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Supplementary Figure 6 from Functional Neoangiogenesis Imaging of Genetically Engineered Mouse Prostate Cancer Using Three-Dimensional Power Doppler Ultrasound

We report the first application of high-frequency three-dimensional power Doppler ultrasound imaging in a genetically engineered mouse (GEM) prostate cancer model. We show that the technology sensitively and specifically depicts functional neoangiogenic blood flow because little or no flow is measurable in normal prostate tissue or tumors smaller than 2–3 mm diameter, the neoangiogenesis “switch-on” size. Vascular structures depicted by power Doppler were verified using Microfil-enhanced micro-computed tomography (micro-CT) and by correlation with microvessel distributions measured by immunohistochemistry and enhanced vascularity visualized by confocal microscopy in two GEM models [transgenic adenocarcinoma of the mouse prostate (TRAMP) and PSP94 gene-directed transgenic mouse adenocarcinoma of the prostate (PSP-TGMAP)]. Four distinct phases of neoangiogenesis in cancer development were observed, specifically, (a) an early latent phase; (b) establishment of a peripheral capsular vascular structure as a neoangiogenesis initiation site; (c) a peak in tumor vascularity that occurs before aggressive tumor growth; and (d) rapid tumor growth accompanied by decreasing vascularity. Microsurgical interventions mimicking local delivery of antiangiogenesis drugs were done by ligating arteries upstream from feeder vessels branching to the prostate. Microsurgery produced an immediate reduction of tumor blood flow, and flow remained low from 1 h to 2 weeks or longer after treatment. Power Doppler, in conjunction with micro-CT, showed that the tumors recruit secondary blood supplies from nearby vessels, which likely accounts for the continued growth of the tumors after surgery. The microsurgical model represents an advanced angiogenic prostate cancer stage in GEM mice corresponding to clinically defined hormone-refractory prostate cancer. Three-dimensional power Doppler imaging is completely noninvasive and will facilitate basic and preclinical research on neoangiogenesis in live animal models. [Cancer Res 2007;67(6):2830–9]

Supplementary Procedures from Functional Neoangiogenesis Imaging of Genetically Engineered Mouse Prostate Cancer Using Three-Dimensional Power Doppler Ultrasound

Supplementary Movie 1 from Functional Neoangiogenesis Imaging of Genetically Engineered Mouse Prostate Cancer Using Three-Dimensional Power Doppler Ultrasound

Supplementary Figure 2 from A New Three-Dimensional Ultrasound Microimaging Technology for Preclinical Studies Using a Transgenic Prostate Cancer Mouse Model

Supplementary Figure 3 from Functional Neoangiogenesis Imaging of Genetically Engineered Mouse Prostate Cancer Using Three-Dimensional Power Doppler Ultrasound

Supplementary Figure 4 from Functional Neoangiogenesis Imaging of Genetically Engineered Mouse Prostate Cancer Using Three-Dimensional Power Doppler Ultrasound

Supplementary Figure 1 from A New Three-Dimensional Ultrasound Microimaging Technology for Preclinical Studies Using a Transgenic Prostate Cancer Mouse Model

Supplementary Figure 5 from Functional Neoangiogenesis Imaging of Genetically Engineered Mouse Prostate Cancer Using Three-Dimensional Power Doppler Ultrasound

Prostate cancer is the most common cancer in adult men in North America. Preclinical studies of prostate cancer employ genetically engineered mouse models, because prostate cancer does not occur naturally in rodents. Widespread application of these models has been limited because autopsy was the only reliable method to evaluate treatment efficacy in longitudinal studies. This article reports the first use of three-dimensional ultrasound microimaging for measuring tumor progression in a genetically engineered mouse model, the 94–amino acid prostate secretory protein gene-directed transgenic prostate cancer model. Qualitative comparisons of three-dimensional ultrasound images with serial histology sections of prostate tumors show the ability of ultrasound to accurately depict the size and shape of malignant masses in live mice. Ultrasound imaging identified tumors ranging from 2.4 to 14 mm maximum diameter. The correlation coefficient of tumor diameter measurements done in vivo with three-dimensional ultrasound and at autopsy was 0.998. Prospective tumor detection sensitivity and specificity were both >90% when diagnoses were based on repeated ultrasound examinations done on separate days. Representative exponential growth curves constructed via longitudinal ultrasound imaging indicated volume doubling times of 5 and 13 days for two prostate tumors. Compared with other microimaging and molecular imaging modalities, the application of three-dimensional ultrasound imaging to prostate cancer in mice showed advantages, such as high spatial resolution and contrast in soft tissue, fast and uncomplicated protocols, and portable and economical equipment that will likely enable ultrasound to become a new microimaging modality for mouse preclinical trial studies.

Supplementary Figure 3 from A New Three-Dimensional Ultrasound Microimaging Technology for Preclinical Studies Using a Transgenic Prostate Cancer Mouse Model

Supplementary Figure Legends 1-6 from Functional Neoangiogenesis Imaging of Genetically Engineered Mouse Prostate Cancer Using Three-Dimensional Power Doppler Ultrasound

PURPOSE Image-guided needle biopsy of small, detectable lesions is crucial for early-stage diagnosis, treatment planning, and management of breast cancer. High-resolution positron emission mammography (PEM) is a dedicated functional imaging modality that can detect breast cancer independent of breast tissue density, but anatomical context and real-time needle visualization are not yet available to guide biopsy. We propose a mechatronic guidance system integrating an ultrasound (US)-guided core-needle biopsy (CNB) with high-resolution PEM localization to improve the spatial sampling of breast lesions. This paper presents the benchtop testing and phantom studies to evaluate the accuracy of the system and its constituent components for targeted PEM-US-guided biopsy under simulated high-resolution PEM localization. METHODS A mechatronic guidance system was developed to operate with the Radialis PEM system and a conventional US system. The system includes a user-operated guidance arm and end-effector biopsy device, integrating a US transducer and CNB gun, with its needle focused on a remote center of motion (RCM). Custom software modules were developed to track, display, and guide the end-effector biopsy device. Registration of the mechatronic guidance system to a simulated PEM detector plate was performed using a landmark-based method. Testing was performed with fiducials positioned in the peripheral and central regions of the simulated detector plate and registration error was quantified. Breast phantom experiments were performed under ideal detection and localization to evaluate for bias in the end-effector biopsy device. The accuracy of the complete mechatronic guidance system to perform targeted breast biopsy was assessed using breast phantoms with simulated lesions. Three-dimensional positioning error was quantified, and principal component analysis assessed for directional trends in 3D space within 95% prediction intervals. Targeted breast biopsies with test phantoms were performed and an overall in-plane needle targeting error was quantified. RESULTS The mean registration errors were 0.63 mm (N = 44) and 0.73 mm (N = 72) in the peripheral and central regions of the simulated PEM detector plate, respectively. A 3D 95% prediction ellipsoid shows an error volume

Complete tumor coverage by the thermal ablation zone and with a safety margin (5 or 10 mm) is required to achieve the entire tumor eradication in liver tumor ablation procedures. However, 2D ultrasound (US) imaging has limitations in evaluating the tumor coverage by imaging only one or multiple planes, particularly for cases with multiple inserted applicators or irregular tumor shapes. In this paper, we evaluate the intra-procedural tumor coverage using 3D US imaging and investigate whether it can provide clinically needed information. Using data from 14 cases, we employed surface- and volume-based evaluation metrics to provide information on any uncovered tumor region. For cases with incomplete tumor coverage or uneven ablation margin distribution, we also proposed a novel margin uniformity -based approach to provide quantitative applicator adjustment information for optimization of tumor coverage. Both the surface- and volume-based metrics showed that 5 of 14 cases had incomplete tumor coverage according to the estimated ablation zone. After applying our proposed applicator adjustment approach, the simulated results showed that 92.9% (13 of 14) cases achieved 100% tumor coverage and the remaining case can benefit by increasing the ablation time or power. Our proposed method can evaluate the intra-procedural tumor coverage and intuitively provide applicator adjustment information for the physician. Our 3D US-based method is compatible with the constraints of conventional US-guided ablation procedures and can be easily integrated into the clinical workflow.

Thanka murals are important cultural heritages of Tibet, but many precious murals were damaged during history. Thanka mural restoration is very important for the protection of Tibetan cultural heritage. Partial convolution has great potential for Thanka mural restoration due to its outstanding performance for inpainting irregular holes. However, three challenges prevent the existing partial convolution-based methods from solving Thanka restoration problems: 1) the features of multi-scale objects in Thanka murals cannot be extracted correctly because of single-scale partial convolution; 2) the stroke-like Thanka inpainting mode cannot be effectively simulated and learned by existing rectangular or arbitrary masks; and 3) the original content of damaged Thanka murals cannot be restored. To resolve these problems, we propose a Thanka mural inpainting method based on multi-scale adaptive partial convolution and stroke-like masks. The proposed method consists of three parts: 1) a kernel-level multi-scale adaptive partial convolution (MAPConv) to accurately discriminate valid pixels from invalid pixels, and to extract the features of multi-scale objects; 2) a parameter-configurable stroke-like mask generation method to simulate and learn the stroke-like Thanka inpainting mode; and 3) a 2-phase learning framework based on MAPConv Unet and different loss functions to restore the original content of Thanka murals. Experiments on both simulated and real damages of Thanka murals demonstrated that our approach works well on a small dataset (N=2780), generates realistic mural content, and restores the damaged Thanka murals with high speed (600 ms for multiple holes in $512\times 512$ images). The proposed end-to-end method can be applied to other small datasets-based inpainting tasks.

Purpose Permanent breast seed implant (PBSI) brachytherapy is a novel technique for early-stage breast cancer. Computed tomography (CT) images are used for treatment planning and freehand 2D ultrasound for implant guidance. The multimodality imaging approach leads to discrepancies in target identification. To address this, a prototype 3D ultrasound (3DUS) system was recently developed for PBSI. In this study, we characterize the 3DUS system performance, establish QA baselines, and develop and test a method to register 3DUS images to CT images for PBSI planning. Methods and Materials 3DUS system performance was characterized by testing distance and volume measurement accuracy, and needle template alignment accuracy. 3DUS-CT registration was achieved through point-based registration using a 3D-printed model designed and constructed to provide visible landmarks on both images and tested on an in-house made gel breast phantom. Results The 3DUS system mean distance measurement accuracy was within 1% in axial, lateral, and elevational directions. A volumetric error of 3% was observed. The mean needle template alignment error was 1.0° ± 0.3 ° and 1.3 ± 0.5 mm. The mean 3DUS-CT registration error was within 3 mm when imaging at the breast centre or across all breast quadrants. Conclusions This study provided baseline data to characterize the performance of a prototype 3DUS system for PBSI planning and developed and tested a method to obtain accurate 3DUS-CT image registration for PBSI planning. Future work will focus on system validation and characterization in a clinical context as well as the assessment of impact on treatment plans.

Objective This study aimed to determine whether there are differences in the lateral ventricular volumes, measured by three-dimensional ultrasound (3D US) depending on the posture of the neonate (right and left lateral decubitus). Study Design This was a prospective analysis of the lateral ventricular volumes of preterm neonates recruited from Victoria Hospital, London, Ontario (June 2018–November 2019). A total of 24 premature neonates were recruited. The first cohort of 18 unstable premature neonates were imaged with 3D US in their current sides providing 15 right-sided and 16 left-sided 3D US images. The neonates in the second cohort of six relatively stable infants were imaged after positioning in each lateral decubitus position for 30 minutes, resulting in 40 3D US images obtained from 20 posture change sessions. The images were segmented and the ventricle volumes in each lateral posture were compared with determine whether the posture of the head influenced the volume of the upper and lower ventricle. Results For the first cohort who did not have their posture changed, the mean of the right and left ventricle volumes were 23.81 ± 15.51 and 21.61 ± 16.19 cm3, respectively, for the 15 images obtained in a right lateral posture and 13.96 ± 8.69 and 14.92 ± 8.77 cm3, respectively, for the 16 images obtained in the left lateral posture. Similarly, for the second cohort who had their posture changed, the mean of right and left ventricle volumes were 20.92 ± 17.3 and 32.74 ± 32.33 cm3, respectively, after 30 minutes in the right lateral posture, and 21.25 ± 18.4 and 32.65 ± 31.58 cm3, respectively, after 30 minutes in the left lateral posture. Our results failed to show a statistically significant difference in ventricular volumes dependence on posture. Conclusion Head positioned to any lateral side for 30 minutes does not have any effect on the lateral ventricular volumes of neonates. Key Points

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Radiofrequency ablation (RFA) is now a widely used minimally invasive treatment method for hepatic tumors. Preoperative planning plays a vital role in RFA therapy. With increasing tumor size, multiple overlapping ablations are needed, which are challenging to optimize while considering clinical constraints. In this paper, we present a new automatic RFA planning method. First, a 2-steps set cover-based model is formulated, which can integrate multiple clinical constraints for optimization of overlapping ablations. To ensure that the planning model can be solved in a reasonable time, a search space reducing strategy is then proposed. We also developed an algorithm for automatic RFA electrode selection, which provides a proper electrode ablation zone for the planning model. The proposed method was evaluated with 20 tumors of varying sizes (0.92 cm3 to 28.4 cm3). Results showed that the proposed method can generate clinical feasible RFA plans with a minimum number of RFA electrodes and ablations, complete tumor coverage and minimized ablation of normal tissue.

Three-dimensional (3D) transrectal ultrasound (TRUS) is utilized in prostate cancer diagnosis and treatment, necessitating time-consuming manual prostate segmentation. We have previously developed an automatic 3D prostate segmentation algorithm involving deep learning prediction on radially sampled 2D images followed by 3D reconstruction, trained on a large, clinically diverse dataset with variable image quality. As large clinical datasets are rare, widespread adoption of automatic segmentation could be facilitated with efficient 2D-based approaches and the development of an image quality grading method. The complete training dataset of 6761 2D images, resliced from 206 3D TRUS volumes acquired using end-fire and side-fire acquisition methods, was split to train two separate networks using either end-fire or side-fire images. Split datasets were reduced to 1000, 500, 250, and 100 2D images. For deep learning prediction, modified U-Net and U-Net++ architectures were implemented and compared using an unseen test dataset of 40 3D TRUS volumes. A 3D TRUS image quality grading scale with three factors (acquisition quality, artifact severity, and boundary visibility) was developed to assess the impact on segmentation performance. For the complete training dataset, U-Net and U-Net++ networks demonstrated equivalent performance, but when trained using split end-fire/side-fire datasets, U-Net++ significantly outperformed the U-Net. Compared to the complete training datasets, U-Net++ trained using reduced-size end-fire and side-fire datasets demonstrated equivalent performance down to 500 training images. For this dataset, image quality had no impact on segmentation performance for end-fire images but did have a significant effect for side-fire images, with boundary visibility having the largest impact. Our algorithm provided fast (

Carotid plaque echogenicity in ultrasound images has been found to be closely correlated with the risk of stroke in atherosclerotic patients. The automatic and accurate classification of carotid plaque echogenicity is of great significance for clinically estimating the stability of carotid plaques and predicting cardiovascular events. Existing convolutional neural networks (CNNs) can provide an automatic carotid plaque echogenicity classification; however, they require a fixed-size input image, while the carotid plaques are of varying sizes. Although cropping and scaling the input carotid plaque images is promising, it will cause content loss or distortion and hence reduce the classification accuracy. In this study, we redesign the spatial pyramid pooling (SPP) and propose multilevel strip pooling (MSP) for the automatic and accurate classification of carotid plaque echogenicity in the longitudinal section. The proposed MSP module can accept arbitrarily sized carotid plaques as input and capture a long-range informative context to improve the accuracy of classification. In our experiments, we implement an MSP-based CNN by using the visual geometry group (VGG) network as the backbone. A total of 1463 carotid plaques (335 echo-rich plaques, 405 intermediate plaques, and 723 echolucent plaques) were collected from Zhongnan Hospital of Wuhan University. The 5-fold cross-validation results show that the proposed MSP-based VGGNet achieves a sensitivity of 92.1%, specificity of 95.6%, accuracy of 92.1%, and F1-score of 92.1%. These results demonstrate that our approach provides a way to enhance the applicability of CNN by enabling the acceptance of arbitrary input sizes and improving the classification accuracy of carotid plaque echogenicity, which has a great potential for an efficient and objective risk assessment of carotid plaques in the clinic.

Objective The goal of this study was to test the reliability and validity of a handheld mechanical three-dimensional (3D) ultrasound (US) device for quantifying femoral articular cartilage (FAC) against the current clinical standard of magnetic resonance imaging (MRI). Design Bilateral knee images of 25 healthy volunteers were acquired with 3D US and 3.0 T MRI. The trochlear FAC was segmented by two raters who repeated segmentations on five cases during separate sessions. MRI and 3D US segmentations were registered using a semi-automated surface-based registration algorithm, and MRI segmentations were trimmed to match the FAC region from 3D US. Intra- (n = 5) and inter-rater (n = 25) reliabilities were assessed using intraclass correlation coefficients (ICCs) calculated from FAC volumes. Relationships between MRI and 3D US were assessed using Spearman correlation and linear regression (n = 25). Results MRI intra-rater ICCs were 0.97 (0.79, 1.00) and 0.90 (0.25, 0.99) for each rater with an inter-rater ICC of 0.83 (0.48, 0.94). 3D US intra-rater ICCs were 1.00 (0.98, 1.00) and 0.98 (0.84, 1.00) for each rater with an inter-rater ICC of 0.96 (0.90, 0.98). Spearman correlation and linear regression revealed a strong correlation ρ = 0.884 (0.746, 0.949) and regression R2 = 0.848 (0.750, 0.950). Conclusion These results suggest 3D US demonstrates excellent intra- and inter-rater reliabilities and strong concurrent validity with MRI when quantifying healthy trochlear FAC volume. 3D US may reduce imaging costs and greatly improve feasibility of quantifying knee cartilage volume during knee arthritis clinical trials and patient care.

Purpose he purpose of this study was to study the dosimetric characterization of sonolucent material “TPX” to be used toward gynecologic high-dose-rate brachytherapy treatments using ultrasound-compatible cylinders in non–model-based dose calculation workflows. Methods Monte Carlo simulations were performed using EGSnrc application egs_brachy in cylinders of polymethylpentene (TPX) plastic, water, and PMMA. Simulations were performed of five 192Ir sources placed longitudinally in ∼3.7 cm diameter, 5.0 cm length cylinders (matching physical cylinders used in film measurements). TPX and PMMA dose distributions and percentage depth dose curves were compared relative to water. Film measurements were performed to validate egs_brachy simulations. TPX and PMMA cylinders were placed in a water tank using 3D-printed supports to position film radially and touching the surface of the cylinders. The same five 192Ir dwell positions were delivered as simulated in egs_brachy. Results The egs_brachy and film percentage depth doses agreed within film uncertainties. The egs_brachy relative dose difference between TPX and water was (0.74 ± 0.09)% and between PMMA and water was (-0.79 ± 0.09)% over the dose scoring phantom. Dose differences for TPX and PMMA relative to water were less than ± 1% within 5 cm of the cylinder surface. Conclusions In a solid sonolucent sheath of TPX, the dosimetric differences are comparable with PMMA and other applicator materials in clinical use. No additional uncertainty to dose calculation is introduced when treating through TPX cylinders compared with current applicator materials, and therefore, it is acceptable to perform gynecologic brachytherapy treatments with a sonolucent sheath inserted during radiation delivery.

Vessel-wall-volume (VWV) is an important three-dimensional ultrasound (3DUS) metric used in the assessment of carotid plaque burden and monitoring changes in carotid atherosclerosis in response to medical treatment. To generate the VWV measurement, we proposed an approach that combined a voxel-based fully convolution network (Voxel-FCN) and a continuous max-flow module to automatically segment the carotid media-adventitia (MAB) and lumen-intima boundaries (LIB) from 3DUS images. Voxel-FCN includes an encoder consisting of a general 3D CNN and a 3D pyramid pooling module to extract spatial and contextual information, and a decoder using a concatenating module with an attention mechanism to fuse multi-level features extracted by the encoder. A continuous max-flow algorithm is used to improve the coarse segmentation provided by the Voxel-FCN. Using 1007 3DUS images, our approach yielded a Dice-similarity-coefficient (DSC) of 93.2±3.0% for the MAB in the common carotid artery (CCA), and 91.9±5.0% in the bifurcation by comparing algorithm and expert manual segmentations. We achieved a DSC of 89.5±6.7% and 89.3±6.8% for the LIB in the CCA and the bifurcation respectively. The mean errors between the algorithm-and manually-generated VWVs were 0.2±51.2 mm3 for the CCA and −4.0±98.2 mm3 for the bifurcation. The algorithm segmentation accuracy was comparable to intra-observer manual segmentation but our approach required less than 1s, which will not alter the clinical work-flow as 10s is required to image one side of the neck. Therefore, we believe that the proposed method could be used clinically for generating VWV to monitor progression and regression of carotid plaques.

PURPOSE To develop a rapid Fourier decomposition (FD) free-breathing pulmonary 1 H MRI (FDMRI) image processing and biomarker pipeline for research use. METHODS We acquired MRI in 20 asthmatic subjects using a balanced steady-state free precession (bSSFP) sequence optimized for ventilation imaging. 2D 1 H MRI series were segmented by enforcing the spatial similarity between adjacent images and the right-to-left lung volume-ratio. The segmented lung series were co-registered using a coarse-to-fine deformable registration framework that used dual optimization techniques. All pairwise registrations were implemented in parallel and FD was performed to generate 2D ventilation-weighted maps and ventilation-defect-percent (VDP). Lung segmentation and registration accuracy were evaluated by comparing algorithm and manual lung-masks, deformed manual lung-masks, and fiducials in the moving and fixed images using Dice-similarity-coefficient (DSC), mean-absolute-distance (MAD), and target-registration-error (TRE). The relationship of FD-VDP and 3 He-VDP was evaluated using the Pearson-correlation-coefficient (r) and Bland Altman analysis. Algorithm reproducibility was evaluated using the coefficient-of-variation (CoV) and intra-class-correlation-coefficient (ICC) for segmentation, registration, and FD-VDP components. RESULTS For lung segmentation, there was a DSC of 95 ± 1.5% and MAD of 2.3 ± 0.5 mm, and for registration there was a DSC of 97 ± 0.8%, MAD of 1.6 ± 0.4 mm and TRE of 3.6 ± 1.2 mm. Reproducibility for segmentation DSC (CoV/ICC = 0.5%/0.92), registration TRE (CoV/ICC = 0.4%/0.98), and FD-VDP (Cov/ICC = 3.9%/0.97) was high. The pipeline required 10 min/subject. FD-VDP was correlated with 3 He-VDP (r = 0.69, P < 0.001) although there was a bias toward lower FD-VDP (bias = -4.9%). CONCLUSIONS We developed and evaluated a pipeline that provides a rapid and precise method for FDMRI ventilation maps.

In magnetic resonance (MR)-targeted, 3-D transrectal ultrasound (TRUS)-guided biopsy, prostate motion during the procedure increases the needle targeting error and limits the ability to accurately sample MR-suspicious tumor volumes. The robustness of the 2-D-3-D registration methods for prostate motion compensation is impacted by local optima in the search space. In this paper, we analyzed the prostate motion characteristics and investigated methods to incorporate such knowledge into the registration optimization framework to improve robustness against local optima. Rigid motion of the prostate was analyzed adopting a mixture-of-Gaussian (MoG) model using 3-D TRUS images acquired at bilateral sextant probe positions with a mechanically assisted biopsy system. The learned motion characteristics were incorporated into Powell's direction set method by devising multiple initial search positions and initial search directions. Experiments were performed on data sets acquired during clinical biopsy procedures, and registration error was evaluated using target registration error (TRE) and converged image similarity metric values after optimization. After incorporating the learned initialization positions and directions in Powell's method, 2-D-3-D registration to compensate for motion during prostate biopsy was performed with rms ± std TRE of 2.33 ± 1.09 mm with ~3 s mean execution time per registration. This was an improvement over 3.12 ± 1.70 mm observed in Powell's standard approach. For the data acquired under clinical protocols, the converged image similarity metric value improved in ≥8% of the registrations whereas it degraded only ≤1% of the registrations. The reported improvements in optimization indicate useful advancements in robustness to ensure smooth clinical integration of a registration solution for motion compensation that facilitates accurate sampling of the smallest clinically significant tumors.

Purpose To develop and validate an image-guided robotic needle delivery system for accurate and repeatable needle targeting procedures in mouse brains inside the 12cm inner diameter gradient coil insert of a 9.4T MR scanner. Many preclinical research techniques require the use of accurate needle deliveries to soft tissues, including brain tissue. Soft tissues are optimally visualized in MR images, which offer high-soft tissue contrast, as well as a range of unique imaging techniques, including functional, spectroscopy and thermal imaging, however, there are currently no solutions for delivering needles to small animal brains inside the bore of an ultra-high field MR scanner. This paper describes the mechatronic design, evaluation of MR compatibility, registration technique, mechanical calibration, the quantitative validation of the in-bore image-guided needle targeting accuracy and repeatability, and demonstrated the system's ability to deliver needles in-situ. Methods Our six degree-of-freedom, MR compatible, mechatronic system was designed to fit inside the bore of a 9.4T MR scanner and is actuated using a combination of piezoelectric and hydraulic mechanisms. The MR compatibility and targeting accuracy of the needle delivery system are evaluated to ensure that the system is precisely calibrated to perform the needle targeting procedures. A semi-automated image registration is performed to link the robot coordinates to the MR coordinate system. Soft tissue targets can be accurately localized in MR images, followed by automatic alignment of the needle trajectory to the target. Intra-procedure visualization of the needle target location and the needle were confirmed through MR images after needle insertion. Results The effects of geometric distortions and signal noise were found to be below threshold that would have an impact on the accuracy of the system. The system was found to have negligible effect on the MR image signal noise and geometric distortion. The system was mechanically calibrated and the mean image-guided needle targeting and needle trajectory accuracies were quantified in an image-guided tissue mimicking phantom experiment to be 178 ± 54μm and 0.27 ± 0.65o, respectively. Conclusions An MR image-guided system for in-bore needle deliveries to soft tissue targets in small animal models has been developed. The results of the needle targeting accuracy experiments in phantoms indicate that this system has the potential to deliver needles to the smallest soft tissue structures relevant in preclinical studies, at a wide variety of needle trajectories. Future work in the form of a fully-automated needle driver with precise depth control would benefit this system in terms of its applicability to a wider range of animal models and organ targets. This article is protected by copyright. All rights reserved.

PURPOSE Recent pulmonary imaging research has revealed that in patients with chronic obstructive pulmonary disease (COPD) and asthma, structural and functional abnormalities are spatially heterogeneous. This novel information may help optimize treatment in individual patients, monitor interventional efficacy, and develop new treatments. Moreover, by automating the measurement of regional biomarkers for the 19 different anatomical lung segments, there is an opportunity to embed imaging biomarkers into clinically acceptable clinical workflows and improve lung disease clinical care. Therefore, to exploit the regional structure-function information provided by thoracic imaging, and as a first step toward this goal, our objective was to develop a fully automated registration pipeline for thoracic x-ray computed tomography (CT) and inhaled gas functional magnetic resonance imaging (MRI) whole lung and segmental structure-function biomarkers. METHODS Thirty-five patients including 15 severe, poorly controlled asthmatics and 20 COPD patients [classified according to the global initiative for chronic obstructive lung disease (GOLD) criteria)] provided written informed consent to a study protocol approved by Health Canada and underwent pulmonary function tests, MRI, and CT during a single 2-hour visit. Using this diverse patient dataset, we developed and evaluated a joint deformable registration approach to simultaneously coregister CT with both 1 H and 3 He MRI by enforcing the similarity of the deformation fields from the two individual registrations. We derived a simpler model that was equivalent to the original challenging optimization problem through variational analysis and the simpler model gave rise to an efficient numerical solver that was parallelized on a graphics processing unit. The coregistered CT-3 He MRI and whole lung/segmental lung masks were used to generate whole lung and segmental 3 He MRI ventilation defect percent (VDP). To estimate fiducial localization reproducibility, a single observer manually identified 109 pairs of CT and 3 He MRI fiducials for 35 patient images on five separate occasions and determined the fiducial localization error (FLE). CT-3 He MRI registration accuracy was evaluated using the target registration error (TRE). Whole lung VDP generated using the algorithm was compared with VDP generated using a previously validated semiautomated approach and computational efficiency was evaluated using run time. RESULTS In 35 patients including 15 with severe asthma and 20 with COPD, mean forced expiratory volume in 1 s (FEV1 ) was 63±24%pred and FEV1 /forced vital capacity (FVC) was 54 ± 17%. FLE was 0.16 mm and 0.34 mm for 3 He MRI and CT, respectively. TRE was 4.5 ± 2.0 mm, 4.0 ± 1.7 mm, 4.8 ± 2.3 mm for asthma, COPD GOLD II, and GOLD III groups, respectively, with a mean of 4.4 ± 2.0 mm for the entire dataset. TRE was significantly improved for joint CT-1 H/3 He MRI registration compared with CT-1 H MRI rigid registration (P

Three dimensional (3D) manual segmentation of the prostate on magnetic resonance imaging (MRI) is a laborious and time-consuming task that is subject to inter-observer variability. In this study, we developed a fully automatic segmentation algorithm for T2-weighted endorectal prostate MRI and evaluated its accuracy within different regions of interest using a set of complementary error metrics. Our dataset contained 42 T2-weighted endorectal MRI from prostate cancer patients. The prostate was manually segmented by one observer on all of the images and by two other observers on a subset of 10 images. The algorithm first coarsely localizes the prostate in the image using a template matching technique. Then, it defines the prostate surface using learned shape and appearance information from a set of training images. To evaluate the algorithm, we assessed the error metric values in the context of measured inter-observer variability and compared performance to that of our previously published semi-automatic approach. The automatic algorithm needed an average execution time of ∼60 s to segment the prostate in 3D. When compared to a single-observer reference standard, the automatic algorithm has an average mean absolute distance of 2.8 mm, Dice similarity coefficient of 82%, recall of 82%, precision of 84%, and volume difference of 0.5 cm in the mid-gland. Concordant with other studies, accuracy was highest in the mid-gland and lower in the apex and base. Loss of accuracy with respect to the semi-automatic algorithm was less than the measured inter-observer variability in manual segmentation for the same task. 3

PURPOSE Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3D) transrectal ultrasound (TRUS) imaging to improve needle guidance. Using these images effectively typically requires the physician to manually segment the prostate to define the margins used for accurate registration, targeting, and other guidance techniques. However, manual prostate segmentation is a time-consuming and difficult intraoperative process, often occurring while the patient is under sedation (biopsy) or anesthetic (brachytherapy). Minimizing procedure time with a 3D TRUS prostate segmentation method could provide physicians with a quick and accurate prostate segmentation, and allow for an efficient workflow with improved patient throughput to enable faster patient access to care. The purpose of this study was to develop a supervised deep learning-based method to segment the prostate in 3D TRUS images from different facilities, generated using multiple acquisition methods and commercial ultrasound machine models to create a generalizable algorithm for needle-based prostate cancer procedures. METHODS Our proposed method for 3D segmentation involved prediction on two-dimensional (2D) slices sampled radially around the approximate central axis of the prostate, followed by reconstruction into a 3D surface. A 2D U-Net was modified, trained, and validated using images from 84 end-fire and 122 side-fire 3D TRUS images acquired during clinical biopsies and brachytherapy procedures. Modifications to the expansion section of the standard U-Net included the addition of 50% dropouts and the use of transpose convolutions instead of standard upsampling followed by convolution to reduce overfitting and improve performance, respectively. Manual contours provided the annotations needed for the training, validation, and testing datasets, with the testing dataset consisting of 20 end-fire and 20 side-fire unseen 3D TRUS images. Since predicting with 2D images has the potential to lose spatial and structural information, comparisons to 3D reconstruction and optimized 3D networks including 3D V-Net, Dense V-Net, and High-resolution 3D-Net were performed following an investigation into different loss functions. An extended selection of absolute and signed error metrics were computed, including pixel map comparisons [dice similarity coefficient (DSC), recall, and precision], volume percent differences (VPD), mean surface distance (MSD), and Hausdorff distance (HD), to assess 3D segmentation accuracy. RESULTS Overall, our proposed reconstructed modified U-Net performed with a median [first quartile, third quartile] absolute DSC, recall, precision, VPD, MSD, and HD of 94.1 [92.6, 94.9]%, 96.0 [93.1, 98.5]%, 93.2 [88.8, 95.4]%, 5.78 [2.49, 11.50]%, 0.89 [0.73, 1.09] mm, and 2.89 [2.37, 4.35] mm, respectively. When compared to the best-performing optimized 3D network (i.e., 3D V-Net with a Dice plus cross-entropy loss function), our proposed method performed with a significant improvement across nearly all metrics. A computation time

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 (3He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP 3He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP 3He MRI (r = 0.90, P < .001). Both model-predicted and HP 3He MRI VDP were correlated with forced expiratory volume in 1 second (FEV1) (model: r = -0.65, P < .001; MRI: r = -0.70, P < .001), ratio of FEV1 to forced vital capacity (model: r = -0.73, P < .001; MRI: r = -0.75, P < .001), diffusing capacity (model: r = -0.69, P < .001; MRI: r = -0.65, P < .001), and quality-of-life score (model: r = 0.59, P = .001; MRI: r = 0.65, P < .001). Conclusion Model-predicted ventilation maps generated by using CT textures and machine learning were correlated with MRI ventilation maps (r = 0.90, P < .001). © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Fain in this issue.

Purpose Quantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of carotid atherosclerosis. Various metrics have been proposed and methods of measurements ranging from manual tracing to automated segmentations have also been investigated. Of those metrics, quantification of carotid plaques by measuring vessel-wall-volume (VWV) using the segmented media-adventitia (MAB) and lumen-intima (LIB) boundaries has been shown to be sensitive to temporal changes in carotid plaque burden. Thus, semi-automatic MAB and LIB segmentation methods are required to help generate VWV measurements with high accuracy and less user interaction. Methods In this paper, we propose a semiautomatic segmentation method based on deep learning to segment the MAB and LIB from carotid three-dimensional ultrasound (3DUS) images. For the MAB segmentation, we convert the segmentation problem to a pixel-by-pixel classification problem. A dynamic convolutional neural network (Dynamic CNN) is proposed to classify the patches generated by sliding a window along the norm line of the initial contour where the CNN model is fine-tuned dynamically in each test task. The LIB is segmented by applying a region-of-interest of carotid images to a U-Net model, which allows the network to be trained end-to-end for pixel-wise classification. Results A total of 144 3DUS images were used in this development, and a threefold cross-validation technique was used for evaluation of the proposed algorithm. The proposed algorithm-generated accuracy was significantly higher than the previous methods but with less user interactions. Comparing the algorithm segmentation results with manual segmentations by an expert showed that the average Dice similarity coefficients (DSC) were 96.46 ± 2.22% and 92.84 ± 4.46% for the MAB and LIB, respectively, while only an average of 34 s (vs 1.13, 2.8 and 4.4 min in previous methods) was required to segment a 3DUS image. The interobserver experiment indicated that the DSC was 96.14 ± 1.87% between algorithm-generated MAB contours of two observers' initialization. Conclusions Our results showed that the proposed carotid plaque segmentation method obtains high accuracy and repeatability with less user interactions, suggesting that the method could be used in clinical practice to measure VWV and monitor the progression and regression of carotid plaques.

This study investigates a fast integral-kernel algorithm for classifying (labeling) the vertebra and disc structures in axial magnetic resonance images (MRI). The method is based on a hierarchy of feature levels, where pixel classifications via non-linear probability product kernels (PPKs) are followed by classifications of 2D slices, individual 3D structures and groups of 3D structures. The algorithm further embeds geometric priors based on anatomical measurements of the spine. Our classifier requires evaluations of computationally expensive integrals at each pixel, and direct evaluations of such integrals would be prohibitively time consuming. We propose an efficient computation of kernel density estimates and PPK evaluations for large images and arbitrary local window sizes via integral kernels. Our method requires a single user click for a whole 3D MRI volume, runs nearly in real-time, and does not require an intensive external training. Comprehensive evaluations over T1-weighted axial lumbar spine data sets from 32 patients demonstrate a competitive structure classification accuracy of 99%, along with a 2D slice classification accuracy of 88%. To the best of our knowledge, such a structure classification accuracy has not been reached by the existing spine labeling algorithms. Furthermore, we believe our work is the first to use integral kernels in the context of medical images.

Prostate cancer (PCa) is a common, serious form of cancer in men that is still prevalent despite ongoing developments in diagnostic oncology. Current detection methods lead to high rates of inaccurate diagnosis. We present a method to directly model and exploit temporal aspects of temporal enhanced ultrasound (TeUS) for tissue characterization, which improves malignancy prediction. We employ a probabilistic-temporal framework, namely, hidden Markov models (HMMs), for modeling TeUS data obtained from PCa patients. We distinguish malignant from benign tissue by comparing the respective log-likelihood estimates generated by the HMMs. We analyze 1100 TeUS signals acquired from 12 patients. Our results show improved malignancy identification compared to previous results, demonstrating over 85% accuracy and AUC of 0.95. Incorporating temporal information directly into the models leads to improved tissue differentiation in PCa. We expect our method to generalize and be applied to other types of cancer in which temporal-ultrasound can be recorded.

Brachytherapy, a type of radiotherapy, may be used to place radioactive sources into or in close proximity to tumors, providing a method for conformally escalating dose in the tumor and the local area surrounding the malignancy. High-dose-rate interstitial brachytherapy of vaginal tumors requires precise placement of multiple needles through holes in a plastic perineal template to deliver treatment while optimizing dose and avoiding overexposure of nearby organs at risk (OARs). Despite the importance of needle placement, image guidance for adaptive, intraoperative needle visualization, allowing misdirected needles to be identified and corrected during insertion, is not standard practice. We have developed a 360-deg three-dimensional (3-D) transvaginal ultrasound (TVUS) system using a conventional probe with a template-compatible custom sonolucent vaginal cylinder and propose its use for intraoperative needle guidance during interstitial gynecologic brachytherapy. We describe the 3-D TVUS mechanism and geometric validation, present mock phantom procedure results, and report on needle localization accuracy in patients. For the six patients imaged, landmark anatomical features and all needles were clearly visible. The implementation of 360-deg 3-D TVUS through a sonolucent vaginal cylinder provides a technique for visualizing needles and OARs intraoperatively during interstitial gynecologic brachytherapy, enabling implants to be assessed and providing the potential for image guidance.

Background and objective Modern microscopes can acquire multi-channel large histological data from tissues of human beings or animals, which contain rich biomedical information for disease diagnosis and biological feature analysis. However, due to the large size, fuzzy tissue structure, and complicated multiple elements integrated in the image color space, it is still a challenge for current software systems to effectively calculate histological data, show the inner tissue structures and unveil hidden biomedical information. Therefore, we developed new algorithms and a software platform to address this issue. Methods This paper presents a multi-channel biomedical data computing and visualization system that can efficiently process large 3D histological images acquired from high-resolution microscopes. A novelty of our system is that it can dynamically display a volume of interest and extract tissue information using a layer-based data navigation scheme. During the data exploring process, the actual resolution of the loaded data can be dynamically determined and updated, and data rendering is synchronized in four display windows at each data layer, where 2D textures are extracted from the imaging volume and mapped onto the displayed clipping planes in 3D space. Results To test the efficiency and scalability of this system, we performed extensive evaluations using several different hardware systems and large histological color datasets acquired from a CryoViz 3D digital system. The experimental results demonstrated that our system can deliver interactive data navigation speed and display detailed imaging information in real time, which is beyond the capability of commonly available biomedical data exploration software platforms. Conclusion Taking advantage of both CPU (central processing unit) main memory and GPU (graphics processing unit) graphics memory, the presented software platform can efficiently compute, process and visualize very large biomedical data and enhance data information. The performance of this system can satisfactorily address the challenges of navigating and interrogating volumetric multi-spectral large histological image at multiple resolution levels.