CRFs) is applied to refine the liver and liver tumor segmentation results. Results: We test our method on the MICCAI 2017 Liver Tumor Segmentation (LiTS) challenge dataset. The Dice Global (DG) score, Dice per case (DC) score, Volume Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), and tumor precision score are calculated to evaluate the liver and liver tumor segmentation accuracies. For the liver segmentation, DG is 0.955; DC is 0.937; VOE is 0.106; and ASSD is 3.678. For the tumor segmentation, DG is 0.746; DC is 0.592; VOE is 0.416; ASSD is 1.585 and the tumor precision score is 0.369. Conclusions: The SFF‐Net learns more spatial information by adding skip‐connections and feature fusion blocks. The experiments validate that our method can accurately segment liver and liver tumors from CT images. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to design a fully automated framework to evaluate intrafraction motion using orthogonal x‐ray images from CyberKnife. Methods: The proposed framework includes three modules: (a) automated fiducial marker detection, (b) three‐dimensional (3D) position reconstruction, and (c) intrafraction motion evaluation. A total of 5927 images from real patients treated with CyberKnife fiducial tracking were collected. The ground truth was established by labeling coarse bounding boxes manually, and binary mask images were then obtained by applying a binary threshold and filter. These images and labels were used to train a detection model using a fully convolutional network (fCN). The output of the detection model can be used to reconstruct the 3D positions of the fiducial markers and then evaluate the intrafraction motion via a rigid transformation. For a patient test, the motion amplitudes, rotations, and fiducial cohort deformations were calculated used the developed framework for 13 patients with a total of 52 fractions. Results: The precision and recall of the fiducial marker detection model were 98.6% and 95.6%, respectively, showing high model performance. The mean (±SD) centroid error between the predicted fiducial markers and the ground truth was 0.25 ± 0.47 pixels on the test data. For intrafraction motion evaluation, the mean (±SD) translations in the superior–posterior (SI), left–right (LR), and anterior–posterior (AP) directions were 13.1 ± 2.2 mm, 2.0 ± 0.4 mm, and 5.2 ± 1.4 mm, respectively, and the mean (±SD) rotations in the roll, pitch and yaw directions were 2.9 ± 1.5°, 2.5 ± 1.5°, and 3.1 ± 2.2°. Seventy‐one percent of the fractions had rotations larger than the system limitations. With rotation correction during rigid registration, only 2 of the 52 fractions had residual errors larger than 2 mm in any direction, while without rotation correction, the probability of large residual errors increased to 46.2%. Conclusion: We developed a framework with high performance and accuracy for automatic fiducial marker detection, which can be used to evaluate intrafraction motion using orthogonal x‐ray images from CyberKnife. For liver patients, most fractions have fiducial cohort rotations larger than the system limitations; however, the fiducial cohort deformation is small, especially for the scenario with rotation correction. [ABSTRACT FROM AUTHOR]

Purpose: Real‐time tumor tracking through active correction by the multileaf collimator or treatment couch offers a promising strategy to mitigate delivery uncertainty due to intrafractional tumor motion. This study evaluated the performance of MLC and couch tracking using the prototype iTools Tracking system in TrueBeam Developer Mode and the application for abdominal cancer treatments. Methods: Experiments were carried out using a phantom with embedded Calypso transponders and a motion simulation platform. Geometric evaluations were performed using a circular conformal field with sinusoidal traces and pancreatic tumor motion traces. Geometric tracking accuracy was retrospectively calculated by comparing the compensational MLC or couch motion extracted from machine log files to the target motion reconstructed from real‐time MV and kV images. Dosimetric tracking accuracy was measured with radiochromic films using clinical abdominal VMAT plans and pancreatic tumor traces. Results: Geometrically, the root‐mean‐square errors for MLC tracking were 0.5 and 1.8 mm parallel and perpendicular to leaf travel direction, respectively. Couch tracking, in contrast, showed an average of 0.8 mm or less geometric error in all directions. Dosimetrically, both MLC and couch tracking reduced motion‐induced local dose errors compared to no tracking. Evaluated with five pancreatic tumor motion traces, the average 2%/2 mm global gamma pass rate of eight clinical abdominal VMAT plans was 67.4% (range: 26.4%–92.7%) without tracking, which was improved to 86.0% (range: 67.9%–95.6%) with MLC tracking, and 98.1% (range: 94.9%–100.0%) with couch tracking. In 16 out of 40 deliveries with different plans and motion traces, MLC tracking did not achieve clinically acceptable dosimetric accuracy with 3%/3mm gamma pass rate below 95%. Conclusions: This study demonstrated the capability of MLC and couch tracking to reduce motion‐induced dose errors in abdominal cases using a prototype tracking system. Clinically significant dose errors were observed with MLC tracking for certain plans which could be attributed to the inferior MLC tracking accuracy in the direction perpendicular to leaf travel, as well as the interplay between motion tracking and plan delivery for highly modulated plans. Couch tracking outperformed MLC tracking with consistently high dosimetric accuracy in all plans evaluated, indicating its clinical potential in the treatment of abdominal cancers. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate computed tomography (CT)‐based thermometry in cryoablation, the thermal sensitivity of an ex‐vivo porcine liver was determined in an initial study design. Methods: The CT‐guided cryoablation was performed in three porcine liver samples over a period of 10 min. Fiber optic temperature probes were positioned parallel to the shaft of the cryoprobe in an axial slice orientation. During ablation, temperature measurements were performed simultaneously with CT imaging at 5 s intervals. On the CT images, the average CT number was calculated for a region of interest of 3 × 3 pixels just below the tip of each temperature probe. A linear regression analysis was performed using eleven data sets to determine the dependence of the CT number on the temperature. Results: With decreasing temperature, an increasing hypodense area around the tip of the cryoprobe was observed on the CT images and decreasing values of the CT number were determined. Starting at a temperature of − 40°C a linear relation between the CT number and the temperature was determined and a thermal sensitivity of 0.95 HU/°C (R2 = 0.73) was obtained. The thermal sensitivity was used to calculate color‐coded temperature maps. The calculated temperature distribution corresponds quantitatively to the increasing hypodense area. Conclusions: A noninvasive CT‐based temperature determination during cryoablation in a normal ex vivo porcine liver is feasible. A thermal sensitivity of 0.95 HU/°C was determined by linear regression analysis. A color‐coded map of the temperature distribution was presented. [ABSTRACT FROM AUTHOR]

Purpose: Although stereotactic body radiation therapy (SBRT) is an attractive noninvasive approach for liver irradiation, it presents specific challenges associated with respiration‐induced liver motion, daily tumor localization due to liver deformation, and poor visualization of target with respect to adjacent normal liver in computed tomography (CT). We aim to identify potential hazards and develop a set of mitigation strategies to improve the safety of our liver SBRT program, using failure mode and effect analysis (FMEA). Materials and methods: A multidisciplinary group consisting of two physicians, three physicists, two dosimetrists, and two therapists was formed. A process map covering ten major stages of the liver SBRT program from the initial diagnosis to posttreatment follow‐up was generated. A total of 102 failure modes (FM), together with their causes and effects, were identified. The occurrence (O), severity (S), and lack of detectability (D) were independently scored using a scale from 1 (lowest risk) to 10 (largest risk). The ranking was done using the risk probability number (RPN) defined as the product of average O, S, and D numbers for each mode. Two fault tree analyses were performed. The failure modes with the highest RPN values as well as highest severity score were considered for investigation and a set of mitigation strategies was developed to address these. Results: The median RPN (RPNmed) values for all modes ranged from of 9 to 105 and the highest median S score (Smed) was 8. Fourteen FMs were identified to be significant by both RPNmed and Smed (top ten RPNmed ranked and highest Smed FMs) and 12 of them were considered for risk mitigation efforts. The remaining two were omitted due to either sufficient checks already in place, or lack of practical mitigation strategies. Implemented measures consisted of five physics tasks, two physician tasks, and three workflow changes. Conclusions: The application of FMEA to our liver SBRT program led to the identification of potential FMs and allowed improvement measures to enhance the safety of our clinical practice. [ABSTRACT FROM AUTHOR]

Purpose: Currently, radiologists use tumor‐to‐normal tissue contrast across multiphase computed tomography (MPCT) for lesion detection. Here, we developed a novel voxel‐based enhancement pattern mapping (EPM) technique and investigated its ability to improve contrast‐to‐noise ratios (CNRs) in a phantom study and in patients with hepatobiliary cancers. Methods: The EPM algorithm is based on the root mean square deviation between each voxel and a normal liver enhancement model using patient‐specific (EPM‐PA) or population data (EPM‐PO). We created a phantom consisting of liver tissue and tumors with distinct enhancement signals under varying tumor sizes, motion, and noise. We also retrospectively evaluated 89 patients with hepatobiliary cancers who underwent active breath‐hold MPCT between 2016 and 2017. MPCT phases were registered using a three‐dimensional deformable image registration algorithm. For the patient study, CNRs of tumor to adjacent tissue across MPCT phases, EPM‐PA and EPM‐PO were measured and compared. Results: EPM resulted in statistically significant CNR improvement (P < 0.05) for tumor sizes down to 3 mm, but the CNR improvement was significantly affected by tumor motion and image noise. Eighty‐two of 89 hepatobiliary cases showed CNR improvement with EPM (PA or PO) over grayscale MPCT, by an average factor of 1.4, 1.6, and 1.5 for cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis, respectively (P < 0.05 for all). Conclusions: EPM increases CNR compared with grayscale MPCT for primary and secondary hepatobiliary cancers. This new visualization method derived from MPCT datasets may have applications for early cancer detection, radiomic characterization, tumor treatment response, and segmentation. Implications for patient care: We developed a voxel‐wise enhancement pattern mapping (EPM) technique to improve the contrast‐to‐noise ratio (CNR) of multiphase CT. The improvement in CNR was observed in datasets of patients with cholangiocarcinoma, hepatocellular carcinoma, and colorectal liver metastasis. EPM has the potential to be clinically useful for cancers with regard to early detection, radiomic characterization, response, and segmentation. [ABSTRACT FROM AUTHOR]

Purpose: Automatic and accurate three‐dimensional (3D) segmentation of liver with severe diseases from computed tomography (CT) images is a challenging task. Fully convolutional networks (FCNs) have emerged as powerful tools for automatic semantic segmentation, with multiple potential applications in medical imaging. However, the use of a large receptive field and multiple pooling layers in the network leads to poor localization around object boundaries. The network usually makes pixel‐wise prediction independently, making it difficult to respect local label consistency and enforce the smoothness of the object boundary. Methods: We have developed an automatic liver segmentation method based on a novel framework that integrates fully convolutional network predictions into active contour models (ACM). We use only a single network architecture to generate a pixel label map containing spatial regional information (foreground and background) as well as layered boundary information. We exploit the structured network outcome to define an external constraint force of active contour models. A unique property of the designed force is that both its strength and direction are adaptive to its position and relative distance to the object boundary. The resulting integrated active contour models have the advantages of incorporating both high‐level and low‐level image information simultaneously, while enforcing the smoothness of the contour. Because the external constraint force can push the evolving contour to the liver boundary and exists everywhere in the image domain, it allows us to place the initial contour far away from the liver boundary. It potentially allows us to control the evolution of the contour in order to preserve the topology of the liver. Results: We have trained and evaluated our model on 73 liver CT scans from a clinic study. The integrated ACM model yields mean dice coefficients (DICE) 95.8 ± 1.4 (%). Without further fine‐tuning the network weights for two independent datasets, it yields mean DICE 96.2 ± 0.9 (%) for the SLIVER07 training dataset, and mean DICE 94.3 ± 2.7 (%) for the LiTS training dataset. In comparison with FCN alone model, the integrated ACM model yields improvements in terms of surface distance and DICE values for almost all the cases. Furthermore, the initialization of the active contour can be very far away from the liver boundary. Conclusions: Experimental results for segmenting livers (with severe diseases on CT images resulting in shape and density abnormalities) have revealed that our proposed model improves segmentation results in comparison with FCN alone. Without further fine‐tuning the network weights for two independent datasets, the model is capable of handling image variations from different datasets due to its inherent deformable nature. It is relatively easy to integrate more advanced (either existing or future) FCN architecture into our framework to further improve the segmentation performance. [ABSTRACT FROM AUTHOR]

Purpose : Abdominal dynamic contrast‐enhanced (DCE) MRI suffers from motion‐induced artifacts that can blur images and distort contrast‐agent uptake curves. For liver perfusion analysis, image reconstruction with rigid‐body motion correction (RMC) can restore distorted portal‐venous input functions (PVIF) to higher peak amplitudes. However, RMC cannot correct for liver deformation during breathing. We present a reconstruction algorithm with deformable motion correction (DMC) that enables correction of breathing‐induced deformation in the whole abdomen. Methods: Raw data from a golden‐angle stack‐of‐stars gradient‐echo sequence were collected for 54 DCE‐MRI examinations of 31 patients. For each examination, a respiratory motion signal was extracted from the data and used to reconstruct 21 breathing states from inhale to exhale. The states were aligned with deformable image registration to the end‐exhale state. Resulting deformation fields were used to correct back‐projection images before reconstruction with view sharing. Images with DMC were compared to uncorrected images and images with RMC. Results: DMC significantly increased the PVIF peak amplitude compared to uncorrected images (P << 0.01, mean increase: 8%) but not compared to RMC. The increased PVIF peak amplitude significantly decreased estimated portal‐venous perfusion in the liver (P << 0.01, mean decrease: 8 ml/(100 ml·min)). DMC also removed artifacts in perfusion maps at the liver edge and reduced blurring of liver tumors for some patients. Conclusions: DCE‐MRI reconstruction with DMC can restore motion‐distorted uptake curves in the abdomen and remove motion artifacts from reconstructed images and parameter maps but does not significantly improve perfusion quantification in the liver compared to RMC. [ABSTRACT FROM AUTHOR]

Purpose Photodynamic therapy ( PDT) is used after surgical resection to treat the microscopic disease for malignant pleural mesothelioma and to increase survival rates. As accurate light delivery is imperative to PDT efficacy, the deformation of the pleural volume during the surgery is studied on its impact on the delivered light fluence. In this study, a three-dimensional finite element-based (3D FEM) deformable image registration is proposed to directly match the volume of lung to the volume of pleural cavity obtained during PDT to have accurate representation of the light fluence accumulated in the lung, heart and liver (organs-at-risk) during treatment. Methods A wand, comprised of a modified endotrachial tube filled with Intralipid and an optical fiber inside the tube, is used to deliver the treatment light. The position of the treatment is tracked using an optical tracking system with an attachment comprised of nine reflective passive markers that are seen by an infrared camera-based navigation system. This information is used to obtain the surface contours of the plural cavity and the cumulative light fluence on every point of the cavity surface that is being treated. The lung, heart, and liver geometry are also reconstructed from a series of computed tomography ( CT) scans of the organs acquired in the same patient before and after the surgery. The contours obtained with the optical tracking system and CTs are imported into COMSOL Multiphysics, where the 3D FEM-based deformable image registration is obtained. The delivered fluence values are assigned to the respective positions (x, y, and z) on the optical tracking contour. The optical tracking contour is considered as the reference, and the CT contours are used as the target, which will be deformed. The data from three patients formed the basis for this study. Results The physical correspondence between the CT and optical tracking geometries, taken at different times, from different imaging devices was established using the 3D FEM-based image deformable registration. The volume of lung was matched to the volume of pleural cavity and the distribution of light fluence on the surface of the heart, liver and deformed lung volumes was obtained. Conclusion The method used is appropriate for analyzing problems over complicated domains, such as when the domain changes (as in a solid-state reaction with a moving boundary), when the desired precision varies over the entire domain, or when the solution lacks smoothness. Implementing this method in real-time for clinical applications and in situ monitoring of the under- or over- exposed regions to light during PDT can significantly improve the treatment for mesothelioma. [ABSTRACT FROM AUTHOR]

Purpose: To use novel voxel-based 3D printed textured phantoms in order to compare low-contrast detectability between two reconstruction algorithms, FBP (filtered-backprojection) and SAFIRE (sinogram affirmed iterative reconstruction) and determine what impact background texture (i.e., anatomical noise) has on estimating the dose reduction potential of SAFIRE. Methods: Liver volumes were segmented from 23 abdominal CT cases. The volumes were characterized in terms of texture features from gray-level co-occurrence and run-length matrices. Using a 3D clustered lumpy background (CLB) model, a fitting technique based on a genetic optimization algorithm was used to find CLB textures that were reflective of the liver textures, accounting for CT system factors of spatial blurring and noise. With the modeled background texture as a guide, four cylindrical phantoms (Textures A-C and uniform, 165 mm in diameter, and 30 mm height) were designed, each containing 20 low-contrast spherical signals (6 mm diameter at nominal contrast levels of ~3.2, 5.2, 7.2, 10, and 14 HU with four repeats per signal). The phantoms were voxelized and input into a commercial multimaterial 3D printer (Object Connex 350), with custom software for voxel-based printing (using principles of digital dithering). Images of the textured phantoms and a corresponding uniform phantom were acquired at six radiation dose levels (SOMATOM Flash, Siemens Healthcare) and observer model detection performance (detectability index of a multislice channelized Hotelling observer) was estimated for each condition (5 contrasts × 6 doses × 2 reconstructions × 4 backgrounds = 240 total conditions). A multivariate generalized regression analysis was performed (linear terms, no interactions, random error term, log link function) to assess whether dose, reconstruction algorithm, signal contrast, and background type have statistically significant effects on detectability. Also, fitted curves of detectability (averaged across contrast levels) as a function of dose were constructed for each reconstruction algorithm and background texture. FBP and SAFIRE were compared for each background type to determine the improvement in detectability at a given dose, and the reduced dose at which SAFIRE had equivalent performance compared to FBP at 100% dose. Results: Detectability increased with increasing radiation dose (P = 2.7×10-59) and contrast level (P = 2.2×10-86) and was higher in the uniform phantom compared to the textured phantoms (P = 6.9×10-51). Overall, SAFIRE had higher d' compared to FBP (P = 0.02). The estimated dose reduction potential of SAFIRE was found to be 8%, 10%, 27%, and 8% for Texture-A, Texture-B, Texture-C and uniform phantoms. Conclusions: In all background types, detectability was higher with SAFIRE compared to FBP. However, the relative improvement observed from SAFIRE was highly dependent on the complexity of the background texture. Iterative algorithms such as SAFIRE should be assessed in the most realistic context possible. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to develop a Monte Carlo simulation model for in vivo associated particle neutron elemental imaging (APNEI) and to study the feasibility of using APNEI to determine the iron distribution in a human liver with the defined model. Methods: The model presented in this study was defined in ???? by the basic geometry of the human body, the use of D+D source neutrons, iron as the element of interest, an iron-containing voxel in the liver as the target region, and 2 large germanium detectors anterior and posterior to the trunk of the body. The f 8 pulse height tally was employed in ???? to determine the signal acquired from iron inelastic scatter gamma rays at various iron concentrations in the target liver voxel. Correspondingly, the f 4 average flux tally in ???? was modified by a dose function such that the equivalent dose to the whole liver and the effective dose to the whole body could be estimated and used as the basis for a limiting number of neutron histories which could feasibly allow for the collection of a sufficient volume of data to construct a 2D image of iron distribution in the liver voxel. Results: Assuming an allowable equivalent dose to the liver of 5 mSv, 143 inelastic scatter iron gamma ray counts (at ~847 keV) would ideally be registered at the germanium detectors for a 1 cm³ cube-shaped liver voxel with an iron concentration of 1000 ppm. According to the simulation model, an image of iron distribution in the liver can be constructed with a 1 cm resolution at the level of 1000 ppm iron. Collecting such an image would yield an estimated whole body dose of 0.82 mSv. The mathematical introduction of image uncertainty resulting from source spot diameter and detector timing resolution more closely approximates the result of real world application. Conclusions: APNEI of certain elements in vivo appears feasible given several timing, sensitivity, and resolution caveats. However, further study is required to determine what the detection limit of iron would be and what image resolution would be in an experimental setup as the present model contains idealized assumptions which overestimate the signal attributable to iron inelastic scatter gamma rays. [ABSTRACT FROM AUTHOR]

Purpose: To present a system for robotic 4D ultrasound (US) imaging concurrent with radiotherapy beam delivery and estimate the proportion of liver stereotactic ablative body radiotherapy (SABR) cases in which robotic US image guidance can be deployed without interfering with clinically used VMAT beam configurations. Methods: The image guidance hardware comprises a 4D US machine, an optical tracking system for measuring US probe pose, and a custom-designed robot for acquiring hands-free US volumes. In software, a simulation environment incorporating the LINAC, couch, planning CT, and robotic US guidance hardware was developed. Placement of the robotic US hardware was guided by a target visibility map rendered on the CT surface by using the planning CT to simulate US propagation. The visibility map was validated in a prostate phantom and evaluated in patients by capturing live US from imaging positions suggested by the visibility map. In 20 liver SABR patients treated with VMAT, the simulation environment was used to virtually place the robotic hardware and US probe. Imaging targets were either planning target volumes (PTVs, range 5.9-679.5 ml) or gross tumor volumes (GTVs, range 0.9-343.4 ml). Presence or absence of mechanical interference with LINAC, couch, and patient body as well as interferences with treated beams was recorded. Results: For PTV targets, robotic US guidance without mechanical interference was possible in 80% of the cases and guidance without beam interference was possible in 60% of the cases. For the smaller GTV targets, these proportions were 95% and 85%, respectively. GTV size (1/20), elongated shape (1/20), and depth (1/20) were the main factors limiting the availability of noninterfering imaging positions. The robotic US imaging system was deployed in two liver SABR patients during CT simulation with successful acquisition of 4D US sequences in different imaging positions. Conclusions: This study indicates that for VMAT liver SABR, robotic US imaging of a relevant internal target may be possible in 85% of the cases while using treatment plans currently deployed in the clinic. With beam replanning to account for the presence of robotic US guidance, intrafractional US may be an option for 95% of the liver SABR cases. [ABSTRACT FROM AUTHOR]

Purpose: Radioembolization with yttrium-90 microspheres may be optimized with patient-specific pretherapy treatment planning. Dose verification and validation of treatment planning methods require quantitative imaging of the post-therapy distribution of yttrium-90 (Y-90). Methods for quantitative imaging of Y-90 using both bremsstrahlung SPECT and PET have previously been described. The purpose of this study was to compare the two modalities quantitatively in humans. Methods: Calibration correction factors for both quantitative Y-90 bremsstrahlung SPECT and a non-time-of-flight PET system without compensation for prompt coincidences were developed by imaging three phantoms. The consistency of these calibration correction factors for the different phantoms was evaluated. Post-therapy images from both modalities were obtained from 15 patients with hepatocellular carcinoma who underwent hepatic radioembolization using Y-90 glass microspheres. Quantitative SPECT and PET images were rigidly registered and the total liver activities and activity distributions estimated for each modality were compared. The activity distributions were compared using profiles, voxel-by-voxel correlation and Bland-Altman analyses, and activity-volume histograms. Results: The mean ± standard deviation of difference in the total activity in the liver between the two modalities was 0% ± 9% (range -21%-18%). Voxel-by-voxel comparisons showed a good agreement in regions corresponding roughly to treated tumor and treated normal liver; the agreement was poorer in regions with low or no expected activity, where PET appeared to overestimate the activity. The correlation coefficients between intrahepatic voxel pairs for the two modalities ranged from 0.86 to 0.94. Cumulative activity volume histograms were in good agreement. Conclusions: These data indicate that, with appropriate reconstruction methods and measured calibration correction factors, either Y-90 SPECT/CT or Y-90 PET/CT can be used for quantitative post-therapy monitoring of Y-90 activity distribution following hepatic radioembolization. [ABSTRACT FROM AUTHOR]

Purpose: The authors are currently developing a dual-resolution multiple-pinhole microSPECT imaging system based on three large NaI(Tl) gamma cameras. Two multiple-pinhole tungsten collimator tubes will be used sequentially for whole-body "scout" imaging of a mouse, followed by high-resolution (hi-res) imaging of an organ of interest, such as the heart or brain. Ideally, the whole-body image will be reconstructed in real time such that data need only be acquired until the area of interest can be visualized well-enough to determine positioning for the hi-res scan. The authors investigated the utility of the origin ensemble (OE) algorithm for online and offline reconstructions of the scout data. This algorithm operates directly in image space, and can provide estimates of image uncertainty, along with reconstructed images. Techniques for accelerating the OE reconstruction were also introduced and evaluated. Methods: System matrices were calculated for our 39-pinhole scout collimator design. SPECT projections were simulated for a range of count levels using the MOBY digital mouse phantom. Simulated data were used for a comparison of OE and maximum-likelihood expectation maximization (MLEM) reconstructions. The OE algorithm convergence was evaluated by calculating the total-image entropy and by measuring the counts in a volume-of-interest (VOI) containing the heart. Total-image entropy was also calculated for simulated MOBY data reconstructed using OE with various levels of parallelization. Results: For VOI measurements in the heart, liver, bladder, and soft-tissue, MLEM and OE reconstructed images agreed within 6%. Image entropy converged after ~2000 iterations of OE, while the counts in the heart converged earlier at ~200 iterations of OE. An accelerated version of OE completed 1000 iterations in <9 min for a 6.8M count data set, with some loss of image entropy performance, whereas the same dataset required ~79 min to complete 1000 iterations of conventional OE. A combination of the two methods showed decreased reconstruction time and no loss of performance when compared to conventional OE alone. Conclusions: OE-reconstructed images were found to be quantitatively and qualitatively similar to MLEM, yet OE also provided estimates of image uncertainty. Some acceleration of the reconstruction can be gained through the use of parallel computing. The OE algorithm is useful for reconstructing multiple-pinhole SPECT data and can be easily modified for real-time reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the geometric and dosimetric accuracies of the CyberKnife Synchrony respiratory tracking system (RTS) and to validate a method for pretreatment patient-specific delivery quality assurance (DQA). Methods: An EasyCube phantom was mounted on the ExacTrac gating phantom, which can move along the superior-inferior (SI) axis of a patient to simulate a moving target. The authors compared dynamic and static measurements. For each case, a Gafchromic EBT3 film was positioned between two slabs of the EasyCube, while a PinPoint ionization chamber was placed in the appropriate space. There were three steps to their evaluation: (1) the field size, the penumbra, and the symmetry of six secondary collimators were measured along the two main orthogonal axes. Dynamic measurements with deliberately simulated errors were also taken. (2) The delivered dose distributions (from step 1) were compared with the planned ones, using the gamma analysis method. The local gamma passing rates were evaluated using three acceptance criteria: 3% local dose difference (LDD)/3 mm, 2%LDD/2 mm, and 3%LDD/1 mm. (3) The DQA plans for six clinical patients were irradiated in different dynamic conditions, to give a total of 19 cases. The measured and planned dose distributions were evaluated with the same gamma-index criteria used in step 2 and the measured chamber doses were compared with the planned mean doses in the sensitive volume of the chamber. Results: (1) A very slight enlargement of the field size and of the penumbra was observed in the SI direction (on average <1 mm), in line with the overall average CyberKnife system error for tracking treatments. (2) Comparison between the planned and the correctly delivered dose distributions confirmed the dosimetric accuracy of the RTS for simple plans. The multicriteria gamma analysis was able to detect the simulated errors, proving the robustness of their method of analysis. (3) All of the DQA clinical plans passed the tests, both in static and dynamic conditions. No statistically significant differences were found between static and dynamic cases, confirming the high degree of accuracy of the Synchrony RTS. Conclusions: The presented methods and measurements verified the mechanical and dosimetric accuracy of the Synchrony RTS. Their method confirms the fact that the RTS, if used properly, is able to treat a moving target with great precision. By combining PinPoint ion chamber, EBT3 films, and gamma evaluation of dose distributions, their DQA method robustly validated the effectiveness of CyberKnife and Synchrony system. [ABSTRACT FROM AUTHOR]

Purpose: Many centers aim to plan liver transarterial radioembolization (TARE) with dosimetry, even without CT-based attenuation correction (AC), or with unoptimized scatter correction (SC) methods. This work investigates the impact of presence vs absence of such corrections, and limited spatial resolution, on 3D dosimetry for TARE. Methods: Three voxelized phantoms were derived from CT images of real patients with different body sizes. Simulations of 99mTc-SPECT projections were performed with the SIMIND code, assuming three activity distributions in the liver: uniform, inside a "liver's segment," or distributing multiple uptaking nodules ("nonuniform liver"), with a tumoral liver/healthy parenchyma ratio of 5:1. Projection data were reconstructed by a commercial workstation, with OSEM protocol not specifically optimized for dosimetry (spatial resolution of 12.6 mm), with/without SC (optimized, or with parameters predefined by the manufacturer; dual energy window), and with/without AC. Activity in voxels was calculated by a relative calibration, assuming identical microspheres and 99mTc-SPECT counts spatial distribution. 3D dose distributions were calculated by convolution with 90Y voxel S-values, assuming permanent trapping of microspheres. Cumulative dose-volume histograms in lesions and healthy parenchyma from different reconstructions were compared with those obtained from the reference biodistribution (the "gold standard," GS), assessing differences for D95%, D70%, and D50% (i.e., minimum value of the absorbed dose to a percentage of the irradiated volume). tool analysis with tolerance of 3%/13 mm was used to evaluate the agreement between GS and simulated cases. The influence of deep-breathing was studied, blurring the reference biodistributions with a 3D anisotropic gaussian kernel, and performing the simulations once again. Results: Differences of the dosimetric indicators were noticeable in some cases, always negative for lesions and distributed around zero for parenchyma. Application of AC and SC reduced systematically the differences for lesions by 5%-14% for a liver segment, and by 7%-12% for a nonuniform liver. For parenchyma, the data trend was less clear, but the overall range of variability passed from -10%/40% for a liver segment, and -10%/20% for a nonuniform liver, to -13%/6% in both cases. Applying AC, SC with preset parameters gave similar results to optimized SC, as confirmed by tool analysis. Moreover, analysis confirmed that solely AC and SC are not sufficient to obtain accurate 3D dose distribution. With breathing, the accuracy worsened severely for all dosimetric indicators, above all for lesions: with AC and optimized SC, -38%/-13% in liver's segment, -61%/-40% in the nonuniform liver. For parenchyma, D50% resulted always less sensitive to breathing and sub-optimal correction methods (difference overall range: -7%/13%). Conclusions: Reconstruction protocol optimization, AC, SC, PVE and respiratory motion corrections should be implemented to obtain the best possible dosimetric accuracy. On the other side, thanks to the relative calibration, D50% inaccuracy for the healthy parenchyma from absence of AC was less than expected, while the optimization of SC was scarcely influent. The relative calibration therefore allows to perform TARE planning, basing on D50% for the healthy parenchyma, even without AC or with suboptimal corrections, rather than rely on nondosimetric methods. [ABSTRACT FROM AUTHOR]

Purpose: Tracking soft-tissue targets has recently been cleared as a new application of Calypso, an electromagnetic wireless transponder tracking system, allowing for gated treatment of the liver based on the motion of the target volume itself. The purpose of this study is to describe the details of validating the Calypso system for wireless transponder tracking of the liver and to present the clinical workflow for using it to deliver gated stereotactic ablative radiotherapy (SABR). Methods: A commercial 3D diode array motion system was used to evaluate the dynamic tracking accuracy of Calypso when tracking continuous large amplitude motion. It was then used to perform end-to-end tests to evaluate the dosimetric accuracy of gated beam delivery for liver SABR. In addition, gating limits were investigated to determine how large the gating window can be while still maintaining dosimetric accuracy. The gating latency of the Calypso system was also measured using a customized motion phantom. Results: The average absolute difference between the measured and expected positional offset was 0.3 mm. The 2%/2 mm gamma pass rates for the gated treatment delivery were greater than 97%. When increasing the gating limits beyond the known extent of planned motion, the gamma pass rates decreased as expected. The 2%/2 mm gamma pass rate for a 1, 2, and 3 mm increase in gating limits was measured to be 97.8%, 82.9%, and 61.4%, respectively. The average gating latency was measured to be 63.8 ms for beam-hold and 195.8 ms for beam-on. Four liver patients with 17 total fractions have been successfully treated at our institution. Conclusions: Wireless transponder tracking was validated as a dosimetrically accurate way to provide gated SABR of the liver. The dynamic tracking accuracy of the Calypso system met manufacturer's specification, even for continuous large amplitude motion that can be encountered when tracking liver tumors close to the diaphragm. The measured beam-hold gating latency was appropriate for targets that will traverse the gating limit each respiratory cycle causing the beam to be interrupted constantly throughout treatment delivery. [ABSTRACT FROM AUTHOR]

Purpose: A robust, automatic, and rapid method for liver delineation is urgently needed for the diagnosis and treatment of liver disorders. Until now, the high variability in liver shape, local image artifacts, and the presence of tumors have complicated the development of automatic 3D liver segmentation. In this study, an automatic three-level AdaBoost-guided active shape model (ASM) is proposed for the segmentation of the liver based on enhanced computed tomography images in a robust and fast manner, with an emphasis on the detection of tumors. Methods: The AdaBoost voxel classifier and AdaBoost profile classifier were used to automatically guide three-level active shape modeling. In the first level of model initialization, fast automatic liver segmentation by an AdaBoost voxel classifier method is proposed. A shape model is then initialized by registration with the resulting rough segmentation. In the second level of active shape model fitting, a prior model based on the two-class AdaBoost profile classifier is proposed to identify the optimal surface. In the third level, a deformable simplex mesh with profile probability and curvature constraint as the external force is used to refine the shape fitting result. In total, three registration methods--3D similarity registration, probability atlas B-spline, and their proposed deformable closest point registration--are used to establish shape correspondence. Results: The proposed method was evaluated using three public challenge datasets: 3Dircadb1, SLIVER07, and Visceral Anatomy3. The results showed that our approach performs with promising efficiency, with an average of 35 s, and accuracy, with an average Dice similarity coefficient (DSC) of 0.94 ± 0.02, 0.96 ± 0.01, and 0.94 ± 0.02 for the 3Dircadb1, SLIVER07, and Anatomy3 training datasets, respectively. The DSC of the SLIVER07 testing and Anatomy3 unseen testing datasets were 0.964 and 0.933, respectively. Conclusions: The proposed automatic approach achieves robust, accurate, and fast liver segmentation for 3D CTce datasets. The AdaBoost voxel classifier can detect liver area quickly without errors and provides sufficient liver shape information for model initialization. The AdaBoost profile classifier achieves sufficient accuracy and greatly decreases segmentation time. These results show that the proposed segmentation method achieves a level of accuracy comparable to that of state-of-theart automatic methods based on ASM. [ABSTRACT FROM AUTHOR]

Purpose: Computational models of microwave ablation (MWA) are widely used during the design optimization of novel devices and are under consideration for patient-specific treatment planning. The objective of this study was to assess the sensitivity of computational models of MWA to tissue biophysical properties. Methods: The Morris method was employed to assess the global sensitivity of the coupled electromagnetic-thermal model, which was implemented with the finite element method (FEM). The FEM model incorporated temperature dependencies of tissue physical properties. The variability of the model was studied using six different outputs to characterize the size and shape of the ablation zone, as well as impedance matching of the ablation antenna. Furthermore, the sensitivity results were statistically analyzed and absolute influence of each input parameter was quantified. A framework for systematically incorporating model uncertainties for treatment planning was suggested. Results: A total of 1221 simulations, incorporating 111 randomly sampled starting points, were performed. Tissue dielectric parameters, specifically relative permittivity, effective conductivity, and the threshold temperature at which they transitioned to lower values (i.e., signifying desiccation), were identified as the most influential parameters for the shape of the ablation zone and antenna impedance matching. Of the thermal parameters considered in this study, the nominal blood perfusion rate and the temperature interval across which the tissue changes phase were identified as the most influential. The latent heat of tissue water vaporization and the volumetric heat capacity of the vaporized tissue were recognized as the least influential parameters. Based on the evaluation of absolute changes, the most important parameter (perfusion) had approximately 40.23 times greater influence on ablation area than the least important parameter (volumetric heat capacity of vaporized tissue). Another significant input parameter (permittivity) had 22.26 times higher influence on the deviation of ablation edge shape from a sphere than one of the less important parameters (latent heat of liver tissue vaporization). Conclusions: Dielectric parameters, blood perfusion rate, and the temperature interval across which the tissue changes phase were found to have the most significant impact on MWA model outputs. The latent heat of tissue water vaporization and the volumetric heat capacity of the vaporized tissue were recognized as the least influential parameters. Uncertainties in model outputs identified in this study can be incorporated to provide probabilistic maps of expected ablation outcome for patient-specific treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study is to measure through simulation the impact of (1) heterogeneity of biophysical parameters in tumor vs healthy tissue, (2) applicator placement relative to the tumor, and (3) proximity to large blood vessels on microwave ablation (MWA) treatment effect area. This will help identify the biophysical properties that have the greatest impact on improving clinical modeling of MWA procedures. Methods: The authors' approach was to develop two-compartment models with variable tissue properties and simulate MWA procedures performed in liver with Perseon Medical's 915 MHz short-tip applicator. Input parameters for the dielectric and thermal properties considered in this study were based on measurements for healthy and malignant (primary or metastatic) liver? tissue previously reported in the literature. Compartment 1 (C1) represented normal, fatty, or cirrhotic liver, and compartment 2 (C2) represented a primary hepatocellular carcinomatumor sample embedded within C1. To evaluate the sensitivity to tissue parameters, a range of clinically relevant tissue properties were simulated. To evaluate the impact of MWA antenna position, the authors simulated various tumor perfusion models with the antenna shifted 5 mm anteriorly and posteriorly. To evaluate the effect of local vasculature, the authors simulated an additional heat sink of various diameters and distances from the tumor. Dice coefficient statistics were used to evaluate ablation zone effects from these local heat sinks. Results: Models showed less than 11% of volume variability (1 cm³ increase) in ablation treatment effect region when accounting for the difference in relative permittivity and electrical conductivity between malignant and healthy liver? tissue. There was a 27% increase in volume when simulating thermal conductivity of fatty liver disease versus the baseline simulation. The ablation zone volume increased more than 36% when simulating cirrhotic surrounding liver? tissue. Antenna placement relative to the tumor had minimal sensitivity to the absolute size of the treatment effect area, with less than 1.5 mm variation. However, when considering the overlap between the ablation zone and the ideal clinical margin when the antenna was displaced 5 mm anteriorly and posteriorly, there was approximately a 6 mm difference in the margins. Dice coefficient statistics showed as much as an 11% decrease in the ablation margin due to the presence of vessel heat sinks within the model. Conclusions: The results from simulating the variance in malignant tissue thermal and electrical properties will help guide better approximations for MWA treatments. The results suggest that assuming malignant and healthy liver tissues have similar dielectric properties is a reasonable first approximation. Antenna placement relative to the tumor has minimal impact on the absolute size of the ablation zone, yet it does cause relevant variation between desired treatment margin and ablation zone. Blood vessel cooling, especially hepatic vessels close to the region of interest, may be a significant factor to consider in treatment planning. Further data need to be collected for assessing treatment planning utility of modeling MWA in this context. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound strain imaging is a relatively low cost and portable modality for monitoring percutaneous thermal ablation of liver neoplasms. However, a 3D strain volume reconstruction from existing 2D strain images is necessary to fully delineate the thermal dose distribution. Tissue mimicking (TM) phantom experiments were performed to validate a novel volume reconstruction algorithm referred to as sheaf of ultrasound planes reconstruction (SOUPR), based on a series of 2D rotational imaging planes. Methods: Reconstruction using SOUPR was formulated as an optimization problem with constraints on data consistency with 2D strain images and data smoothness of the volume data. Reconstructed ablation inclusion dimensions, volume, and elastographic signal to noise ratio (SNRe) and contrast to noise ratio (CNRe) were compared with conventional 3D ultrasound strain imaging based on interpolating a series of quasiparallel 2D strain images with a wobbler transducer. Results: Volume estimates of the phantom inclusion were in a similar range for both acquisition approaches. SNRe and CNRe obtained with SOUPR were significantly higher on the order of 250% and 166%, respectively. The mean error of the inclusion dimension reconstructed with a wobbler transducer was on the order of 10.4%, 3.5%, and 19.0% along the X, Y, and Z axes, respectively, while the error with SOUPR was on the order of 2.6%, 2.8%, and 9.6%. A qualitative comparison of SOUPR and wobbler reconstruction was also performed using a thermally ablated region created in ex vivo bovine liver tissue. Conclusions: The authors have demonstrated using experimental evaluations with a TM phantom that the reconstruction results obtained with SOUPR were superior when compared with a conventional wobbler transducer in terms of inclusion shape preservation and detectability. [ABSTRACT FROM AUTHOR]

Purpose: Respiratory gating is a strategy for overcoming image degradation caused by patient motion in Positron Emission Tomography (PET) imaging. Traditional methods for sorting data, namely, phase-based gating or amplitude-based gating, come with an inherent trade-off between resolution improvements and added noise present in the subjugated data. If the goal of motion correction in PET is realigned from creating 4D images that attempt to mimic nongated images, towards ideal utilization of the information available, then new paths for data management emerge. In this work, the authors examine the application of a method in a new class of frequency based data subjugation algorithms, termed gating+. This strategy utilizes data driven information to locally adapt signal to its optimal segregation, thereby creating a new approach to 4D data utilization PET. Methods: 189 18F-fluorodeoxyglucose (FDG) PET scans were acquired at a single bed position centered on the thorax region. 4D gated image sets were reconstructed using data driven gating. The gating+ signal optimization algorithm, previously presented in small animal PET images and simulations, was used to segregate data in frequency space to generate optimized 4D images in the population-the first application and analysis of gating+ in human PET scans. The nongated, phase gated, and gating+ representations of the data were compared using FDG uptake analysis in the identified lesions and noise measurements from background regions. Results: Optimized processing required less than 1 min per scan on a standard PC (plus standard reconstruction time), and yielded entire 4D optimized volumes plus motion maps. Optimized scans had noise characteristics similar to nongated images, yet also contained much of the resolution and motion information found in the gated images. The average SUVmax increase in the lesion sample between gated/nongated and gating+/nongated (±}SD in population) was 35.8%±}34.6% and 28.6%±}27.9%, respectively. The average percent standard deviation (%SD ±} SD in population) in liver volumes of interest (VOIs) across the sample for the nongated, gated, and gating+ scans was 6.7%±}2.4%, 13.6%±}3.3%, and 7.1%±}2.5%, respectively. In all cases, the noise in the gating+ liver VOIs was closer to the nongated measurements than to the gated. Conclusions: The gating+ algorithm introduces the notion of conforming 4D data segregation to the local information and statistics that support it. By segregating data in frequency space, the authors are able to generate low noise motion information rich image sets, derived solely from selective use of raw data. Their work shows that the gating+ algorithm can be robustly applied in populations, and across varying qualities of motion and scans statistics, and be integrated as part of a fully automated motion correction workflow. Furthermore, the idea of smart signal utilization underpins a new concept of low risk or even risk-free motion correction application in PET. [ABSTRACT FROM AUTHOR]

Purpose: Intrafractional organ motion imposes considerable challenges to scanned ion beam therapy and demands for a thorough verification of the applied treatment. At the Heidelberg Ion-Beam Therapy Center (HIT), the scanned ion beam delivery is verified by means of postirradiation positronemission- tomography (PET) imaging. This work presents a first clinical evaluation of PET-based treatment monitoring in ion beam therapy under consideration of target motion. Methods: Three patients with mobile liver lesions underwent scanned carbon ion irradiation at HIT and postirradiation PET/CT (x-ray-computed-tomography) imaging with a commercial scanner. Respiratory motion was recorded during irradiation and subsequent image acquisition. This enabled a time-resolved (4D) calculation of the expected irradiation-induced activity pattern and, for one patient where an additional 4D CT was acquired at the PET/CT scanner after treatment, a motioncompensated PET image reconstruction. For the other patients, PET data were reconstructed statically. To verify the treatment, calculated prediction and reconstructed measurement were compared with a focus on the ion beam range. Results: Results in the current three patients suggest that for motion amplitudes in the order of 2mm there is no benefit from incorporating respiratory motion information into PET-based treatment monitoring. For a target motion in the order of 10 mm, motion-related effects become more severe and a time-resolved modeling of the expected activity distribution can lead to an improved data interpretation if a sufficient number of true coincidences is detected. Benefits from motion-compensated PET image reconstruction could not be shown conclusively at the current stage. Conclusions: The feasibility of clinical PET-based treatment verification under consideration of organ motion has been shown for the first time. Improvements in noise-robust 4D PET image reconstruction are deemed necessary to enhance the clinical potential. [ABSTRACT FROM AUTHOR]

Purpose: Efficient and accurate 3D liver segmentations from contrast-enhanced computed tomography (CT) images play an important role in therapeutic strategies for hepatic diseases. However, inhomogeneous appearances, ambiguous boundaries, and large variance in shape often make it a challenging task. The existence of liver abnormalities poses further difficulty. Despite the significant intensity difference, liver tumors should be segmented as part of the liver. This study aims to address these challenges, especially when the target livers contain subregions with distinct appearances. Methods: The authors propose a novel multiregion-appearance based approach with graph cuts to delineate the liver surface. For livers with multiple subregions, a geodesic distance based appearance selection scheme is introduced to utilize proper appearance constraint for each subregion. A special case of the proposed method, which uses only one appearance constraint to segment the liver, is also presented. The segmentation process is modeled with energy functions incorporating both boundary and region information. Rather than a simple fixed combination, an adaptive balancing weight is introduced and learned from training sets. The proposed method only calls initialization inside the liver surface. No additional constraints from user interaction are utilized. Results: The proposed method was validated on 50 3D CT images from three datasets, i.e., Medical Image Computing and Computer Assisted Intervention (MICCAI) training and testing set, and local dataset. On MICCAI testing set, the proposed method achieved a total score of 83.4±3.1, outperforming nonexpert manual segmentation (average score of 75.0). When applying their method to MICCAI training set and local dataset, it yielded a mean Dice similarity coefficient (DSC) of 97.7%±0.5% and 97.5%±0.4%, respectively. These results demonstrated the accuracy of the method when applied to different computed tomography (CT) datasets. In addition, user operator variability experiments showed its good reproducibility. Conclusions: A multiregion-appearance based method is proposed and evaluated to segment liver. This approach does not require prior model construction and so eliminates the burdens associated with model construction and matching. The proposed method provides comparable results with state-of-the-art methods. Validation results suggest that it may be suitable for the clinical use. [ABSTRACT FROM AUTHOR]