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

Transrectal ultrasound image processing for brachytherapy applications

In this thesis, we propose a novel algorithm for detecting needles and their corresponding implanted radioactive seed locations in the prostate. The seed localization process is carried out efficiently using separable Gaussian filters in a probabilistic Gibbs random field framework. An approximation of the needle path through the prostate volume is obtained using a polynomial fit. The seeds are then detected and assigned to their corresponding needles by calculating local maxima of the voronoi region around the needle position. In our experiments, we were able to successfully localize over 85% of the implanted seeds. Furthermore, as a regular part of a brachytherapy cancer treatment, patient’s prostate is scanned using a trans-rectal ultrasound probe, its boundary is manually outlined, and its volume is estimated for dosimetry purposes. In this thesis, we also propose a novel semi-automatic segmentation algorithm for prostate boundary detection that requires a reduced amount of radiologist’s input, and thus speeds up the surgical procedure. Saved time can be used to re-scan the prostate during the operation and accordingly adjust the treatment plan. The proposed segmentation algorithm utilizes texture differences between ultrasound images of the prostate tissue and the surrounding tissues. It is carried out in 5 the polar coordinate system and it uses three-dimensional data correlation to improve the smoothness and reliability of the segmentation. Test results show that the boundary segmentation obtained from the algorithm can reduce manual input by the factor of 3, without significantly affecting the accuracy of the segmentation (i.e. semi-automatically estimated prostate volume is within 90% of the original estimate)

MRI/TRUS data fusion for brachytherapy

BACKGROUND: Prostate brachytherapy consists in placing radioactive seeds for tumour destruction under transrectal ultrasound imaging (TRUS) control. It requires prostate delineation from the images for dose planning. Because ultrasound imaging is patient- and operator-dependent, we have proposed to fuse MRI data to TRUS data to make image processing more reliable. The technical accuracy of this approach has already been evaluated. METHODS: We present work in progress concerning the evaluation of the approach from the dosimetry viewpoint. The objective is to determine what impact this system may have on the treatment of the patient. Dose planning is performed from initial TRUS prostate contours and evaluated on contours modified by data fusion. RESULTS: For the eight patients included, we demonstrate that TRUS prostate volume is most often underestimated and that dose is overestimated in a correlated way. However, dose constraints are still verified for those eight patients. CONCLUSIONS: This confirms our initial hypothesis

MR-Guided Interventions for Prostate Cancer

Cataloged from PDF version of article.MR imaging is currently the most effective diagnostic imaging tool for visualizing the anatomy and pathology of the prostate gland. Currently, the practicality and cost effectiveness of transrectal ultrasound dominates image guidance for needle-based prostate interventions. Challenges to the integration of diagnostic and interventional MR imaging have included the lack of real-time feedback, the complexity of the imaging technique, and limited access to the perineum within the geometric constraints of the MR imaging scanner. Two basic strategies have been explored and clinically demonstrated in the literature: (1) coregistration of previously acquired diagnostic MR imaging to interventional TRUS or open scanner MR images, and (2) stereotactic needle interventions within conventional diagnostic scanners using careful patient positioning or the aid of simple manipulators. Currently, researchers are developing techniques that render MR imaging the method of choice for the direct guidance of many procedures. This article focuses on needle-based interventions for prostate cancer, including biopsy, brachytherapy, and thermal therapy. With rapid progress in biologic imaging of the prostate gland, the authors believe that MR imaging guidance will play an increasing role in the diagnosis and treatment of prostate cancer. © 2005 Elsevier Inc. All rights reserved

AUTOMATIC PROSTATE BOUNDARY SEGMENTATION FOR 2D ULTRASOUND IMAGES

Segmenting the prostate boundary is essential in determining the dose plan needed for a successful brachytherapy procedure - an effective and a commonly used treatment for prostate cancer. However, manual segmentation is time consuming and can introduce inter and intra-operator variability. In this thesis, we describe an algorithm for segmenting the prostate from two dimensional ultrasound (2D US) images, which can be either semi-automatic, requiring only one user input, or fully-automatic, with some assumptions of image acquisition. Segmentation begins with the user inputting the approximate centre of the prostate for the semi-automatic version of the algorithm, or with assuming the centre of the prostate to be at the centre of the image for the fully-automatic version. The image is then filtered with a Laplacian of Gaussian (LoG) filter that identifies prostate edge candidates. The next step removes most of the false edges (not on the prostate boundary), and keeps as many true edges (on the boundary) as possible. Then, domain knowledge is used to remove any prostate boundary candidates that are probably false edge pixels. The image is then scanned along radiai lines and only the first-detected boundary candidates are kept. The final step involves the removal of some remaining false edge pixels by fitting a polynomial to the image points, removing the point with the maximum distance from the fit, and repeating the process until this maximum distance is less than 4mm. The resulting candidate edges form an initial model that is then deformed using the Discrete Dynamic Contour (DDC) model to obtain a closed contour of the prostate boundary. The accuracy of the prostate boundary that was produced by both versions of the algorithm was evaluated by comparing it to a contour that was manually iii outlined by an expert radiologist. We segmented 51 2D Transrectal ultrasound (TRUS) prostate images using both versions of the algorithm and found that the mean distance between the contours produced by our algorithm and the manual outlines was 0.7 ± 0.3 mm for the semi-automatic version and 0.8 ± 0.4 mm for the fully- automatic version. The accuracy and the sensitivity of the algorithm to area measurements were (94.3 ± 4.2)% and (92.1 ± 3.6)% for the semi-automatic version, respectively and (92.9 ± 6.9)% and (91.2 ± 5.1)% for the fully-automatic version, respectively

Magnetic resonance imaging for localization of prostate cancer in the setting of biochemical recurrence

The clinical suspicion of local recurrence of prostate cancer after radical treatment is based on the onset of biochemical failure. The use of multiparametric magnetic resonance imaging (MRI) for prostate cancer has increased over recent years, mainly for detection, staging, and active surveillance. However, suspicion of recurrence in the set of biochemical failure is becoming a significant reason for clinicians to request multiparametric MRI. Radiologists should be able to recognize the normal posttreatment MRI findings. Fibrosis and atrophic remnant seminal vesicles (SV) after radical prostatectomy are often found and must be differentiated from local relapse. Moreover, brachytherapy, external beam radiotherapy, and focal therapies tend to diffusely decrease the signal intensity of the peripheral zone on T2-weighted images due to the loss of water content, consequently mimicking tumor and hemorrhage. The combination of T2-weighted images and functional studies like diffusion-weighted imaging and dynamic contrast-enhanced imaging improves the identification of local relapse. Tumor recurrence tends to restrict on diffusion images and avidly enhances after contrast administration. The authors provide a review of the normal findings and the signs of local tumor relapse after radical prostatectomy, external beam radiotherapy, brachytherapy and focal therapies

Update on the ICUD-SIU consultation on multi-parametric magnetic resonance imaging in localised prostate cancer

Introduction: Prostate cancer (PCa) imaging is a rapidly evolving field. Dramatic improvements in prostate MRI during the last decade will probably change the accuracy of diagnosis. This chapter reviews recent current evidence about MRI diagnostic performance and impact on PCa management. Materials and methods: The International Consultation on Urological Diseases nominated a committee to review the literature on prostate MRI. A search of the PubMed database was conducted to identify articles focussed on MP-MRI detection and staging protocols, reporting and scoring systems, the role of MP-MRI in diagnosing PCa prior to biopsy, in active surveillance, in focal therapy and in detecting local recurrence after treatment. Results: Differences in opinion were reported in the use of the strength of magnets [1.5 Tesla (T) vs. 3T] and coils. More agreement was found regarding the choice of pulse sequences; diffusion-weighted MRI (DW-MRI), dynamic contrast-enhanced MRI (DCE MRI), and/or MR spectroscopy imaging (MRSI) are recommended in addition to conventional T2-weighted anatomical sequences. In 2015, the Prostate Imaging Reporting and Data System (PI-RADS version 2) was described to standardize image acquisition and interpretation. MP-MRI improves detection of clinically significant PCa (csPCa) in the repeat biopsy setting or before the confirmatory biopsy in patients considering active surveillance. It is useful to guide focal treatment and to detect local recurrences after treatment. Its role in biopsy-naive patients or during the course of active surveillance remains debated. Conclusion: MP-MRI is increasingly used to improve detection of csPCa and for the selection of a suitable therapeutic approach