Transrectal Ultrasound Images Research Articles

Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy

Registration and fusion of magnetic resonance imaging (MRI) and transrectal ultrasound (TRUS) of the prostate can provide guidance for prostate brachytherapy. However, accurate registration remains a challenging task due to the lack of ground truth regarding voxel-level spatial correspondence, limited field of view, low contrast-to-noise ratio, and signal-to-noise ratio in TRUS. In this study, we proposed a fully automated deep learning approach based on a weakly supervised method to address these issues. We employed deep learning techniques to combine image segmentation and registration, including affine and nonrigid registration, to perform an automated deformable MRI-TRUS registration. To start with, we trained two separate fully convolutional neural networks (CNNs) to perform a pixel-wise prediction for MRI and TRUS prostate segmentation. Then, to provide the initialization of the registration, a 2D CNN was used to register MRI-TRUS prostate images using an affine registration. After that, a 3D UNET-like network was applied for nonrigid registration. For both the affine and nonrigid registration, pairs of MRI-TRUS labels were concatenated and fed into the neural networks for training. Due to the unavailability of ground-truth voxel-level correspondences and the lack of accurate intensity-based image similarity measures, we propose to use prostate label-derived volume overlaps and surface agreements as an optimization objective function for weakly supervised network training. Specifically, we proposed a hybrid loss function that integrated a Dice loss, a surface-based loss, and a bending energy regularization loss for the nonrigid registration. The Dice and surface-based losses were used to encourage the alignment of the prostate label between the MRI and the TRUS. The bending energy regularization loss was used to achieve a smooth deformation field. Thirty-six sets of patient data were used to test our registration method. The image registration results showed that the deformed MR image aligned well with the TRUS image, as judged by corresponding cysts and calcifications in the prostate. The quantitative results showed that our method produced a mean target registration error (TRE) of 2.53 ± 1.39 mm and a mean Dice loss of 0.91 ± 0.02. The mean surface distance (MSD) and Hausdorff distance (HD) between the registered MR prostate shape and TRUS prostate shape were 0.88 and 4.41 mm, respectively. This work presents a deep learning-based, weakly supervised network for accurate MRI-TRUS image registration. Our proposed method has achieved promising registration performance in terms of Dice loss, TRE, MSD, and HD.

Can transrectal prostate ultrasound compete with multiparametric MRI in the detection of clinically significant prostate cancer?

We consider the current and future role of transrectal ultrasound imaging in the diagnosis of prostate cancer, with a particular focus on the pre-biopsy localization and targeting role that multiparametric MRI (mpMRI) has come to occupy for some men in recent years. We draw a distinction between transrectal ultrasound (TRUS) used only as a means of distributing zonal biopsies with its employment as a means for identifying and targeting sonographically abnormal lesions. The role of AI in lesion identification and targeting will be reviewed. Comparisons of cost and availability, frequency of contraindications and diagnostic accuracy between these two imaging modalities will be drawn.

Multi-Channel Feature Pyramid Networks for Prostate Segmentation, Based on Transrectal Ultrasound Imaging

Accurate segmentation for transrectal ultrasound imaging (TRUS) is often a challenging medical image processing task. The problem of weak boundary between adjacent prostate tissue and non-prostate tissue, and high similarity between artifact area and prostate area has always been the difficulty of TRUS image segmentation. In this paper, we construct a multi-channel feature pyramid network (MFPN) based on deep convolutional neural network-based prostate segmentation method to process multi-scale feature maps. Each level enhances the edge characteristics of the prostate by controlling the scale of the channel. The optimized regression mechanism of the target area was used to accurately locate the prostate. Experimental results showed that the proposed method achieved the key indicator Dice similarity coefficient and average absolute distance of 0.9651 mm and 0.504 mm, which outperformed state-of-the-art approaches.

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

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. 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. 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 <0.7s per prostate was observed, which is a sufficiently short segmentation time for intraoperative implementation. Our proposed algorithm was able to provide a fast and accurate 3D segmentation across variable 3D TRUS prostate images, enabling a generalizable intraoperative solution for needle-based prostate cancer procedures. This method has the potential to decrease procedure times, supporting the increasing interest in needle-based 3D TRUS approaches.

Comparison of CTVHR and organs at risk contours between TRUS and MR images in IB cervical cancers: a proof of concept study

PurposeTo compare CTVHR and OAR dimensions and inter-rater agreement between magnetic resonance (MR) and trans-rectal ultrasound (TRUS) images in IB cervical cancer patients.MethodsIB cervical cancer patients treated with (chemo)radiotherapy plus MR-guided brachytherapy (BT) were prospectively enrolled in this study. Radiation oncologists contoured CTVHR and OARs in pre-BT MR images (MRI) and intra-operative TRUS images. These contours were subsequently compared in regard to volume and dimension. Contour inter-rater agreement analysis was also investigated using kappa index (KI). Stata 15.0 was used for statistical analysis and a p-value < 0.05 was considered statistically significant.ResultsTRUS CTVHR volumes were statistically smaller than the respective MRI contoured volumes. TRUS CTVHR thickness was found to be consistently smaller than MRI contours in all patients. No statistical difference was seen in width and height between the two different imaging modalities. MRI contours had a median KI of 0.66 (range: 0.56–0.77) while TRUS-based contours had a median KI of 0.64 (range: 0.47–0.77). Bladder and rectum had very satisfactory KI in both imaging modalities. Vaginal contours had moderate agreement in MR (0.52) and in TRUS images (0.58).ConclusionTRUS images allow good visualization of CTVHR and OARs in IB cervical cancer patients. Inter-rater contour variability was comparable between TRUS and MR images. TRUS is a promising modality on its own for image-guided BT.

Multi-needle Localization with Attention U-Net in US-guided HDR Prostate Brachytherapy.

Ultrasound (US)-guided high dose rate (HDR) prostate brachytherapy requests the clinicians to place HDR needles (catheters) into the prostate gland under transrectal US (TRUS) guidance in the operating room. The quality of the subsequent radiation treatment plan is largely dictated by the needle placements, which varies upon the experience level of the clinicians and the procedure protocols. Real-time plan dose distribution, if available, could be a vital tool to provide more subjective assessment of the needle placements, hence potentially improving the radiation plan quality and the treatment outcome. However, due to low signal-to-noise ratio (SNR) in US imaging, real-time multi-needle segmentation in 3D TRUS, which is the major obstacle for real-time dose mapping, has not been realized to date. In this study, we propose a deep learning-based method that enables accurate and real-time digitization of the multiple needles in the 3D TRUS images of HDR prostate brachytherapy. A deep learning model based on the U-Net architecture was developed to segment multiple needles in the 3D TRUS images. Attention gates were considered in our model to improve the prediction on the small needle points. Furthermore, the spatial continuity of needles was encoded into our model with total variation (TV) regularization. The combined network was trained on 3D TRUS patches with the deep supervision strategy, where the binary needle annotation images were provided as ground truth. The trained network was then used to localize and segment the HDR needles for a new patient's TRUS images. We evaluated our proposed method based on the needle shaft and tip errors against manually defined ground truth and compared our method with other state-of-art methods (U-Net and deeply supervised attention U-Net). Our method detected 96% needles of 339 needles from 23 HDR prostate brachytherapy patients with 0.290±0.236mm at shaft error and 0.442±0.831mm at tip error. For shaft localization, our method resulted in 96% localizations with less than 0.8mm error (needle diameter is 1.67mm), while for tip localization, our method resulted in 75% needles with 0mm error and 21% needles with 2mm error (TRUS image slice thickness is 2mm). No significant difference is observed (P=0.83) on tip localization between our results with the ground truth. Compared with U-Net and deeply supervised attention U-Net, the proposed method delivers a significant improvement on both shaft error and tip error (P<0.05). We proposed a new segmentation method to precisely localize the tips and shafts of multiple needles in 3D TRUS images of HDR prostate brachytherapy. The 3D rendering of the needles could help clinicians to evaluate the needle placements. It paves the way for the development of real-time plan dose assessment tools that can further elevate the quality and outcome of HDR prostate brachytherapy.

Pre-clinical evaluation of an image-guided in-situ Raman spectroscopy navigation system for targeted prostate cancer interventions.

Transrectal ultrasound (TRUS) image guidance is the standard of care for diagnostic and therapeutic interventions in prostate cancer (PCa) patients, but can lead to high false-negative rates, compromising downstream effectiveness of therapeutic choices. A promising approach to improve in-situ detection of PCa lies in using the optical properties of the tissue to discern cancer from healthy tissue. In this work, we present the first in-situ image-guided navigation system for a spatially tracked Raman spectroscopy probe integrated in a PCa workflow, capturing the optical tissue fingerprint. The probe is guided with fused TRUS/MR imaging and tested with both tissue-simulating phantoms and ex-vivo prostates. The workflow was designed to be integrated the clinical workflow for trans-perineal prostate biopsies, as well as for high-dose rate (HDR) brachytherapy. The proposed system developed in 3D Slicer includes an electromagnetically tracked Raman spectroscopy probe, along with tracked TRUS imaging automatically registered to diagnostic MRI. The proposed system is tested on both custom gelatin tissue-simulating optical phantoms and biological tissue phantoms. A random-forest classifier was then trained on optical spectrums from ex-vivo prostates following prostatectomy using our optical probe. Preliminary in-human results are presented with the Raman spectroscopy instrument to detect malignant tissue in-situ with histopathology confirmation. In 5 synthetic gelatin and biological tissue phantoms, we demonstrate the ability of the image-guided Raman system by detecting over 95% of lesions, based on biopsy samples. The included lesion volumes ranged from 0.1 to 0.61cc. We showed the compatibility of our workflow with the current HDR brachytherapy setup. In ex-vivo prostates of PCa patients, the system showed a 81% detection accuracy in high grade lesions. Pre-clinical experiments demonstrated promising results for in-situ confirmation of lesion locations in prostates using Raman spectroscopy, both in phantoms and human ex-vivo prostate tissue, which is required for integration in HDR brachytherapy procedures.

Automatic detection of brachytherapy seeds in 3D ultrasound images using a convolutional neural network

A novel approach for automatic localization of brachytherapy seeds in 3D transrectal ultrasound (TRUS) images, using machine learning based algorithm, is presented.3D radiofrequency ultrasound signals were collected from 13 patients using the linear array of the TRUS probe during the brachytherapy procedure in which needles are used for insertion of stranded seeds. Gold standard for the location of seeds on TRUS data were obtained with the guidance of the complete reconstruction of the seed locations from multiple C-arm fluoroscopy views and used in the creation of the training set. We designed and trained a convolutional neural network (CNN) model that worked on 3D cubical sub-regions of the TRUS images, that will be referred to as patches, representing seed, non-seed within a needle track and non-seed elsewhere in the images. The models were trained with these patches to detect the needle track first and then the individual seeds within the needle track. A leave-one-out cross validation approach was used to test the model on the data from eight of the patients, for whom accurate seed locations were available from fluoroscopic imaging.The total inference time was about 7 min for needle track detection in each patient’s image and approximately 1 min for seed detection in each needle, leading to a total seed detection time of less than 15 min. Our seed detection algorithm achieved precision, recall and F1_score.The results from our CNN-based method were compared to manual seed localization performed by an expert. The CNN model yielded higher precision (lower false discovery rate) compared to the manual method. The automated approach requires little modification to the current clinical setups and offers the prospect of application in real time intraoperative dosimetric analysis of the implant.

Effects of enzyme replacement therapy on bone density in late onset Pompe disease

To assess the prostate cancer (CaP) detection rates of contrast-enhanced, transrectal subharmonic ultrasound imaging (SHI).This IRB-approved study enrolled 55 subjects. The initial 5 subjects were studied for SHI optimization, while the remaining 50 were evaluated with contrast-enhanced sonography using continuous SHI, color, and power Doppler as well as conventional grayscale, continuous color, and power Doppler and SHI combined with maximum flash replenishment. A maximum of 6 directed biopsy cores were obtained from sites of greatest asymmetrical enhancement, followed by spatially distributed cores in a double sextant distribution. Subharmonic time-intensity parameters, including time to peak intensity, peak intensity, and estimated perfusion were also evaluated for each directed biopsy core. Receiver operating characteristic curve analysis and conditional logistic regression were employed to assess the benefit of each modality and the quantitative SHI parameters.Cancer was detected in 22 of 50 subjects. Among subjects with clinically significant CaP (n = 11), targeted cores were more likely to be positive (odds ratio 1.39, P = .02). The majority of patients detected by SHI demonstrated significant CaP (5/8); SHI remained an independent marker of malignancy in a multivariate logistic regression model (P = .027). Receiver operating characteristic curve analysis of imaging findings compared to biopsy results yielded diagnostic accuracies ranging from 0.59 to 0.80 for all imaging modalities with the highest being for quantitative subharmonic perfusion estimates.This first-in-humans study provides a preliminary estimate of the diagnostic accuracy of SHI for detection of clinically significant CaP (up to 80%).

Comparative study on the diagnostic value of multiparametric transrectal ultrasound and multiparametric MRI in prostate cancer

Objective The aim of the study is to compare the diagnostic value of multiparametric transrectal ultrasound(TRUS) and multiparametric magnetic resonance imaging (MRI) in prostate cancer. Methods The clinical data of 102 patients who received multiparametric TRUS (including conventional transrectal ultrasound, shear wave sonoelastography and contrast enhanced ultrasound), multiparametric MRI(including T2 weighted diffusion weighted, and dynamic contrast enhanced MRI) and laboratory tests from April 2016 to May 2018 were retrospectively analyzed. The average age was 66.1 years old, ranging 38.0-85.0 years old. The average PSA was 30.1 ng/ml, ranging 0.4-227.0 ng/ml. The average PSAD was 0.67 ng/ml2, ranging 0.02-4.27 ng/ml2. The pathology results from TRUS guided biopsy or surgical operation were chosen as gold standard. Diagnostic performance including sensitivity, specificity, positive predictive value(PPV), negative predictive value(NPV), accuracy and area under the receiver operating characteristic curve(AUROC)of multiparametric TRUS and multiparametric MRI in prostate cancer were analyzed. Results There were 62 prostate cancer and 40 BPH patients in our study. Parallel multiparametric TRUS diagnosed 63 prostate cancer and 39 BPH, and multiparametric MRI diagnosed 75 prostate cancer and 27 BPH. The sensitivity, specificity and accuracy of parallel multiparametric TRUS were 98.4%, 70.0% and 87.3%, respectively. And those of multiparametric MRI were 95.2%, 60.0% and 81.4%, respectively. The AUROC of parallel multiparametric TRUS and multiparametric MRI were 0.842 and 0.776, with no significant differences(P=0.208). Conclusion The diagnostic value of multiparametric TRUS was not inferior to multiparametric MRI in prostate cancer. Key words: Prostatic neoplasms; Prostate cancer; Magnetic resonance imaging; Transrectal ultrasound; Multiparametric

Inter-imaging accuracy of computed tomography, magnetic resonance imaging, and transrectal ultrasound in measuring prostate volume compared to the anatomic prostatic weight.

To evaluate the accuracy of transrectal ultrasound (TRUS), computed tomography (CT), and magnetic resonance imaging (MRI) compared to the reference standard of the post-surgical anatomic prostatic weight (APW). A total of 349 patients from two institutions were included. The CT and MRI dimensions, and TRUS-reported prostate volumes (PV) were obtained. The prolate ellipsoid formula was used to calculate PV. Cross-sectional measurements were evaluated and compared to the reported post-surgical pathology measurements and calculated pathology volume (path PV). A basic statistical analysis was performed using the Pearson correlation, Bland-Altman analysis, and Passing-Bablok regression. A total of 198 patients were included in the MRI group, 118 in the CT group, 295 in the TRUS group, and 51 in the all-inclusive common cohort. The MRI PV demonstrated a good to excellent correlation with the APW (r=0.79). The CT PV demonstrated a good correlation with APW (r=0.78). The TRUS PV showed a correlation with APW (r=0.67). The correlations identified in each individual group held true in the common cohort as well. The path PV showed an excellent correlation with APW (r=0.87), followed by MRI PV (r=0.81), then CT PV (r=0.73), and lastly TRUS PV (r=0.71). MRI and CT are equally effective in assessing the PV, and they can be readily utilized to guide the benign prostatic hyperplasia (BPH) management without repeating in-office TRUS. This is not only cost-effective, but also eliminates patient anxiety and discomfort.

Evaluation of transrectal ultrasound-based dosimetry for brachytherapy of prostate cancer: a single-center experience.

PurposeTo explore the possibility of intraoperative transrectal ultrasound (TRUS)-based dose verification in transperineal brachytherapy (BT) with iodine-125 (125I) seeds for prostate cancer.Material and methodsFifteen patients with prostate cancer were treated using BT with 125I seeds. Post-implant TRUS and computed tomography (CT) images were imported into treatment planning system (TPS) for dosimetry. Dosimetry parameters, including minimum dose received by 90% of the volume (D90), percentage of the volume receiving 100% of prescribed dose (V100), and percentage of the volume receiving 200% of prescribed dose (V200) were calculated based on TRUS and CT images, separately. The D90 value of TRUS-based dosimetry was transformed to its expected value. Comparisons of the dosimetric parameters between post-operative verification and preoperative plans were made by paired t-test. One-way ANOVA model was used to assess the differences in preoperative plans. Agreements were evaluated between the preoperative planning and post-operative actual dose parameters using Bland-Altman analysis.ResultsIn total, 825 of 125I seeds were implanted successfully in 15 patients. In TRUS-based dosimetry, 674 seeds (81%) were identified clearly in TRUS-based images, and the expected value of D90 parameter showed no significant differences compared with the preoperative planning and CT post-operation results (p > 0.05). In CT-based dosimetry, 810 seeds (98%) were identified clearly in CT-based images, and there was good consistency of D90, V100, and V200 values (p > 0.05). Post-implant CT-based dosimetry indicated that 125I seed implantation had fulfilled the expected plan.ConclusionsIntraoperative TRUS can be used for dosimetric verification of BT for prostate cancer.

Transrectal Subharmonic Ultrasound Imaging for Prostate Cancer Detection

OBJECTIVETo assess the prostate cancer (CaP) detection rates of contrast-enhanced, transrectal subharmonic ultrasound imaging (SHI). MATERIALS AND METHODSThis IRB-approved study enrolled 55 subjects. The initial 5 subjects were studied for SHI optimization, while the remaining 50 were evaluated with contrast-enhanced sonography using continuous SHI, color, and power Doppler as well as conventional grayscale, continuous color, and power Doppler and SHI combined with maximum flash replenishment. A maximum of 6 directed biopsy cores were obtained from sites of greatest asymmetrical enhancement, followed by spatially distributed cores in a double sextant distribution. Subharmonic time-intensity parameters, including time to peak intensity, peak intensity, and estimated perfusion were also evaluated for each directed biopsy core. Receiver operating characteristic curve analysis and conditional logistic regression were employed to assess the benefit of each modality and the quantitative SHI parameters. RESULTSCancer was detected in 22 of 50 subjects. Among subjects with clinically significant CaP (n = 11), targeted cores were more likely to be positive (odds ratio 1.39, P = .02). The majority of patients detected by SHI demonstrated significant CaP (5/8); SHI remained an independent marker of malignancy in a multivariate logistic regression model (P = .027). Receiver operating characteristic curve analysis of imaging findings compared to biopsy results yielded diagnostic accuracies ranging from 0.59 to 0.80 for all imaging modalities with the highest being for quantitative subharmonic perfusion estimates. CONCLUSIONThis first-in-humans study provides a preliminary estimate of the diagnostic accuracy of SHI for detection of clinically significant CaP (up to 80%).

Prostate deformable registration through geometric transformation by finite element method

Transrectal ultrasound (TRUS) guided needle biopsy is currently the clinical routine for diagnosis of prostate cancer. Due to the blurry TRUS imaging, magnetic resonance imaging (MRI) of prostate is usually taken prior to the biopsy procedure to assist needle target planning. In this paper, a novel geometric transformation method, which is based on the mechanics of materials, is proposed to facilitate the deformable registration of lesion sites from MRI to ultrasound imaging. In the method an enlarged shell, scaled from the geometry of prostate constructed from ultrasound imaging, is used to squeeze the prostate constructed from MRI imaging. The prostate can automatically rotate and position itself towards the target geometry, and deforms to match the target throughout the entire volumes. Transformations of 3D volumes as well as 2D sections of prostate are carried out to evaluate the transformation performance with respect to different material properties and segmentation errors. Some practical issues on applying the method to clinical biopsy operations are discussed. We show that this FEM based transformation method is promising in deformable registration and could be used in other organ image registrations.

Localisation of Zones of Cancer Detection in Prostate Gland Using Ratio Matrix and Radial Scanning of 2D Trans-rectal Ultrasound Images;

Researchers have continued to proffer various solutions to the challenge of delineating from Trans-rectal ultrasound (TRUS) 2D-images of the prostate the regions of desired property. This paper presents an algorithm that categorises the detected regions suspected to be cancerous, hyper-echoic pixels, in the prostate gland from a 2D Trans-rectal Ultrasound images into three zones. The developed algorithm uses radial scanning of the pixels of the prostate gland image from common seed point both to detect and delineate the suspected cancerous pixels into zones, namely peripheral, transition and central, by applying ratios of the anatomical zones of the prostate gland. Expert knowledge, intensity and gradient features were implemented to delineate regions of interest. MATLAB programming tool was used for creating the codes that implemented the algorithms. Samples of TRUS 2D-images of the prostate for patients with raised PSA values (>10 ng/ml) used in a previous work by Award (2007) were used for testing the algorithm. The test results showed that the algorithm could detect zones of the prostate boundary exhibit image properties for cancer cells and also the percentage of malignancy detected in zones agreed with existing research findings. Comparison of detection results with that of an expert radiologist yielded the following performance parameters; accuracy of 88.55% and sensitivity of 71.65%.

The development of high quality training program for real tine trans rectal ultrasound low dose rate (LDR) prostate brachytherapy

Although ultrasound forms a critical component of transrectal ultrasound image guided brachytherapy for prostate cancer, ultrasound training is not required as part of radiation oncology training programs, nor does any objective competency measure exist to independently assess clinical performance [ [1] Davis B.J. et al. American Brachytherapy Society consensus guidelines for transrectal ultrasound-guided permanent prostate brachytherapy. Brachytherapy. 2012; Abstract Full Text Full Text PDF Scopus (360) Google Scholar ]. Physical simulation training and objective clinical competency testing can provide a structured approach to training [ [2] McGaghie W.C. et al. A critical review of simulation-based medical education research: 2003–2009. Med. Educ. 2010; Google Scholar ], but only if suitably challenging training devices are available which replicate the complex anatomy of the male pelvis and prostate. This study describes the iterative process in the development of a range of training devices which simulate both the anthropomorphic and sonographic characteristics of the different presentations of patient specific prostate cancer. The design of the clinical features involved selection of patient cases and then rapid prototyping the different anatomical features and inverse casting these features in tissue mimicking materials (TMM). Novel TMMs were developed that had the sonographic appearance of the prostate and overlying tissues, as well as having the relevant mechanical compliance to give the training devices required haptic feedback. These devices will be used in the development of a training programme and will complement the learner’s development of the specific skills required for the procedure. The use of training strategies such as gamification allows trainees to track their own performance over time as well as relative to their peers; thereby, providing a structured and competitive approach to learning. The 3D prototyped clinical features in these devices provided a more clinically-relevant representation of the procedure, thus providing a more efficacious training opportunity.

A partial augmented reality system with live ultrasound and registered preoperative MRI for guiding robot-assisted radical prostatectomy.

We propose an image guidance system for robot assisted laparoscopic radical prostatectomy (RALRP). A virtual 3D reconstruction of the surgery scene is displayed underneath the endoscope's feed on the surgeon's console. This scene consists of an annotated preoperative Magnetic Resonance Image (MRI) registered to intraoperative 3D Trans-rectal Ultrasound (TRUS) as well as real-time sagittal 2D TRUS images of the prostate, 3D models of the prostate, the surgical instrument and the TRUS transducer. We display these components with accurate real-time coordinates with respect to the robot system. Since the scene is rendered from the viewpoint of the endoscope, given correct parameters of the camera, an augmented scene can be overlaid on the video output. The surgeon can rotate the ultrasound transducer and determine the position of the projected axial plane in the MRI using one of the registered da Vinci instruments. This system was tested in the laboratory on custom-made agar prostate phantoms. We achieved an average total registration accuracy of 3.2 ± 1.3mm. We also report on the successful application of this system in the operating room in 12 patients. The average registration error between the TRUS and the da Vinci system for the last 8 patients was 1.4 ± 0.3mm and average target registration error of 2.1 ± 0.8mm, resulting in an in vivo overall robot system to MRI mean registration error of 3.5mm or less, which is consistent with our laboratory studies.

Prospective intra/inter-observer evaluation of pre-brachytherapy cervical cancer tumor width measured in TRUS and MR imaging

BackgroundUltrasound (US) imaging has been proved as an excellent diagnostic tool in gynecology and, due to its wide availability and limited cost, is under intense investigation as base for dose adaptation in cervical cancer brachytherapy. Purpose of this work is to test inter/intra-observer uncertainties between magnetic resonance (MR) and trans-rectal ultrasound (TRUS) imaging in defining maximum tumor width before first brachytherapy (BT) application in a prospective cohort of cervical cancer patients undergoing image-guided adaptive brachytherapy (IGABT).MethodsOne hundred ten consecutive cervical cancer patients treated between 2013 and 2016 were included. Before the first BT implant patients underwent MR and TRUS scan with no applicator in place. Images were independently analyzed by three examiners, blinded to the other’s results. With clinical information at hand, maximum tumor width was measured on preBT TRUS and MR. Quantitative agreement analysis was undertaken. Intra-class correlation coefficient (ICC), Passing-Bablok and Bland Altman plots were used to evaluate the intra/inter-observers measurement agreement.ResultsAverage difference between tumor width measured on MR (HRCTVMR) and TRUS (HRCTVTRUS) was 1.3 ± 3.2 mm (p < 0.001); 1.1 ± 4.6 mm (p = 0.01) and 0.7 ± 3 mm (p = 0.01). The error was less than 3 mm in 79, 82 and 80% of the measurements for the three observers, respectively. Intra-observer ICC was 0.96 (CI95% 0.94–0.97), 0.93 (CI95% 0.9–0.95) and 0.96 (CI95% 0.95–0.98) respectively. Inter-observer ICC for HRCTVMR width measures was 0.92 (CI95% 0.89–0.94) with no difference among FIGO stages. Inter-observer ICC for HRCTVTRUS was 0.86 (CI95% 0.81–0.9). For FIGO stage I and II tumors, ICC HRCTVTRUS values were comparable to respective HRCTVMR ICC values. For larger tumors HRCTVTRUS inter-observer ICC values were lower than respective HRCTVMR although remaining acceptable.ConclusionsOur results suggest that TRUS is equivalent to MR in assessing preBT tumor maximum width in cervical cancer FIGO stage I/II. In more advanced stages TRUS seems to be slightly inferior to MR although maintaining a good agreement to gold standard imaging.

Quantitative multiparametric ultrasound for prostate cancer targeted biopsy

Prostate cancer (PCa) is conventionally diagnosed using transrectal ultrasound (TRUS) guided biopsy. B-mode ultrasound is used to direct the biopsy needle to regions of the prostate gland, but is not used to target cancer-suspicious regions due to its low specificity for PCa. We are developing multiparametric quantitative ultrasound imaging approaches to facilitate targeting ultrasound-guided biopsies toward regions suspicious for PCa. To identify PCa during TRUS imaging, we combine information from four ultrasonic techniques: acoustic radiation force impulse (ARFI) imaging, shear wave elasticity imaging (SWEI), quantitative ultrasound (QUS) and B-mode ultrasound. 3-D co-registered in vivo ultrasound data, and MRI T2-weighted and ADC data volumes have been acquired from 30 patients prior to radical prostatectomy, obtaining estimates of ARFI displacement, shear wave speed, QUS midband fit, B-mode brightness, T2 intensity, and ADC values. In each data volume, healthy and cancerous regions were manually segmented with guidance from whole mount histology; the intersection of the cancerous segmentations from all modalities was labeled as the ground truth. We will present a range of classification approaches, including LDA and SVM, to combine information from each modality in order to develop automated tools for targeted biopsy.Prostate cancer (PCa) is conventionally diagnosed using transrectal ultrasound (TRUS) guided biopsy. B-mode ultrasound is used to direct the biopsy needle to regions of the prostate gland, but is not used to target cancer-suspicious regions due to its low specificity for PCa. We are developing multiparametric quantitative ultrasound imaging approaches to facilitate targeting ultrasound-guided biopsies toward regions suspicious for PCa. To identify PCa during TRUS imaging, we combine information from four ultrasonic techniques: acoustic radiation force impulse (ARFI) imaging, shear wave elasticity imaging (SWEI), quantitative ultrasound (QUS) and B-mode ultrasound. 3-D co-registered in vivo ultrasound data, and MRI T2-weighted and ADC data volumes have been acquired from 30 patients prior to radical prostatectomy, obtaining estimates of ARFI displacement, shear wave speed, QUS midband fit, B-mode brightness, T2 intensity, and ADC values. In each data volume, healthy and cancerous regions were manually segm...

BrachyView: Reconstruction of seed positions and volume of an LDR prostate brachytherapy patient plan using a baseline subtraction algorithm

PurposeBrachyView is a novel in-body imaging system developed with the objective to provide real-time intraoperative dosimetry for low dose rate (LDR) prostate brachytherapy treatments. The BrachyView coordinates combined with conventional transrectal ultrasound (TRUS) imaging, provides the possibility to localise the effective position of the implanted seeds inside the prostate volume, providing a unique tool for intra-operative verification of the quality of the implantation. This research presents the first complete LDR brachytherapy plan reconstructed by the BrachyView system and is used to evaluate the effectiveness of an imaging algorithm with baseline subtraction. MethodsA plan featuring 98 I-125 brachytherapy seeds, with an average activity of 0.248 mCi, were implanted into a prostate gel phantom under TRUS guidance. Images of implanted seeds were obtained by the BrachyView after the implantation of seeds. The baseline subtraction algorithm is applied as a pixel-to-pixel counts subtraction and is applied to every second projection obtained after the implantation of each needle. Seed positions and effectiveness of the baseline reconstruction in the identification of seeds were verified by a high-resolution post-implant CT scan. ResultsA complete brachytherapy plan has been reconstructed with a 100% detection rate. This is possible due to the effectiveness of the baseline subtraction, with its application an overall increase of 11.3% in position accuracy and 8.2% increase in detection rate was noted. ConclusionIt has been demonstrated that the BrachyView system shows the potential to be a solution to providing clinics with the means for intraoperative dosimetry for LDR prostate brachytherapy treatments.