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Purpose: To present a novel methodical approach to compare visibility of percutaneous needles in ultrasound images., Methods: A motor-driven rotation platform was used to gradually change the needle angle while capturing image data. Data analysis was automated using block-matching-based registration, with a tracking and refinement step. Every 25 frames, a Hough transform was used to improve needle alignments after large rotations. The method was demonstrated by comparing three commercial needles (14G radiofrequency ablation, RFA; 18G Trocar; 22G Chiba) and six prototype needles with different sizes, materials, and surface conditions (polished, sand-blasted, and kerfed), within polyvinyl alcohol phantom tissue and ex vivo bovine liver models. For each needle and angle, a contrast-to-noise ratio (CNR) was determined to quantify visibility. CNR values are presented as a function of needle type and insertion angle. In addition, the normalized area under the (CNR-angle) curve was used as a summary metric to compare needles., Results: In phantom tissue, the first kerfed needle design had the largest normalized area of visibility and the polished 1 mm diameter stainless steel needle the smallest (0.704 ± 0.199 vs. 0.154 ± 0.027, p < 0.01). In the ex vivo model, the second kerfed needle design had the largest normalized area of visibility, and the sand-blasted stainless steel needle the smallest (0.470 ± 0.190 vs. 0.127 ± 0.047, p < 0.001). As expected, the analysis showed needle visibility peaks at orthogonal insertion angles. For acute or obtuse angles, needle visibility was similar or reduced. Overall, the variability in needle visibility was considerably higher in livers., Conclusion: The best overall visibility was found with kerfed needles and the commercial RFA needle. The presented methodical approach to quantify ultrasound visibility allows comparisons of (echogenic) needles, as well as other technological innovations aiming to improve ultrasound visibility of percutaneous needles, such as coatings, material treatments, and beam steering approaches., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: Efficient compression of images while preserving image quality has the potential to be a major enabler of effective remote clinical diagnosis and treatment, since poor Internet connection conditions are often the primary constraint in such services. This paper presents a framework for organ-specific image compression for teleinterventions based on a deep learning approach and anisotropic diffusion filter., Methods: The proposed method, deep learning and anisotropic diffusion (DLAD), uses a convolutional neural network architecture to extract a probability map for the organ of interest; this probability map guides an anisotropic diffusion filter that smooths the image except at the location of the organ of interest. Subsequently, a compression method, such as BZ2 and HEVC-visually lossless, is applied to compress the image. We demonstrate the proposed method on three-dimensional (3D) CT images acquired for radio frequency ablation (RFA) of liver lesions. We quantitatively evaluate the proposed method on 151 CT images using peak-signal-to-noise ratio ( PSNR ), structural similarity ( SSIM ), and compression ratio ( CR ) metrics. Finally, we compare the assessments of two radiologists on the liver lesion detection and the liver lesion center annotation using 33 sets of the original images and the compressed images., Results: The results show that the method can significantly improve CR of most well-known compression methods. DLAD combined with HEVC-visually lossless achieves the highest average CR of 6.45, which is 36% higher than that of the original HEVC and outperforms other state-of-the-art lossless medical image compression methods. The means of PSNR and SSIM are 70 dB and 0.95, respectively. In addition, the compression effects do not statistically significantly affect the assessments of the radiologists on the liver lesion detection and the lesion center annotation., Conclusions: We thus conclude that the method has a high potential to be applied in teleintervention applications., (© 2021 American Association of Physicists in Medicine.)

Purpose: Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle., Methods: We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins., Results: Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%., Conclusions: Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety., (© 2018 American Association of Physicists in Medicine.)

Purpose: In CT-guided liver tumor ablation interventions, registration of a preoperative contrast-enhanced CT image to the intraoperative CT image is hypothesized to improve guidance. This is a highly challenging registration task due to differences in patient poses and large deformations, and therefore high registration errors are expected. In this study, our objective is to develop a method that enables users to locally improve the registration where the registration fails, with minimal user interaction., Methods: The method is based on a conventional nonrigid intensity-based registration framework, extended with a novel point-to-surface penalty. The point-to-surface penalty serves to improve the alignment of the liver boundary, while requiring minimal user interaction during the intervention: annotating some points on the liver surface at those regions where the conventional registration seems inaccurate., Results: The method is evaluated on 18 clinical datasets. It improves registration accuracy compared with the conventional nonrigid registration in terms of average surface distance (from 2.75 to 2.05 mm) and target registration error (from 6.92 to 5.8 mm)., Conclusions: In this study, we introduce a semiautomated registration algorithm that improves the accuracy of image registration., (© 2017 American Association of Physicists in Medicine.)

Purpose: Myocardial blood flow (MBF) obtained by dynamic CT perfusion (CTP) has been recently introduced to assess hemodynamic significance of coronary stenosis in coronary artery disease. The diagnostic performance of dynamic CTP MBF is limited due to subjective interpretation of MBF maps and MBF variations caused by physiological, methodological, and technical issues. In this paper, we introduce a novel method to quantify the hypoperfused volume (HPV) in myocardial territories derived from CT angiography (CTA) to overcome the limitations of current dynamic CTP MBF analysis methods., Methods: The diagnostic performance of HPV in classifying significant stenoses was evaluated on 22 patients (57 vessels) that underwent CTA, CTP and invasive fractional flow reserve (FFR). FFR was used as the standard of reference to determine stenosis significance. The diagnostic performance was compared to that of the mean MBF computed in regions manually annotated by an expert (MA-MBF). HPV was derived by thresholding the MBF in myocardial territories constructed from CTA by locating the closest artery. Diagnostic performance was evaluated using leave-one-case out cross-validation. Inter-observer reproducibility was assessed by performing annotations of coronary seeds (HPV) and manual regions (MA-MBF) with two users. In addition, the influence of different parameter settings on the diagnostic performance of HPV was assessed., Results: Leave-one-case out cross-validation showed that HPV has an accuracy of 72% (58-83%) with sensitivity of 72% (47-90%) and specificity of 72% (58-83%). The accuracy of MA-MBF was 70% (57-82%) with a sensitivity of 50% (26-74%) and a specificity of 79% (64-91%). The Spearman correlation and the kappa statistic was (ρ = 0.94, κ = 0.86) for HPV and (ρ = 0.72, κ = 0.82) for MA-MBF. The influence of parameter settings on HPV based diagnostic performance was not significant., Conclusions: The proposed HPV accurately classifies hemodynamically significant stenoses with a level of accuracy comparable to the mean MBF in regions annotated by an expert. HPV improves inter-observer reproducibility as compared to MA-MBF by providing a more objective criterion to associate the stenotic coronary with the supplied myocardial territory., (© 2017 American Association of Physicists in Medicine.)

Purpose: The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework., Methods: Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient., Results: Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00., Conclusions: A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.

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.

Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer., Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T2-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas and intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework., Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved., Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.

Purpose: In current clinical practice, head and neck (H&N) hyperthermia treatment planning (HTP) is solely based on computed tomography (CT) images. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast over CT. The purpose of the authors' study is to investigate the relevance of using MRI in addition to CT for patient modeling in H&N HTP., Methods: CT and MRI scans were acquired for 11 patients in an immobilization mask. Three observers manually segmented on CT, MRI T1 weighted (MRI-T1w), and MRI T2 weighted (MRI-T2w) images the following thermo-sensitive tissues: cerebrum, cerebellum, brainstem, myelum, sclera, lens, vitreous humor, and the optical nerve. For these tissues that are used for patient modeling in H&N HTP, the interobserver variation of manual tissue segmentation in CT and MRI was quantified with the mean surface distance (MSD). Next, the authors compared the impact of CT and CT and MRI based patient models on the predicted temperatures. For each tissue, the modality was selected that led to the lowest observer variation and inserted this in the combined CT and MRI based patient model (CT and MRI), after a deformable image registration. In addition, a patient model with a detailed segmentation of brain tissues (including white matter, gray matter, and cerebrospinal fluid) was created (CT and MRIdb). To quantify the relevance of MRI based segmentation for H&N HTP, the authors compared the predicted maximum temperatures in the segmented tissues (Tmax) and the corresponding specific absorption rate (SAR) of the patient models based on (1) CT, (2) CT and MRI, and (3) CT and MRIdb., Results: In MRI, a similar or reduced interobserver variation was found compared to CT (maximum of median MSD in CT: 0.93 mm, MRI-T1w: 0.72 mm, MRI-T2w: 0.66 mm). Only for the optical nerve the interobserver variation is significantly lower in CT compared to MRI (median MSD in CT: 0.58 mm, MRI-T1w: 1.27 mm, MRI-T2w: 1.40 mm). Patient models based on CT (Tmax: 38.0 °C) and CT and MRI (Tmax: 38.1 °C) result in similar simulated temperatures, while CT and MRIdb (Tmax: 38.5 °C) resulted in significantly higher temperatures. The SAR corresponding to these temperatures did not differ significantly., Conclusions: Although MR imaging reduces the interobserver variation in most tissues, it does not affect simulated local tissue temperatures. However, the improved soft-tissue contrast provided by MRI allows generating a detailed brain segmentation, which has a strong impact on the predicted local temperatures and hence may improve simulation guided hyperthermia.

Purpose: The authors aim to improve image guidance during percutaneous coronary interventions of chronic total occlusions (CTO) by providing information obtained from computed tomography angiography (CTA) to the cardiac interventionist. To this end, the authors investigate a method to register a 3D CTA model to biplane reconstructions., Methods: The authors developed a method for registering preoperative coronary CTA with intraoperative biplane x-ray angiography (XA) images via 3D models of the coronary arteries. The models are extracted from the CTA and biplane XA images, and are temporally aligned based on CTA reconstruction phase and XA ECG signals. Rigid spatial alignment is achieved with a robust probabilistic point set registration approach using Gaussian mixture models (GMMs). This approach is extended by including orientation in the Gaussian mixtures and by weighting bifurcation points. The method is evaluated on retrospectively acquired coronary CTA datasets of 23 CTO patients for which biplane XA images are available., Results: The Gaussian mixture model approach achieved a median registration accuracy of 1.7 mm. The extended GMM approach including orientation was not significantly different (P>0.1) but did improve robustness with regards to the initialization of the 3D models., Conclusions: The authors demonstrated that the GMM approach can effectively be applied to register CTA to biplane XA images for the purpose of improving image guidance in percutaneous coronary interventions.

Purpose: There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans., Methods: Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability., Results: Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P<0.001) for computation of the epicardial fat volume., Conclusions: The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.

Purpose: Hyperthermia treatment of head and neck tumors requires accurate treatment planning, based on 3D patient models that are derived from segmented 3D images. These segmentations are currently obtained by manual outlining of the relevant tissue regions, which is a tedious and time-consuming procedure (≈ 8 h) limiting the clinical applicability of hyperthermia treatment. In this context, the authors present and evaluate an automatic segmentation algorithm for CT images of the head and neck., Methods: The proposed method combines anatomical information, based on atlas registration, with local intensity information in a graph cut framework. The method is evaluated with respect to ground truth manual delineation and compared with multiatlas-based segmentation on a dataset of 18 labeled CT images using the Dice similarity coefficient (DSC), the mean surface distance (MSD), and the Hausdorff surface distance (HSD) as evaluation measures. On a subset of 13 labeled images, the influence of different labelers on the method's accuracy is quantified and compared with the interobserver variability., Results: For the DSC, the proposed method performs significantly better for the segmentation of all the tissues, except brain stem and spinal cord. The MSD shows a significant improvement for optical nerve, eye vitreous humor, lens, and thyroid. For the HSD, the proposed method performs significantly better for eye vitreous humor and brainstem. The proposed method has a significantly better score for DSC, MSD, and HSD than the multiatlas-based method for the eye vitreous humor. For the majority of the tissues (8/11) the segmentation accuracy of the proposed method is approaching the interobserver agreement. The authors' method showed better robustness to variations in atlas labeling compared with multiatlas segmentation. Moreover, the method improved the segmentation reproducibility compared with human observer's segmentations., Conclusions: In conclusion, the proposed framework provides in an accurate automatic segmentation of head and neck tissues in CT images for the generation of 3D patient models, which improves reproducibility, and substantially reduces labor involved in therapy planning.

Purpose: The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images., Methods: The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion., Results: The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification., Conclusions: With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.