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Purpose: Breast elastography is a critical tool for improving the targeted radiotherapy treatment of breast tumors. Current breast radiotherapy imaging protocols only involve prone and supine CT scans. There is a lack of knowledge on the quantitative accuracy with which breast elasticity can be systematically measured using only prone and supine CT datasets. The purpose of this paper is to describe a quantitative elasticity estimation technique for breast anatomy using only these supine/prone patient postures. Using biomechanical, high-resolution breast geometry obtained from CT scans, a systematic assessment was performed in order to determine the feasibility of this methodology for clinically relevant elasticity distributions. Methods: A model-guided inverse analysis approach is presented in this paper. A graphics processing unit (GPU)-based linear elastic biomechanical model was employed as a forward model for the inverse analysis with the breast geometry in a prone position. The elasticity estimation was performed using a gradient-based iterative optimization scheme and a fast-simulated annealing (FSA) algorithm. Numerical studies were conducted to systematically analyze the feasibility of elasticity estimation. For simulating gravity-induced breast deformation, the breast geometry was anchored at its base, resembling the chest-wall/breast tissue interface. Ground-truth elasticity distributions were assigned to the model, representing tumor presence within breast tissue. Model geometry resolution was varied to estimate its influence on convergence of the system. A priori information was approximated and utilized to record the effect on time and accuracy of convergence. The role of the FSA process was also recorded. A novel error metric that combined elasticity and displacement error was used to quantify the systematic feasibility study. For the authors' purposes, convergence was set to be obtained when each voxel of tissue was within 1 mm of ground-truth deformation. Results: The authors' analyses showed that a ~97% model convergence was systematically observed with no-a priori information. Varying the model geometry resolution showed no significant accuracy improvements. The GPU-based forward model enabled the inverse analysis to be completed within 10-70 min. Using a priori information about the underlying anatomy, the computation time decreased by as much as 50%, while accuracy improved from 96.81% to 98.26%. The use of FSA was observed to allow the iterative estimation methodology to converge more precisely. Conclusions: By utilizing a forward iterative approach to solve the inverse elasticity problem, this work indicates the feasibility and potential of the fast reconstruction of breast tissue elasticity using supine/prone patient postures. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to explore the effects of a paraffin screen located at various positions in the maze on the neutron dose equivalent at the maze door. Methods: The neutron dose equivalent was measured at the maze door of a room containing a 15 MV linear accelerator for x-ray therapy. Measurements were performed for several positions of the paraffin screen covering only 27.5% of the cross-sectional area of the maze. The neutron dose equivalent was also measured at all screen positions. Two simple models of the neutron source were considered in which the first assumed that the source was the cross-sectional area at the inner entrance of the maze, radiating neutrons in an isotropic manner. In the second model the reduction in the neutron dose equivalent at the maze door due to the paraffin screen was considered to be a function of the mean values of the neutron fluence and energy at the screen. Results: The results of this study indicate that the equivalent dose at the maze door was reduced by a factor of 3 through the use of a paraffin screen that was placed inside the maze. It was also determined that the contributions to the dosage from areas that were not covered by the paraffin screen as viewed from the dosimeter, were 2.5 times higher than the contributions from the covered areas. This study also concluded that the contributions of the maze walls, ceiling, and floor to the total neutron dose equivalent were an order of magnitude lower than those from the surface at the far end of the maze. Conclusions: This study demonstrated that a paraffin screen could be used to reduce the neutron dose equivalent at the maze door by a factor of 3. This paper also found that the reduction of the neutron dose equivalent was a linear function of the area covered by the maze screen and that the decrease in the dose at the maze door could be modeled as an exponential function of the product [formula] at the screen. [ABSTRACT FROM AUTHOR]

Purpose: Association between tissue stiffness alteration and pathology is well known. This has formed the basis for prostate elastography imaging techniques where images of prostate tissue mechanical properties are reconstructed. In this paper, the authors present a novel prostate elastography technique which, unlike other techniques, relies on magnitude image data only. Methods: This proposed technique works in conjunction with ultrasound or magnetic resonance imaging (MRI) imaging modalities and it requires the prostate's pre- and postdeformation images as input. It uses a constrained reconstruction method where the elastic moduli of the prostate's normal and pathological tissues are determined based on an essential subset of the tissue deformation provided by the images data. The elasticity reconstruction technique uses optimization where similarity between calculated and observed shape features of the postcompression prostate image is maximized. The method was validated with an in silico phantom study followed by studies using ultrasound and MR with tissue-mimicking phantoms. Results: Using the proposed methods, it was observed that the maximum uncertainties of the reconstructed Young's modulus ratios of tumor to normal tissue were 15.6% and 9.7%, which were obtained from the transrectal ultrasound (TRUS) and MR tissue-mimicking phantom studies, respectively. Conclusions: This novel prostate elastography technique relies on prostate TRUS or MRI images that can be routinely acquired without additional imaging hardware. The phantom studies provided evidence that the proposed technique has a good potential to reconstruct prostate stiffness maps noninvasively particularly when applied in conjunction with MRI. Further studies are necessary to evaluate the technique's merits for clinical use. [ABSTRACT FROM AUTHOR]

Purpose: Estimating the fluid motion in ultrasonic videos is a crucial step in the analysis of duodenogastric reflux. Severe image noise and illumination changes in the pyloric region (the region of interest) challenge the accurate estimation of gastric flow. In this paper, the authors propose an illumination-robust optical flow method based on the weighted cross-correlation. Methods: Cross-correlation was combined with the variational optical method framework as an illumination-robust local feature identifier. In consideration of accuracy near edges, they constructed visual similarity weights according to the characteristics of ultrasonic images. A processing procedure containing coarse-to-fine step and refinement was designed to get the final results. They tested the proposed method on synthetic and real ultrasonic images and compared it with other three optical flow methods. For quantitative evaluation, two metrics of angular and amplitude error were used. Results: The synthetic results demonstrate that the proposed method performs better on ultrasonic images, with angular error of 4.1° and amplitude error of 3.3%. In qualitative comparison, the proposed method kept the motion field smooth in the homogeneous region while preserving edge information. When they used the results of the proposed method to judge the gastric flow direction, the automatic judgments agreed well with visual observation. Conclusions: The proposed method is a good tool for image velocimetry in ultrasonic images. It provides promising results to estimate the motion of gastric flow in ultrasonic videos. [ABSTRACT FROM AUTHOR]

Purpose: Mechanical properties of the arteries are essential in assessing cardiovascular diseases. New imaging modalities that allow mapping strain, shear and elasticity distributions within the arterial wall are rapidly evolving. Very recently, our group introduced an iterative optical flow-based elastography method devoted to B-mode data. In this paper, the authors propose an adaptation of the optical flow method to investigate aortic remodeling following Kawasaki disease, an early childhood vasculitis. Namely, displacement and strain of the aortic wall are used to assess aortic stiffness in this human disease model. The authors also introduce a fully developed automatic method to support postprocessing data analysis. Methods: The sequalae of Kawasaki disease on the ascending aorta were examined in children. The pathologic population (n = 4) was 15.00 ± 2.45 years old, while the healthy control population (n = 5) was 13.13 ± 0.18 years old. B-mode data were digitally recorded with commercially available cardiac echocardiography machines. Results: Kawasaki disease had a very significant impact on the aortic stiffness. Indeed, pathologic subjects' aortic wall strain estimate was significantly lower compared to healthy controls (2.75% ± 0.56% versus 4.24% ± 0.65%, respectively; p < 0.001). Similarly, displacement of the aortic wall was also significantly lower compared to controls (p < 0.001). Conclusions: The potential of the optical flow-based method to quantify aortic wall remodeling in a human disease model was demonstrated. The authors now intend to extend this investigation to a larger pathologic cohort with various degrees of vasculitis severity. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound (US) elastography measures the deformation of tissue in response to stress to derive and display its stiffness. Typically, the clinicians visually inspect and categorize the pseudo-color pattern of an elastography image into one of 5-6 different scores. This scoring method could lead to sizable variability due to its subjective nature. In this paper, we introduce a new method to objectively derive an elasticity score for differential diagnosis of thyroid nodules using US elastography. Methods: Elastography data from 106 patients (123 nodules: 103 benign and 20 malignant) referred for a fine needle aspiration (FNA) biopsy were used for this retrospective study. No external compression was applied since carotid artery pulsation was used as the compression source. Elasticity contrast index (ECI), which was derived using spatiotemporal strain information, was computed to quantify the local stiffness contrast within a nodule. A larger ECI value suggests a stiffer nodule, thus indicating an increased likelihood of being malignant. Results: The mean ECI value of malignant nodules (0.83 ± 0.23) was significantly higher than that of benign nodules (0.53 ± 0.18) (p = 0.00002). Using a cut-off value of 0.60, 19 malignant nodules and 76 benign nodules were detected correctly, leading to the sensitivity, specificity, positive predictive value and negative predictive value of 95%, 73.8%, 41.3%, and 98.7%, respectively. Conclusions: The objective determination of an elasticity score by our method could reduce variability in scoring, leading to more reliable elastography results. With further clinical validation, we believe that ultrasound elastography could be used as an FNA triage tool in managing thyroid nodules. [ABSTRACT FROM AUTHOR]

Purpose: Quantification of object elasticity properties has significant technical implications as well as important practical applications, such as medical disease diagnosis. In general, given noisy measurements on the kinematic states of the objects from imaging data, the aim is to recover the elasticity parameters for assumed material constitutive models of the objects. The implementation is complicated caused by the large dimensionality of the parameters. Methods: Various versions of the least-square (LS) methods have been widely used, which, however, do not perform well under reasonably realistic levels of disturbances. Another popular strategy, based on the extended Kalman filter (EKF), is also far from optimal and subject to divergence if either the initializations are poor or the noises are not Gaussian. In this paper, the authors propose a robust system identification paradigm for the quantitative analysis of object elasticity. It is derived and extended from the ℋ∞ filtering principles and is particularly powerful for real-world situations where the types and levels of the disturbances are unknown. Results: Using synthetic data, the authors investigate the sensitivity of the strategies toward different types (Gaussian and Poisson) and levels of noises, as well as various initializations. The experimental results show consistently superior performance of the proposed method over the LS and EKF algorithms in reliably identifying object elastic modulus distributions. Conclusions: Results from phase contrast imaging data of canine hearts and human MRI data are also presented, which demonstrate the power of the framework. [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: To use machine‐learning algorithms and blur measure (BM) operators to automatically detect motion blur in mammograms. Motion blur has been reported to reduce lesion detection performance and mask small abnormalities, resulting in failure to detect them until they reach more advanced stages. Automatic detection of blur could support the clinical decision‐making process during the mammography exam by allowing for an immediate retake, thereby preventing unnecessary expense, time, and patient anxiety. Methods: Blur was simulated mathematically to mimic the real blur seen in clinical practice. The blur point‐spread‐function (PSF) mask is generated by distributing pixel intensity of an image pixel moving under random motion within the range of blur effect (the maximum amount of tissue motion allowed). The random motion trajectory vector is generated on a super‐sampled image frame to accommodate smaller substeps; the vector was then sampled on a regular pixel grid using subpixel linear interpolation to generate the blur PSF mask. This randomly generated motion trajectory is constrained by several factors: the effects of variations in tissue elasticity, imaging exposure time, and size of blur effect (motion boundary in millimeters) were examined. The blur mask is convolved with a mammogram to create blur. Five motion blur magnitudes (0.1, 0.25, 0.5, 1.0, and 1.5 mm) were simulated on 244 and 434 mammograms from the INbreast and DDSM databases, respectively. Blur was quantified using nine BM operators for each mammogram and at each blur level. The mammograms were assigned to training (70%) and testing (30%) datasets to train three machine‐learning classifiers: Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, to distinguish five levels of blurred from unblurred mammograms, using six‐way classification. Results: For the INbreast mammograms, the average classification accuracies were 87.7%, 85.7%, and 85.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively, and the average classification accuracies for DDSM were 93.5%, 93.6%, and 92.7% for Ensemble Bagged Trees, fine Gaussian SVM, and weighted KNN, respectively. Conclusions: Preliminary results show the potential to detect simulated blur automatically using those methods. Although limited work has been done to quantify the effects of motion blur on radiologists' performance, there is evidence that motion blur might not be detected visually by a human observer and could negatively affect radiologists' lesion detection performance. As of this date, no other study has investigated the ability of machine‐learning classifiers and BM operators to detect motion blur in mammograms. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to investigate the effect of simultaneous changes in cortical porosity, tissue mineral density, and elastic properties on radial speed of sound (SOS) in cortical bone. The authors applied quantitative pulse-echo (PE) ultrasound techniques that hold much potential especially for screening of osteoporosis at primary healthcare facilities. Currently, most PE measurements of cortical thickness, a well-known indicator of fracture risk, use a predefined estimate for SOS in bone to calculate thickness. Due to variation of cortical bone porosity, the use of a constant SOS value propagates to an unknown error in cortical thickness assessment by PE ultrasound. Methods: The authors conducted 2.25 and 5.00 MHz focused PE ultrasound time of flight measurements on femoral diaphyses of 18 cadavers in vitro. Cortical porosities of the samples were determined using microcomputed tomography and related to SOS in the samples. Additionally, the effect of cortical bone porosity and mechanical properties of the calcified matrix on SOS was investigated using numerical finite difference time domain simulations. Results: Both experimental measurements and simulations demonstrated significant negative correlation between radial SOS and cortical porosity (R2 ⩾ 0.493, p < 0.01 and R2 ⩾ 0.989, p < 0.01, respectively). When a constant SOS was assumed for cortical bone, the error due to variation of cortical bone porosity (4.9%-16.4%) was about 6% in the cortical thickness assessment in vitro. Conclusions: Use of a predefined, constant value for radial SOS in cortical bone, i.e., neglecting the effect of measured variation in cortical porosity, propagated to an error of 6% in cortical thickness. This error can be critical as characteristic cortical thinning of 1.10%±1.06% per yr decreases bending strength of the distal radius and results in increased fragility in postmenopausal women. Provided that the cortical porosity can be estimated in vivo, the relationship between radial SOS and cortical porosity can be utilized and a porosity based radial SOS estimate could be implemented to determine cortical thickness. This would constitute a step toward individualized quantitative ultrasound diagnostics of osteoporosis. [ABSTRACT FROM AUTHOR]

Purpose: Human skin consists of several layers including epidermis, dermis, and hypodermis. The determination of the in vivo mechanical properties of an individual skin layer represents a great challenge to date. In this study, the authors explore the use of the supersonic shear imaging (SSI) technique and inverse analysis to determine the in vivo elastic properties of the dermis layer of human skin. Methods: The measurements are conducted on the volar forearms and dorsal forearms of 18 healthy volunteers (nine females and nine males) using the SSI technique that gives the velocities of the shear wave generated by the acoustic force. Finite element analysis is carried out to simulate the propagation of the shear wave in the multilayer soft media and the results are used to interpret the experimental data and deduce the shear modulus of the dermis layer. Results: The shear moduli of the skin dermis layer obtained for the 18 healthy volunteers exhibit significant anisotropy. A standard statistical analysis demonstrates the differences between sexes. Conclusions: This study demonstrates that the SSI technique together with the inverse analysis represents a useful tool to characterize the in vivo elastic properties of human skin. [ABSTRACT FROM AUTHOR]

Purpose: Very fast Monte Carlo (MC) simulations of proton transport have been implemented recently on graphics processing units (GPUs). However, these MCs usually use simplified models for nonelastic proton-nucleus interactions. Our primary goal is to build a GPU-based proton transport MC with detailed modeling of elastic and nonelastic proton-nucleus collisions. Methods: Using the CUDA framework, the authors implemented GPU kernels for the following tasks: (1) simulation of beam spots from our possible scanning nozzle configurations, (2) proton propagation through CT geometry, taking into account nuclear elastic scattering, multiple scattering, and energy loss straggling, (3) modeling of the intranuclear cascade stage of nonelastic interactions when they occur, (4) simulation of nuclear evaporation, and (5) statistical error estimates on the dose. To validate our MC, the authors performed (1) secondary particle yield calculations in proton collisions with therapeutically relevant nuclei, (2) dose calculations in homogeneous phantoms, (3) recalculations of complex head and neck treatment plans from a commercially available treatment planning system, and compared with GEANT4.9.6p2/TOPAS. Results: Yields, energy, and angular distributions of secondaries from nonelastic collisions on various nuclei are in good agreement with the GEANT4.9.6p2 Bertini and Binary cascade models. The 3D-gamma pass rate at 2%-2 mm for treatment plan simulations is typically 98%. The net computational time on a NVIDIA GTX680 card, including all CPU-GPU data transfers, is ~20 s for 1×107 proton histories. Conclusions: Our GPU-based MC is the first of its kind to include a detailed nuclear model to handle nonelastic interactions of protons with any nucleus. Dosimetric calculations are in very good agreement with GEANT4.9.6p2/TOPAS. Our MC is being integrated into a framework to perform fast routine clinical QA of pencil-beam based treatment plans, and is being used as the dose calculation engine in a clinically applicable MC-based IMPT treatment planning system. The detailed nuclear modeling will allow us to perform very fast linear energy transfer and neutron dose estimates on the GPU. [ABSTRACT FROM AUTHOR]

Purpose: Generally speaking, breast imaging experts and physicians select a representative slice from the strain elastographic image sequences to diagnose the tumor. Given the strain image qualities, it is difficult to make a successful diagnosis using human eyes only. The main purpose of this study is to develop an automatic and reliable method to select the representative slice from the elastography cine loops and/or video and then diagnose the tumor by means of the elastographic features generated from the selected slice. Methods: In this study, the authors collected 80 biopsy-proven breast tumors, comprising of 45 benign and 35 malignant lesions, to estimate the performance of the automatic slice selection method. Images chosen using several slice selection criteria (e.g., whole-image analysis or tumor region analysis) were compared to the physician-selected images to determine the best selection criterion. The level set tumor segmentation method was applied to the corresponding B-mode part of the representative elastographic slice to overlap tumor boundaries on strain images and to calculate elastographic features for diagnosis. Results: The experiment showed that the diagnostic performance, in terms of accuracy, sensitivity, and specificity, evaluated by the leave-one-out method, based on the elastographic features for the representative slice selected by the proposed slice selection method, was 71.3%, 91.4%, and 55.6%, respectively, while the performance values for the physician-selected slice were 65.0%, 77.1%, and 55.6%, respectively. Conclusions: Both the sensitivity and accuracy of the proposed slice selection method were better than those of the physician-selected slice, and the specificity of these two different schemes is similar. According to the statistical analysis of experimental results, the performance of the proposed slice selection method was similar to that of the physician's selection. The authors concluded that the proposed slice selection method could assist the physician in selecting the appropriate representative slice and in decreasing the time of selection. [ABSTRACT FROM AUTHOR]

Purpose: Patient-to-image registration is critical to providing surgeons with reliable guidance information in the application of image-guided neurosurgery systems. The conventional point-matching registration method, which is based on skin markers, requires expensive and time-consuming logistic support. Surface-matching registration with facial surface scans is an alternative method, but the registration accuracy is unstable and the error in the more posterior parts of the head is usually large because the scan range is limited. This study proposes a new surface-matching method using a portable 3D scanner to acquire a point cloud of the entire head to perform the patient-to-image registration. Methods: A new method for transforming the scan points from the device space into the patient space without calibration and tracking was developed. Five positioning targets were attached on a reference star, and their coordinates in the patient space were measured prior. During registration, the authors moved the scanner around the head to scan its entire surface as well as the positioning targets, and the scanner generated a unique point cloud in the device space. The coordinates of the positioning targets in the device space were automatically detected by the scanner, and a spatial transformation from the device space to the patient space could be calculated by registering them to their coordinates in the patient space that had been measured prior. A three-step registration algorithm was then used to register the patient space to the image space. The authors evaluated their method on a rigid head phantom and an elastic head phantom to verify its practicality and to calculate the target registration error (TRE) in different regions of the head phantoms. The authors also conducted an experiment with a real patient's data to test the feasibility of their method in the clinical environment. Results: In the phantom experiments, the mean fiducial registration error between the device space and the patient space, the mean surface registration error, and the mean TRE of 15 targets on the surface of each phantom were 0.34±0.01 mm and 0.33±0.02 mm, 1.17±0.02 mm and 1.34±0.10 mm, and 1.06±0.11 mm and 1.48±0.21 mm, respectively. When grouping the targets according to their positions on the head, high accuracy was achieved in all parts of the head, and the TREs were similar across different regions. The authors compared their method with the current surface registration methods that use only a part of the facial surface on the elastic phantom, and the mean TRE of 15 targets was 1.48±0.21 mm and 1.98±0.53 mm, respectively. In a clinical experiment, the mean TRE of seven targets on the patient's head surface was 1.92±0.18 mm, which was sufficient to meet clinical requirements. Conclusions: The proposed surface-matching registration method provides sufficient registration accuracy even in the posterior area of the head. The 3D point cloud of the entire head, including the facial surface and the back of the head, can be easily acquired using a portable 3D scanner. The scanner does not need to be calibrated prior or tracked by the optical tracking system during scanning. [ABSTRACT FROM AUTHOR]

Purpose: Ultrasound-based solutions for diagnosis and prognosis of prostate cancer are highly desirable. The authors have devised a method for detecting prostate cancer using a vibroelastography (VE) system developed in our group and a tissue classification approach based on texture analysis of VE images. Methods: The VE method applies wide-band mechanical vibrations to the tissue. Here, the authors report on the use of this system for cancer detection and show that the texture of VE images characterized by the first and the second order statistics of the pixel intensities form a promising set of features for tissue typing to detect prostate cancer. The system was used to image patients prior to radical surgery. The removed specimens were sectioned and studied by an experienced histopathologist. The authors registered the whole-mount histology sections to the ultrasound images using an automatic registration algorithm. This enabled the quantitative evaluation of the performance of the authors' imaging method in cancer detection in an unbiased manner. The authors used support vector machine (SVM) classification to measure the cancer detection performance of the VE method. Regions of tissue of size 5 × 5 mm, labeled as cancer and noncancer based on automatic registration to histology slides, were classified using SVM. Results: The authors report an area under ROC of 0.81 ± 0.10 in cancer detection on 1066 tissue regions from 203 images. All cancer tumors in all zones were included in this analysis and were classified versus the noncancer tissue in the peripheral zone. This outcome was obtained in leaveone- patient-out validation. Conclusions: The developed 3D prostate vibroelastography system and the proposed multiparametric approach based on statistical texture parameters from the VE images result in a promising cancer detection method. [ABSTRACT FROM AUTHOR]

Purpose: Various types of cancers including prostate cancer are known to be associated with biological changes that lead to tissue stiffening. Digital rectal examination is based on manually palpating the prostate tissue via the rectum. This test lacks sufficient accuracy required for early diagnosis which is necessary for effective management of prostate cancer. To develop an effective prostate cancer diagnostic technique, the authors propose an imaging technique that maps the distribution of the relative prostate tissue's elasticity modulus. Unlike digital rectal examination, this technique is quantitative, capable of accurately detecting small prostate lesions that cannot be sensed by manual palpation, and its accuracy is independent of the physician's experience. Methods: The proposed technique is a quasistatic elastography technique which uses ultrasound imaging to acquire tissue displacements resulting from transrectal ultrasound mechanical stimulation. The system involves a standard ultrasound imaging unit with accessibility to its radiofrequency data. The displacements are used as data for the tissue elasticity reconstruction. This reconstruction does not require tissue segmentation and is based on physics governing tissue mechanics. It is formulated using an inverse problem framework where elastic tissue deformation equations are fully inverted using an iterative scheme where each iteration involves stress calculation followed by elastic modulus updating until convergence is achieved. In silico and tissue mimicking phantom studies were conducted to validate the proposed technique, followed by a clinical pilot study involving two prostate cancer patients with whole-mount histopathology analysis on prostatectomy specimens to confirm a cancer location. Results: The phantom studies demonstrated robustness and reasonably high accuracy of the proposed method. Obtained Young's modulus ratios indicated reconstruction errors of less than 12%. Reconstructed elastic modulus images of the two clinical cases were compared to whole-mount histopathology slides where cancerous areas were identified. This comparison indicated marked tissue stiffening in the cancer area with reasonably accurate consistency observed between cancerous lesions identified by histopathology and high stiffness areas of the elastography images. Conclusions: Results obtained from the phantom and patient studies indicate that the proposed method is reasonably accurate for detecting cancerous lesions. The proposed system does not require any additional hardware attachment for mechanical stimulation or data acquisition while the elasticity reconstruction algorithm can be easily implemented, leading to a low cost system that can be potentially utilized as an effective clinical tool for prostate cancer diagnosis. [ABSTRACT FROM AUTHOR]

Purpose: X-ray mammography is the primary tool for early detection of breast cancer and for follow-up after breast conserving therapy (BCT). BCT-treated breasts are smaller, less elastic, and more sensitive to pain. Instead of the current force-controlled approach of applying the same force to each breast, pressure-controlled protocols aim to improve standardization in terms of physiology by taking breast contact area and inelasticity into account. The purpose of this study is to estimate the potential for pressure protocols to reduce discomfort and pain, particularly the number of severe pain complaints for BCT-treated breasts. Methods: A prospective observational study including 58 women having one BCT-treated breast and one untreated nonsymptomatic breast, following our hospitalˈs 18 decanewton (daN) compression protocol was performed. Breast thickness, applied force, contact area, mean pressure, breast volume, and inelasticity (mean E-modulus) were statistically compared between the within-women breast pairs, and data were used as predictors for severe pain, i.e., scores 7 and higher on an 11-point Numerical Rating Scale. Curve-fitting models were used to estimate how pressure-controlled protocols affect breast thickness, compression force, and pain experience. Results: BCT-treated breasts had on average 27% smaller contact areas, 30% lower elasticity, and 30% higher pain scores than untreated breasts (all p < 0.001). Contact area was the strongest predictor for severe pain ( p < 0.01). Since BCT-treatment is associated with an average 0.36 dm2 decrease in contact area, as well as increased pain sensitivity, BCT-breasts had on average 5.3 times higher odds for severe pain than untreated breasts. Model estimations for a pressure-controlled protocol with a 10 kPa target pressure, which is below normal arterial pressure, suggest an average 26% (range 10%-36%) reduction in pain score, and an average 77% (range 46%-95%) reduction of the odds for severe pain. The estimated increase in thickness is +6.4% for BCT breasts. Conclusions: After BCT, women have hardly any choice in avoiding an annual follow-up mammogram. Model estimations show that a 10 kPa pressure-controlled protocol has the potential to reduce pain and severe pain particularly for these women. The results highly motivate conducting further research in larger subject groups. [ABSTRACT FROM AUTHOR]

Purpose: In the scope of reference dosimetry of radiotherapy beams, Monte Carlo (MC) simulations are widely used to compute ionization chamber dose response accurately. Uncertainties related to the transport algorithm can be verified performing self-consistency tests, i.e., the so-called 'Fano cavity test.' The Fano cavity test is based on the Fano theorem, which states that under charged particle equilibrium conditions, the charged particle fluence is independent of the mass density of the media as long as the cross-sections are uniform. Such tests have not been performed yet for MC codes simulating proton transport. The objectives of this study are to design a new Fano cavity test for proton MC and to implement the methodology in two MC codes: Geant4 and PENELOPE extended to protons (PENH). Methods: The new Fano test is designed to evaluate the accuracy of proton transport. Virtual particles with an energy of E0 and a mass macroscopic cross section of [ABSTRACT FROM AUTHOR]

Purpose: Respiratory motion introduces uncertainties in tumor location and lung deformation, which often results in difficulties calculating dose distributions in thoracic radiation therapy. Deformable image registration (DIR) has ability to describe respiratory-induced lung deformation, with which the radiotherapy techniques can deliver high dose to tumors while reducing radiation in surrounding normal tissue. The authors' goal is to propose a DIR method to overcome two main challenges of the previous biomechanical model for lung deformation, i.e., the requirement of precise boundary conditions and the lack of elasticity distribution. Methods: As opposed to typical methods in biomechanical modeling, the authors' method assumes that lung tissue is inhomogeneous. The authors thus propose a DIR method combining a varying intensity flow (VF) block-matching algorithm with the finite element method (FEM) for lung deformation from end-expiratory phase to end-inspiratory phase. Specifically, the lung deformation is formulated as a stress-strain problem, for which the boundary conditions are obtained from the VF block-matching algorithm and the element specific Young's modulus distribution is estimated by solving an optimization problem with a quasi-Newton method. The authors measure the spatial accuracy of their nonuniform model as well as a standard uniform model by applying both methods to four-dimensional computed tomography images of six patients. The spatial errors produced by the registrations are computed using large numbers (>1000) of expert-determined landmark point pairs. Results: In right-left, anterior-posterior, and superior-inferior directions, the mean errors (standard deviation) produced by the standard uniform FEM model are 1.42(1.42), 1.06(1.05), and 1.98(2.10) mm whereas the authors' proposed nonuniform model reduces these errors to 0.59(0.61), 0.52(0.51), and 0.78(0.89) mm. The overall 3D mean errors are 3.05(2.36) and 1.30(0.97) mm for the uniform and nonuniform models, respectively. Conclusions: The results indicate that the proposed nonuniform model can simulate patient-specific and position-specific lung deformation via spatially varying Young's modulus estimates, which improves registration accuracy compared to the uniform model and is therefore a more suitable description of lung deformation. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer is a major public health issue for women, and early detection significantly increases survival rate. Currently, there is increased research interest in elastographic soft-tissue imaging techniques based on the correlation between pathology and mechanical stiffness. Anthropomorphic breast phantoms are critical for ex vivo validation of emerging elastographic technologies. This research develops heterogeneous breast phantoms for use in testing elastographic imaging modalities. Methods: Mechanical property estimation of eight different elastomers is performed to determine storage moduli (E′) and damping ratios (ζ) using a dynamic mechanical analyzer. Dynamic compression testing was carried out isothermally at room temperature over a range of 4-50 Hz. Silicone compositions with physiologically realistic storage modulus were chosen for mimicking skin adipose, cancerous tumors, and pectoral muscles and 13 anthropomorphic breast phantoms were constructed for ex vivo trials of digital image elastotomography (DIET) breast cancer screening system. A simpler fabrication was used to assess the possibility of multiple tumor detection using magnetic resonance elastography (MRE). Results: Silicone materials with ranges of storage moduli (E′) from 2 to 570 kPa and damping ratios (ζ) from 0.03 to 0.56 were identified. The resulting phantoms were tested in two different elastographic breast cancer diagnostic modalities. A significant contrast was successfully identified between healthy tissues and cancerous tumors both in MRE and DIET. Conclusions: The phantoms presented promise aid to researchers in elastographic imaging modalities for breast cancer detection and provide a foundation for silicone based phantom materials for mimicking soft tissues of other human organs. [ABSTRACT FROM AUTHOR]

Purpose: A novel technique is proposed to characterize lung tissue incompressibility variation during respiration. Estimating lung tissue incompressibility parameter variations resulting from air content variation throughout respiration is critical for computer assisted tumor motion tracking. Continuous tumor motion is a major challenge in lung cancer radiotherapy, especially with external beam radiotherapy. If not accounted for, this motion may lead to areas of radiation overdosage for normal tissue. Given the unavailability of imaging modality that can be used effectively for real-time lung tumor tracking, computer assisted approach based on tissue deformation estimation can be a good alternative. This approach involves lung biomechanical model where its fidelity depends on input tissue properties. This investigation shows that considering variable tissue incompressibility parameter is very important for predicting tumor motion accurately, hence improving the lung radiotherapy outcome. Methods: First, an in silico lung phantom study was conducted to demonstrate the importance of employing variable Poisson's ratio for tumor motion predication. After it was established that modeling this variability is critical for accurate tumor motion prediction, an optimization based technique was developed to estimate lung tissue Poisson's ratio as a function of respiration cycle time. In this technique, the Poisson's ratio and lung pressure value were varied systematically until optimal values were obtained, leading to maximum similarity between acquired and simulated 4D CT lung images. This technique was applied in an ex vivo porcine lung study where simulated images were constructed using the end exhale CT image and deformation fields obtained from the lung's FE modeling of each respiration time increment. To model the tissue, linear elastic and Marlow hyperelastic material models in conjunction with variable Poisson's ratio were used. Results: The phantom study showed that the tumor motion trajectory and its final locations obtained from simulations with and without considering tissue incompressibility variation were very different. For example, tumor displacements in the z direction were -11.23 and -38.10 mm obtained with the Marlow hyperelastic material model in conjunction with constant and variable Poisson's ratio, respectively. By comparing the acquired 4D-CT image sequence of the porcine lung with their image sequence counterparts obtained from the hyperelastic model with constant and variable Poisson's ratio, it was shown that using variable tissue incompressibility reduced errors significantly in tumor motion prediction. Conclusions: This investigation demonstrates the importance of incompressibility variation estimation and utilization for accurate tumor tracking in computer assisted lung external beam radiation therapy. An optimization framework was developed to estimate a Poisson's ratio function in terms of respiration cycle time using experimental image data of the lung. Utilizing this function along with respiratory system FE modeling may lead to more effective tumor targeting, hence potentially improving the outcome of lung external beam radiation therapy techniques. This is particularly true for stereotactic body radiation therapy where only one or a few fraction treatments are applied, precluding the possibility of averaging out dosimetric deviations introduced by the respiratory motion. [ABSTRACT FROM AUTHOR]

Purpose: The focus of this article is to develop signal and imaging processing methods to derive an accurate estimation of local tissue elasticity using the crawling wave (CrW) sonoelastography method. The task is to reduce noise and to improve the contrast of the elasticity map. Methods: The protocol of the CrW approach was first tested on heterogeneous elastic phantoms as a model of prostate cancers. Then, the contrast-to-noise ratio of the estimation was calculated iteratively with various sequences of algorithms to determine the optimal signal processing settings. Finally, the optimized signal processing was applied to ex vivo prostate cancer detection. The comparison of the segmented elasticity map and the histology tumor outline was made by quadrants to evaluate the diagnostic performance of the protocol. Furthermore, the CrW approach was combined with amplitude-sonoelastography to achieve a higher specificity. Results: This study demonstrated the feasibility of the proposed approach for clinical applications. In the application to ex vivo prostate cancer detection, the established approach was tested on 43 excised prostate glands. The combination of the CrW approach and amplitude-sonoelastography achieved an accuracy of over 80% for finding tumors larger than 4 mm in diameter. The elasticity values and contrast found by the CrW approach were in agreement with the previous results derived from mechanical testing. Conclusions: Crawling waves can be applied to detect prostate cancer with accuracy approaching 80% and can quantify the stiffness or shear modulus of both cancerous and noncancerous tissues. The technique therefore shows promise for guiding biopsies to suspect regions that are otherwise difficult to identify. [ABSTRACT FROM AUTHOR]

Purpose: Percutaneous thermal ablation is increasingly being used to destroy hepatic tumors in situ. The success of ablative techniques is highly dependent on adequate ablation zone monitoring, and ultrasound-based strain imaging could become a convenient and cost-effective means to delineate ablation zone boundaries. This study investigates in vivo electrode displacement-based strain imaging for monitoring hepatic ablation procedures that are difficult to perform with conventional elastography. Methods: In our method, minute displacements (less than a millimeter) are applied to the unconstrained end of the ablation electrode, resulting in localized tissue deformation within the ablation zone that provides the mechanical stimuli required for strain imaging. This article presents electrode displacement strain images of radiofrequency ablation zones created in porcine liver in vivo (n=13). Results: Cross-sectional area measurements from strain images of these ablation zones were obtained using manual and automated segmentation. Area measurements from strain images were highly correlated with areas measured on histopathology images, quantitated using linear regression (R=0.894, P<0.001 and R=0.828, P<0.001, respectively). Conclusions: This study further demonstrates that electrode displacement elastography is capable of providing high-contrast images using widely available commercial ultrasound systems which may potentially be used to assess the extent of thermal ablation zones. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) cross-correlation algorithms are necessary to estimate local displacement vector information for strain imaging. However, most of the current two-dimensional cross-correlation algorithms were developed for linear array transducers. Although sector and phased array transducers are routinely used for clinical imaging of abdominal and cardiac applications, strain imaging for these applications has been performed using one-dimensional (1D) cross-correlation analysis. However, one-dimensional cross-correlation algorithms are unable to provide accurate and precise strain estimation along all the angular insonification directions which can range from -45° to 45° with sector and phased array transducers. In addition, since sector and phased array based images have larger separations between beam lines as the pulse propagates deeper into tissue, signal decorrelation artifacts with deformation or tissue motion are more pronounced. In this article, the authors propose a multilevel two-dimensional hybrid algorithm for ultrasound sector and phased array data that demonstrate improved tracking and estimation performance when compared to the traditional 1D cross-correlation or 2D cross-correlation based methods. Experimental results demonstrate that the signal-to-noise and contrast-to-noise ratio estimates improve significantly for smaller window lengths for the hybrid method when compared to the currently used one-dimensional or two-dimensional cross-correlation algorithms. Strain imaging results on ex vivo thermal lesions created in liver tissue and in vivo on cardiac short-axis views demonstrate the improved image quality obtained with this method. [ABSTRACT FROM AUTHOR]

We describe a quasistatic method for mechanical characterization of tissue-mimicking material used in elastography. We demonstrate that it is possible to assess the elasticity modulus with a reasonable reproducibility using simple and easy tools and methods. Possessing a simple relevant technique with evaluated relative error to assess Young’s modulus of these phantoms could deeply improve the quality of the research in the field of elastography. The method was tested and validated with four samples of polyvinyl alcohol (PVA) cryogel with different elasticity values corresponding to those of stiffer soft biological tissues. Young’s moduli, varying from 70 to 180 kPa depending on the number of freeze-thaw cycles (two to five), were measured within strict measurement conditions and found to have a reproducibility varying from 4% to 8%. Relative error, estimated as the ratio between observed and reference values, varied from 16% to 32%. Besides, measurement stability over 4 months was evaluated. The method demonstrated good feasibility and acceptable reproducibility for mechanically characterizing and controlled over time phantoms used for validating new potential ultrasound imaging techniques in the field of elastography. Nevertheless, in this study, investigation was performed on gel possessing young’s modulus values ranging from 80 to 215 kPa. Some tissue values of Young’modulus were reported to be lower, ranging from 0.6 to 28 kPa as liver or glandular values. Consequently, further validation of this static method for mechanical characterization of phantom gels should be performed using softer PVA cryogel. [ABSTRACT FROM AUTHOR]

Purpose: Quantification of stiffness changes may provide important diagnostic information and aid in the early detection of cancers. Shear wave elastography is an imaging technique that assesses tissue stiffness using acoustic radiation force as an alternate to manual palpation reported previously with quasistatic elastography. In this study, the elastic properties of tissue mimicking materials, including agar, polyacrylamide (PAA), and silicone, are evaluated with an objective to determine material characteristics which resemble normal and cancerous prostate tissue. Methods: Acoustic properties and stiffness of tissue mimicking phantoms were measured using compressional mechanical testing and shear wave elastography using supersonic shear imaging. The latter is based on the principles of shear waves generated using acoustic radiation force. The evaluation included tissue mimicking materials (TMMs) within the prostate at different positions and sizes that could mimic cancerous and normal prostate tissue. Patient data on normal and prostate cancer tissues quantified using biopsy histopathology were used to validate the findings. Pathologist reports on histopathology were blinded to mechanical testing and elastographic findings. Results: Young's modulus values of 86.2 ± 4.5 and 271.5 ± 25.7 kPa were obtained for PAA mixed with 2% Al2O3 particles and silicone, respectively. Young's modulus of TMMs from mechanical compression testing showed a clear trend of increasing stiffness with an increasing percentage of agar. The silicone material had higher stiffness values when compared with PAA with Al2O3. The mean Young's modulus value in cancerous tissue was 90.5 ± 4.5 kPa as compared to 93.8 ± 4.4 and 86.2 ± 4.5 kPa obtained with PAA with 2% Al2O3 phantom at a depth of 52.4 and 36.6 mm, respectively. Conclusions: PAA mixed with Al2O3 provides the most suitable tissue mimicking material for prostate cancer tumor material, while agar could form the surrounding background to simulate normal prostate tissue. [ABSTRACT FROM AUTHOR]

Purpose: Each dynamic elastography contains multiple images; therefore, physicians need to determine the most representative image from the scanned sequence to make diagnosis. To eliminate interobserver variations on diagnoses and help doctors providing correct treatments, the authors developed an objective computer-aided diagnostic scheme without requiring manually selecting representative images for diagnosis. Methods: About 112 histological-proven lesions including 66 benign and 46 malignant tumors were involved as the material database. Suspicious lesions were automatically segmented on the first B-mode images on each captured dynamic elastography. Tissue strains inside lesions on elastograms were classified by utilizing the fuzzy c-means algorithm. In order to reduce the influence of image quality, important tumor characteristics were computed from every strain images in elastography and regressed to a probability of being malignant. Since tumor boundaries changed slightly between adjacent slices, a tumor boundary tracking scheme based on template matching was applied on slices excepting the first one. Results: The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are 85.71% (96/112), 86.96% (40/46), 84.85% (56/66), 80.00% (40/50), and 90.32% (56/62). In addition, the area under the receiver operating characteristic curve is 0.9016. Conclusions: The authors' proposed study provides a reliable computer-aided system which helps physicians to make diagnoses according to features computed from entire elastographic sequence. Experimental results illustrate that the diagnostic scheme is sufficient in distinguishing benign and malignant tumors. It is not necessary for physicians to spend a lot of time to determine the most suitable image for diagnosis. The tumor boundary tracking mechanism effectively eliminates the computation time since it slightly adjusts tumor boundaries between neighboring slices instead of segmenting the tumor contour on each image in the dynamic elastography. The system sufficiently reduces the variations of diagnosis caused by operator dependencies and image qualities and furthermore save physicians' workloads. [ABSTRACT FROM AUTHOR]