102 research outputs found

    Deformable registration of X-ray and MRI for post-implant dosimetry in low-dose-rate prostate brachytherapy

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    Purpose Dosimetric assessment following permanent prostate brachytherapy (PPB) commonly involves seed localization using CT and prostate delineation using coregistered MRI. However, pelvic CT leads to additional imaging dose and requires significant resources to acquire and process both CT and MRI. In this study, we propose an automatic postimplant dosimetry approach that retains MRI for soft‐tissue contouring, but eliminates the need for CT and reduces imaging dose while overcoming the inconsistent appearance of seeds on MRI with three projection x rays acquired using a mobile C‐arm. Methods Implanted seeds are reconstructed using x rays by solving a combinatorial optimization problem and deformably registered to MRI. Candidate seeds are located in MR images using local hypointensity identification. X ray‐based seeds are registered to these candidate seeds in three steps: (a) rigid registration using a stochastic evolutionary optimizer, (b) affine registration using an iterative closest point optimizer, and (c) deformable registration using a local feature point search and nonrigid coherent point drift. The algorithm was evaluated using 20 PPB patients with x rays acquired immediately postimplant and T2‐weighted MR images acquired the next day at 1.5 T with mean 0.8 × 0.8 × 3.0 mmurn:x-wiley:00942405:media:mp13667:mp13667-math-0001 voxel dimensions. Target registration error (TRE) was computed based on the distance from algorithm results to manually identified seed locations using coregistered CT acquired the same day as the MRI. Dosimetric accuracy was determined by comparing prostate D90 determined using the algorithm and the ground truth CT‐based seed locations. Results The mean ± standard deviation TREs across 20 patients including 1774 seeds were 2.23 ± 0.52 mm (rigid), 1.99 ± 0.49 mm (rigid + affine), and 1.76 ± 0.43 mm (rigid + affine + deformable). The corresponding mean ± standard deviation D90 errors were 5.8 ± 4.8%, 3.4 ± 3.4%, and 2.3 ± 1.9%, respectively. The mean computation time of the registration algorithm was 6.1 s. Conclusion The registration algorithm accuracy and computation time are sufficient for clinical PPB postimplant dosimetry

    The impact of computed high b-value images on the diagnostic accuracy of DWI for prostate cancer: A receiver operating characteristics analysis.

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    To evaluate the performance of computed high b value diffusion-weighted images (DWI) in prostate cancer detection. 97 consecutive patients who had undergone multiparametric MRI of the prostate followed by biopsy were reviewed. Five radiologists independently scored 138 lesions on native high b-value images (b = 1200 s/mm2), apparent diffusion coefficient (ADC) maps, and computed high b-value images (contrast equivalent to b = 2000 s/mm2) to compare their diagnostic accuracy. Receiver operating characteristic (ROC) analysis and McNemar's test were performed to assess the relative performance of computed high b value DWI, native high b-value DWI and ADC maps. No significant difference existed in the area under the curve (AUC) for ROCs comparing B1200 (b = 1200 s/mm2) to computed B2000 (c-B2000) in 5 readers. In 4 of 5 readers c-B2000 had significantly increased sensitivity and/or decreased specificity compared to B1200 (McNemar's p < 0.05), at selected thresholds of interpretation. ADC maps were less accurate than B1200 or c-B2000 for 2 of 5 readers (P < 0.05). This study detected no consistent improvement in overall diagnostic accuracy using c-B2000, compared with B1200 images. Readers detected more cancer with c-B2000 images (increased sensitivity) but also more false positive findings (decreased specificity)

    Tools for improving high-dose-rate prostate cancer brachytherapy using three-dimensional ultrasound and magnetic resonance imaging

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    High-dose-rate brachytherapy (HDR-BT) is an interstitial technique for the treatment of intermediate and high-risk localized prostate cancer that involves placement of a radiation source directly inside the prostate using needles. Dose-escalated whole-gland treatments have led to improvements in survival, and tumour-targeted treatments may offer future improvements in therapeutic ratio. The efficacy of tumour-targeted HDR-BT depends on imaging tools to enable accurate dose delivery to prostate sub-volumes. This thesis is focused on implementing ultrasound tools to improve HDR-BT needle localization accuracy and efficiency, and evaluating dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) for tumour localization. First, we implemented a device enabling sagittally-reconstructed 3D (SR3D) ultrasound, which provides sub-millimeter resolution in the needle insertion direction. We acquired SR3D and routine clinical images in a cohort of 12 consecutive eligible HDR-BT patients, with a total of 194 needles. The SR3D technique provided needle insertion depth errors within 5 mm for 93\% of needles versus 76\% for the clinical imaging technique, leading to increased precision in dose delivered to the prostate. Second, we implemented an algorithm to automatically segment multiple HDR-BT needles in a SR3D image. The algorithm was applied to the SR3D images from the first patient cohort, demonstrating mean execution times of 11.0 s per patient and successfully segmenting 82\% of needles within 3 mm. Third, we augmented SR3D imaging with live-2D sagittal ultrasound for needle tip localization. This combined technique was applied to another cohort of 10 HDR-BT patients, reducing insertion depth errors compared to routine imaging from a range of [-8.1 mm, 7.7 mm] to [-6.2 mm, 5.9 mm]. Finally, we acquired DCE-MRI in 16 patients scheduled to undergo prostatectomy, using either high spatial resolution or high temporal resolution imaging, and compared the images to whole-mount histology. The high spatial resolution images demonstrated improved high-grade cancer classification compared to the high temporal resolution images, with areas under the receiver operating characteristic curve of 0.79 and 0.70, respectively. In conclusion, we have translated and evaluated specialized imaging tools for HDR-BT which are ready to be tested in a clinical trial investigating tumour-targeted treatment

    Image quality assessment by overlapping task-specific and task-agnostic measures: application to prostate multiparametric MR images for cancer segmentation

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    Image quality assessment (IQA) in medical imaging can be used to ensure that downstream clinical tasks can be reliably performed. Quantifying the impact of an image on the specific target tasks, also named as task amenability, is needed. A task-specific IQA has recently been proposed to learn an image-amenability-predicting controller simultaneously with a target task predictor. This allows for the trained IQA controller to measure the impact an image has on the target task performance, when this task is performed using the predictor, e.g. segmentation and classification neural networks in modern clinical applications. In this work, we propose an extension to this task-specific IQA approach, by adding a task-agnostic IQA based on auto-encoding as the target task. Analysing the intersection between low-quality images, deemed by both the task-specific and task-agnostic IQA, may help to differentiate the underpinning factors that caused the poor target task performance. For example, common imaging artefacts may not adversely affect the target task, which would lead to a low task-agnostic quality and a high task-specific quality, whilst individual cases considered clinically challenging, which can not be improved by better imaging equipment or protocols, is likely to result in a high task-agnostic quality but a low task-specific quality. We first describe a flexible reward shaping strategy which allows for the adjustment of weighting between task-agnostic and task-specific quality scoring. Furthermore, we evaluate the proposed algorithm using a clinically challenging target task of prostate tumour segmentation on multiparametric magnetic resonance (mpMR) images, from 850 patients. The proposed reward shaping strategy, with appropriately weighted task-specific and task-agnostic qualities, successfully identified samples that need re-acquisition due to defected imaging process.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://www.melba-journal.or

    Multi Parametric Magnetic Resonance Imaging in the early detection and risk stratification of prostate cancer: The PROMIS trial

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    Although prostate cancer is the most common cancer in men, it remains a difficult and controversial disease in terms of its diagnostic, risk stratification and treatment pathway. This is mainly due to the shortcomings of the standard diagnostic test, trans rectal ultrasound guided biopsy (TRUSBx), that is triggered following an elevated serum prostate specific antigen (PSA) test and the lack of agreement on disease thresholds that correlate to patient risk, if left untreated (and thus undetected). These factors often complicate the selection of the appropriate management that best fits the individual patient. In this doctoral thesis I propose, examine and validate a different approach that aims to shift the current diagnostic paradigm to that of incorporating an imaging test, multi-parametric magnetic resonance imaging (MP-MRI), prior to TRUS biopsy. First, I will discuss the nature of prostate cancer and highlight the shortcomings of the current diagnostic pathway and their implications. Second, I will analyze the shortcomings in early MP-MRI research that might have hindered its acceptance and adoption into the pathway and review the advances in research that occurred since I started my research. Third, I will discuss the rationale and methodological design considerations behind the PROstate Mri Imaging Study (PROMIS). PROMIS was a multicentre diagnostic paired validating confirmatory cohort study conducted to provide level 1b evidence on diagnostic accuracy of MP-MRI. It was designed to avoid the pitfalls identified in the current literature. I will discuss and analyze the design, conduct and results of the trial and its implications. Finally, I will discuss the wider implications of my work on the clinical practice of prostate cancer management and the future research opportunities made possible by the PROMIS data and its findings

    Biomechanical Modeling and Inverse Problem Based Elasticity Imaging for Prostate Cancer Diagnosis

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    Early detection of prostate cancer plays an important role in successful prostate cancer treatment. This requires screening the prostate periodically after the age of 50. If screening tests lead to prostate cancer suspicion, prostate needle biopsy is administered which is still considered as the clinical gold standard for prostate cancer diagnosis. Given that needle biopsy is invasive and is associated with issues including discomfort and infection, it is desirable to develop a prostate cancer diagnosis system that has high sensitivity and specificity for early detection with a potential to improve needle biopsy outcome. Given the complexity and variability of prostate cancer pathologies, many research groups have been pursuing multi-parametric imaging approach as no single modality imaging technique has proven to be adequate. While imaging additional tissue properties increases the chance of reliable prostate cancer detection and diagnosis, selecting an additional property needs to be done carefully by considering clinical acceptability and cost. Clinical acceptability entails ease with respect to both operating by the radiologist and patient comfort. In this work, effective tissue biomechanics based diagnostic techniques are proposed for prostate cancer assessment with the aim of early detection and minimizing the numbers of prostate biopsies. The techniques take advantage of the low cost, widely available and well established TRUS imaging method. The proposed techniques include novel elastography methods which were formulated based on an inverse finite element frame work. Conventional finite element analysis is known to have high computational complexity, hence computation time demanding. This renders the proposed elastography methods not suitable for real-time applications. To address this issue, an accelerated finite element method was proposed which proved to be suitable for prostate elasticity reconstruction. In this method, accurate finite element analysis of a large number of prostates undergoing TRUS probe loadings was performed. Geometry input and displacement and stress fields output obtained from the analysis were used to train a neural network mapping function to be used for elastopgraphy imaging of prostate cancer patients. The last part of the research presented in this thesis tackles an issue with the current 3D TRUS prostate needle biopsy. Current 3D TRUS prostate needle biopsy systems require registering preoperative 3D TRUS to intra-operative 2D TRUS images. Such image registration is time-consuming while its real-time implementation is yet to be developed. To bypass this registration step, concept of a robotic system was proposed which can reliably determine the preoperative TRUS probe position relative to the prostate to place at the same position relative to the prostate intra-operatively. For this purpose, a contact pressure feedback system is proposed to ensure similar prostate deformation during 3D and 2D image acquisition in order to bypass the registration step

    Computer-Assisted Characterization of Prostate Cancer on Magnetic Resonance Imaging

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    Prostate cancer (PCa) is one of the most prevalent cancers among men. Early diagnosis can improve survival and reduce treatment costs. Current inter-radiologist variability for detection of PCa is high. The use of multi-parametric magnetic resonance imaging (mpMRI) with machine learning algorithms has been investigated both for improving PCa detection and for PCa diagnosis. Widespread clinical implementation of computer-assisted PCa lesion characterization remains elusive; critically needed is a model that is validated against a histologic reference standard that is densely sampled in an unbiased fashion. We address this using our technique for highly accurate fusion of mpMRI with whole-mount digitized histology of the surgical specimen. In this thesis, we present models for characterization of malignant, benign and confounding tissue and aggressiveness of PCa. Further validation on a larger dataset could enable improved characterization performance, improving survival rates and enabling a more personalized treatment plan

    Deep learning applications in the prostate cancer diagnostic pathway

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    Prostate cancer (PCa) is the second most frequently diagnosed cancer in men worldwide and the fifth leading cause of cancer death in men, with an estimated 1.4 million new cases in 2020 and 375,000 deaths. The risk factors most strongly associated to PCa are advancing age, family history, race, and mutations of the BRCA genes. Since the aforementioned risk factors are not preventable, early and accurate diagnoses are a key objective of the PCa diagnostic pathway. In the UK, clinical guidelines recommend multiparametric magnetic resonance imaging (mpMRI) of the prostate for use by radiologists to detect, score, and stage lesions that may correspond to clinically significant PCa (CSPCa), prior to confirmatory biopsy and histopathological grading. Computer-aided diagnosis (CAD) of PCa using artificial intelligence algorithms holds a currently unrealized potential to improve upon the diagnostic accuracy achievable by radiologist assessment of mpMRI, improve the reporting consistency between radiologists, and reduce reporting time. In this thesis, we build and evaluate deep learning-based CAD systems for the PCa diagnostic pathway, which address gaps identified in the literature. First, we introduce a novel patient-level classification framework, PCF, which uses a stacked ensemble of convolutional neural networks (CNNs) and support vector machines (SVMs) to assign a probability of having CSPCa to patients, using mpMRI and clinical features. Second, we introduce AutoProstate, a deep-learning powered framework for automated PCa assessment and reporting; AutoProstate utilizes biparametric MRI and clinical data to populate an automatic diagnostic report containing segmentations of the whole prostate, prostatic zones, and candidate CSPCa lesions, as well as several derived characteristics that are clinically valuable. Finally, as automatic segmentation algorithms have not yet reached the desired robustness for clinical use, we introduce interactive click-based segmentation applications for the whole prostate and prostatic lesions, with potential uses in diagnosis, active surveillance progression monitoring, and treatment planning

    Medical Image Registration Using Deep Neural Networks

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    Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications
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