Dataset Information

Prostate volume analysis in image registration for prostate cancer care: a verification study.

ABSTRACT: Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urological prostate cancer care. Paired whole-pelvis MR and CT scan data were used (n = 20). A manual prostate CTV contour was performed independently on each patients MR and CT image. A semi-automated rigid-, automated rigid- and automated non-rigid registration technique was applied to align the MR and CT data. Dice Similarity Index (DSI), 95% Hausdorff distance (95%HD) and average surface distance (ASD) measures were used to assess the closeness of the manual and registered contours. The automated non-rigid approach had a significantly improved performance compared to the automated rigid- and semi-automated rigid-registration, having better average scores and decreased spread for the DSI, 95%HD and ASD (all p < 0.001). Additionally, the automated rigid approach had similar significantly improved performance compared to the semi-automated rigid registration across all accuracy metrics observed (all p < 0.001). Overall, all registration techniques studied here demonstrated sufficient accuracy for exploring their clinical use. While the fully automated non-rigid registration algorithm in the present study provided the most accurate registration, the semi-automated rigid registration is a quick, feasible, and accessible method to perform image registration for prostate cancer care by urologists and radiation oncologists now.

SUBMITTER: Bugeja JM

PROVIDER: S-EPMC10703743 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prostate volume analysis in image registration for prostate cancer care: a verification study.

Bugeja Jessica M JM Mehawed Georges G Roberts Matthew J MJ Rukin Nicholas N Dowling Jason J Murray Rebecca R

Physical and engineering sciences in medicine 20231011 4

Combined magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) may enhance diagnosis, aid surgical planning and intra-operative orientation for prostate biopsy and radical prostatectomy. Although PET-MRI may provide these benefits, PET-MRI machines are not widely available. Image fusion of Prostate specific membrane antigen PET/CT and MRI acquired separately may be a suitable clinical alternative. This study compares CT-MR registration algorithms for urol ...[more]

PMID: 37819450

Similar Datasets

Project description:ImportanceMagnetic resonance imaging (MRI) guidance improves the accuracy of prostate biopsy for the detection of clinically significant prostate cancer, but the optimal use of such guidance is not yet clear.ObjectiveTo determine the cancer detection rate (CDR) of targeting MRI-visible lesions vs systematic prostate sampling in the diagnosis of clinically significant prostate cancer in men who were biopsy naive.Design, setting, and participantsThis paired cohort trial, known as the Prospective Assessment of Image Registration in the Diagnosis of Prostate Cancer (PAIREDCAP) study, was conducted in an academic medical center from January 2015 to April 2018. Men undergoing first-time prostate biopsy were enrolled. Paired-cohort participants were a consecutive series of men with MRI-visible lesions (defined by a Prostate Imaging Reporting & Data System version 2 score ≥ 3), who each underwent 3 biopsy methods at the same sitting: first, a systematic biopsy; second, an MRI-lesion biopsy targeted by cognitive fusion; and third, an MRI-lesion targeted by software fusion. Another consecutive series of men without MRI-visible lesions underwent systematic biopsies to help determine the false-negative rate of MRI during the trial period.Main outcomes and measuresThe primary end point was the detection rate of clinically significant prostate cancer (Gleason grade group ≥2) overall and by each biopsy method separately. The secondary end points were the effects of the Prostate Imaging Reporting & Data System version 2 grade, prostate-specific antigen density, and prostate volume on the primary end point. Tertiary end points were the false-negative rate of MRI and concordance of biopsy-method results by location of detected cancers within the prostate.ResultsA total of 300 men participated; 248 had MRI-visible lesions (mean [SD] age, 65.5 [7.7] years; 197 were white [79.4%]), and 52 were control participants (mean [SD] age, 63.6 [5.9] years; 39 were white [75%]). The overall CDR was 70% in the paired cohort group, achieved by combining systematic and targeted biopsy results. The CDR by systematic sampling was 15% in the group without MRI-visible lesions. In the paired-cohort group, CDRs varied from 47% (116 of 248 men) when using cognitive fusion biopsy alone, to approximately 60% when using systematic biopsy (149 of 248 men) or either fusion method alone (154 of 248 men), to 70% (174 of 248 men) when combining systematic and targeted biopsy. Discordance of tumor locations suggests that the different biopsy methods detect different tumors. Thus, combining targeting and systematic sampling provide greatest sensitivity for detection of clinically significant prostate cancer. For all biopsy methods, the Prostate Imaging Reporting & Data System version 2 grade and prostate-specific antigen density were directly associated with CDRs, and prostate volume was inversely associated.Conclusions and relevanceAn MRI-visible lesion in men undergoing first-time prostate biopsy identifies those with a heightened risk of clinically significant prostate cancer. Combining targeted and systematic biopsy offers the best chances of detecting the cancer.

Project description:In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image.The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a multiple linear regression (MLR) model, i.e., ridge regression (RR) model, which has the best prediction accuracy than other MLR models such as canonical correlation analysis (CCA) and principal component regression (PCR).To demonstrate the performance of the proposed method, we first evaluate its registration accuracy by comparing the deformation field predicted by our method with the deformation field estimated by the thin plate spline (TPS) based correspondence interpolation method on 306 serial prostate CT images of 24 patients. The average predictive error on the voxels around 5 mm of prostate boundary is 0.38 mm for our method of RR-based correlation model. Also, the corresponding maximum error is 2.89 mm. We then compare the speed for deformation interpolation by different methods. When considering the larger region of interest (ROI) with the size of 512 × 512 × 61, our method takes 24.41 seconds to interpolate the dense deformation field while TPS method needs 6.7 minutes; when considering a small ROI (surrounding prostate) with size of 112 × 110 × 93, our method takes 1.80 seconds, while TPS method needs 25 seconds.Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy, compared to the TPS-based correspondence (or deformation) interpolation approach.

Dataset Information

Prostate volume analysis in image registration for prostate cancer care: a verification study.

Publications

Prostate volume analysis in image registration for prostate cancer care: a verification study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets