Medical image computing and computer-aided medical interventions applied to soft tissues. Work in progress in urology

Until recently, Computer-Aided Medical Interventions (CAMI) and Medical Robotics have focused on rigid and non deformable anatomical structures. Nowadays, special attention is paid to soft tissues, raising complex issues due to their mobility and deformation. Mini-invasive digestive surgery was probably one of the first fields where soft tissues were handled through the development of simulators, tracking of anatomical structures and specific assistance robots. However, other clinical domains, for instance urology, are concerned. Indeed, laparoscopic surgery, new tumour destruction techniques (e.g. HIFU, radiofrequency, or cryoablation), increasingly early detection of cancer, and use of interventional and diagnostic imaging modalities, recently opened new challenges to the urologist and scientists involved in CAMI. This resulted in the last five years in a very significant increase of research and developments of computer-aided urology systems. In this paper, we propose a description of the main problems related to computer-aided diagnostic and therapy of soft tissues and give a survey of the different types of assistance offered to the urologist: robotization, image fusion, surgical navigation. Both research projects and operational industrial systems are discussed

Using CamiTK for rapid prototyping of interactive Computer Assisted Medical Intervention applications

Computer Assisted Medical Intervention (CAMI hereafter) is a complex multi-disciplinary field. CAMI research requires the collaboration of experts in several fields as diverse as medicine, computer science, mathematics, instrumentation, signal processing, mechanics, modeling, automatics, optics, etc

Medical Image Registration Using Deep Neural Networks

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

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

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

Non-Cancerous Abnormalities That Could Mimic Prostate Cancer Like Signal in Multi-Parametric MRI Images

Prostate Cancer (PCa) is the most common non-cutaneous cancer in North American men. Multi-parametric magnatic resonance imaging (mpMRI) has the potential to be used as a non-invasive procedure to predict locations and prognosis of PCa. This study aims to examine non-cancerous pathology lesions and normal histology that could mimic cancer in mpMRI signals. This study includes 19 radical prostatectomy specimens from the London Health Science Centre (LHSC) that were marked with 10 strand-shaped fiducials per specimen which were used as landmarks in histology processing and ex vivo MRI. Initial registration between fiducials on histology and MR images was performed followed by the development of an interactive digital technique for deformable registration of in vivo to ex vivo MRI with digital histopathology images. The relationship between MRI signals and non-cancerous abnormalities that could mimic PCa has not been tested previously in correlation with digital histopathology imaging. The unregistered mp-MRI images are contoured by 4 individual radiology observers according to the Prostate Imaging Reporting and Data System (PI-RADS). Analysis of the radiology data showed prostatic intraepithelial neoplasia (PIN), atrophy and benign prostatic hyperplasia (BPH) as main non-cancerous abnormalities responsible for cancer like signals on mpMRI. This study will help increase the accuracy of detecting PCa and play a role in the diagnosis and classification of confounders that mimic cancer in MR images

Prostate Tumor Volume Measurement on Digital Histopathology and Magnetic Resonance Imaging

An accurate assessment of prostate tumour burden supports appropriate treatment selection, ranging from active surveillance through focal therapy, to radical whole-prostate therapies. For selected patients, knowledge of the three-dimensional locations and sizes of prostate tumours on pre-procedural imaging supports planning of effective focal therapies that preferentially target tumours, while sparing surrounding healthy tissue. In the post-prostatectomy context, pathologic measurement of tumour burden in the surgical specimen may be an independent prognostic factor determining the need for potentially life-saving adjuvant therapy. An accurate and repeatable method for tumour volume assessment based on histology sections taken from the surgical specimen would be supportive both to the clinical workflow in the post-prostatectomy setting and to imaging validation studies correlating tumour burden measurements on pre-prostatectomy imaging with reference standard histologic tumour volume measurements. Digital histopathology imaging is enabling a transition to a more objective quantification of some surgical pathology assessments, such as tumour volume, that are currently visually estimated by pathologists and subject to inter-observer variability. Histologic tumour volume measurement is challenged by the traditional 3–5 mm sparse spacing of images acquired from sections of radical prostatectomy specimens. Tumour volume estimates may benefit from a well-motivated approach to inter-slide tumour boundary interpolation that crosses these large gaps in a smooth fashion. This thesis describes a new level set-based shape interpolation method that reconstructs smooth 3D shapes based on arbitrary 2D tumour contours on digital histology slides. We measured the accuracy of this approach and used it as a reference standard against which to compare previous approaches in the literature that are simpler to implement in a clinical workflow, with the aim of determining a method for histologic tumour volume estimation that is both accurate and amenable to widespread implementation. We also measured the effect of decreasing inter-slide spacing on the repeatability of histologic tumour volume estimation. Furthermore, we used this histologic reference standard for tumour volume to measure the accuracy, inter-observer variability, and inter-sequence variability of prostate tumour volume estimation based on radiologists’ contouring of multi-parametric magnetic resonance imaging (MPMRI). Our key findings were that (1) simple approaches to histologic tumour volume estimation that are based on 2- or 3-dimensional linear tumour measurements are more accurate than those based on 1-dimensional measurements; (2) although tumour shapes produced by smooth through-slide interpolation are qualitatively substantially different from those obtained from a planimetric approach normally used as a reference standard for histologic tumour volume, the volumes obtained were similar; (3) decreasing inter-slide spacing increases repeatability of histologic tumour volume estimates, and this repeatability decreases rapidly for inter-slide spacing values greater than 5 mm; (4) on MPMRI, observers consistently overestimated tumour volume as compared to the histologic reference standard; and (5) inter-sequence variability in MPMRI-based tumour volume estimation exceeded inter-observer variability

New Mechatronic Systems for the Diagnosis and Treatment of Cancer

Both two dimensional (2D) and three dimensional (3D) imaging modalities are useful tools for viewing the internal anatomy. Three dimensional imaging techniques are required for accurate targeting of needles. This improves the efficiency and control over the intervention as the high temporal resolution of medical images can be used to validate the location of needle and target in real time. Relying on imaging alone, however, means the intervention is still operator dependent because of the difficulty of controlling the location of the needle within the image. The objective of this thesis is to improve the accuracy and repeatability of needle-based interventions over conventional techniques: both manual and automated techniques. This includes increasing the accuracy and repeatability of these procedures in order to minimize the invasiveness of the procedure. In this thesis, I propose that by combining the remote center of motion concept using spherical linkage components into a passive or semi-automated device, the physician will have a useful tracking and guidance system at their disposal in a package, which is less threatening than a robot to both the patient and physician. This design concept offers both the manipulative transparency of a freehand system, and tremor reduction through scaling currently offered in automated systems. In addressing each objective of this thesis, a number of novel mechanical designs incorporating an remote center of motion architecture with varying degrees of freedom have been presented. Each of these designs can be deployed in a variety of imaging modalities and clinical applications, ranging from preclinical to human interventions, with an accuracy of control in the millimeter to sub-millimeter range

Automated intraoperative calibration for prostate cancer brachytherapy

Purpose: Prostate cancer brachytherapy relies on an accurate spatial registration between the implant needles and the TRUS image, called "calibration". The authors propose a new device and a fast, automatic method to calibrate the brachytherapy system in the operating room, with instant error feedback. Methods: A device was CAD-designed and precision-engineered, which mechanically couples a calibration phantom with an exact replica of the standard brachytherapy template. From real-time TRUS images acquired from the calibration device and processed by the calibration system, the coordinate transformation between the brachytherapy template and the TRUS images was computed automatically. The system instantly generated a report of the target reconstruction accuracy based on the current calibration outcome. Results: Four types of validation tests were conducted. First, 50 independent, real-time calibration trials yielded an average of 0.57 6 0.13 mm line reconstruction error (LRE) relative to ground truth. Second, the averaged LRE was 0.37 6 0.25 mm relative to ground truth in tests with six different commercial TRUS scanners operating at similar imaging settings. Furthermore, testing with five different commercial stepper systems yielded an average of 0.29 6 0.16 mm LRE relative to ground truth. Finally, the system achieved an average of 0.56 6 0.27 mm target registration error (TRE) relative to ground truth in needle insertion tests through the template in a water tank. Conclusions: The proposed automatic, intraoperative calibration system for prostate cancer brachytherapy has achieved high accuracy, precision, and robustness