Dataset Information

Improved prostate delineation in prostate HDR brachytherapy with TRUS-CT deformable registration technology: A pilot study with MRI validation.

ABSTRACT: Accurate prostate delineation is essential to ensure proper target coverage and normal-tissue sparing in prostate HDR brachytherapy. We have developed a prostate HDR brachytherapy technology that integrates intraoperative TRUS-based prostate contour into HDR treatment planning through TRUS-CT deformable registration (TCDR) to improve prostate contour accuracy. In a perspective study of 16 patients, we investigated the clinical feasibility as well as the performance of this TCDR-based HDR approach. We compared the performance of the TCDR-based approach with the conventional CT-based HDR in terms of prostate contour accuracy using MRI as the gold standard. For all patients, the average Dice prostate volume overlap was 91.1 ± 2.3% between the TCDR-based and the MRI-defined prostate volumes. In a subset of eight patients, inter and intro-observer reliability study was conducted among three experienced physicians (two radiation oncologists and one radiologist) for the TCDR-based HDR approach. Overall, a 10 to 40% improvement in prostate volume accuracy can be achieved with the TCDR-based approach as compared with the conventional CT-based prostate volumes. The TCDR-based prostate volumes match closely to the MRI-defined prostate volumes for all 3 observers (mean volume difference: 0.5 ± 7.2%, 1.8 ± 7.2%, and 3.5 ± 5.1%); while CT-based contours overestimated prostate volumes by 10.9 ± 28.7%, 13.7 ± 20.1%, and 44.7 ± 32.1%. This study has shown that the TCDR-based HDR brachytherapy is clinically feasible and can significantly improve prostate contour accuracy over the conventional CT-based prostate contour. We also demonstrated the reliability of the TCDR-based prostate delineation. This TCDR-based HDR approach has the potential to enable accurate dose planning and delivery, and potentially enhance prostate HDR treatment outcome.

SUBMITTER: Yang X

PROVIDER: S-EPMC5689894 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Improved prostate delineation in prostate HDR brachytherapy with TRUS-CT deformable registration technology: A pilot study with MRI validation.

Yang Xiaofeng X Rossi Peter J PJ Jani Ashesh B AB Mao Hui H Zhou Zhengyang Z Curran Walter J WJ Liu Tian T

Journal of applied clinical medical physics 20170101 1

Accurate prostate delineation is essential to ensure proper target coverage and normal-tissue sparing in prostate HDR brachytherapy. We have developed a prostate HDR brachytherapy technology that integrates intraoperative TRUS-based prostate contour into HDR treatment planning through TRUS-CT deformable registration (TCDR) to improve prostate contour accuracy. In a perspective study of 16 patients, we investigated the clinical feasibility as well as the performance of this TCDR-based HDR approac ...[more]

PMID: 28291925

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Project description:PurposeValidating deformable multimodality image registrations is challenging due to intrinsic differences in signal characteristics and their spatial intensity distributions. Evaluating multimodality registrations using these spatial intensity distributions is also complicated by the fact that these metrics are often employed in the registration optimization process. This work evaluates rigid and deformable image registrations of the prostate in between diagnostic-MRI and radiation treatment planning-CT by utilizing a planning-MRI after fiducial marker placement as a surrogate. The surrogate allows for the direct quantitative analysis that can be difficult in the multimodality domain.MethodsFor thirteen prostate patients, T2 images were acquired at two different time points, the first several weeks prior to planning (diagnostic-MRI) and the second on the same day as the planning-CT (planning-MRI). The diagnostic-MRI was deformed to the planning-CT utilizing a commercially available algorithm which synthesizes a deformable image registration (DIR) algorithm from local rigid registrations. The planning-MRI provided an independent surrogate for the planning-CT for assessing registration accuracy using image similarity metrics, including Pearson correlation and normalized mutual information (NMI). A local analysis was performed by looking only within the prostate, proximal seminal vesicles, penile bulb, and combined areas.ResultsThe planning-MRI provided an excellent surrogate for the planning-CT with residual error in fiducial alignment between the two datasets being submillimeter, 0.78 mm. DIR was superior to the rigid registration in 11 of 13 cases demonstrating a 27.37% improvement in NMI (P < 0.009) within a regional area surrounding the prostate and associated critical organs. Pearson correlations showed similar results, demonstrating a 13.02% improvement (P < 0.013).ConclusionBy utilizing the planning-MRI as a surrogate for the planning-CT, an independent evaluation of registration accuracy is possible. This population provides an ideal testing ground for MRI to CT DIR by obviating the need for multimodality comparisons which are inherently more challenging.

Project description: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.

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Project description:This work presents a comprehensive commissioning and workflow development process of a real-time, ultrasound (US) image-guided treatment planning system (TPS), a stepper and a US unit. To adequately benchmark the system, commissioning tasks were separated into (1) US imaging, (2) stepper mechanical, and (3) treatment planning aspects. Quality assurance US imaging measurements were performed following the AAPM TG-128 and GEC-ESTRO recommendations and consisted of benchmarking the spatial resolution, accuracy, and low-contrast detectability. Mechanical tests were first used to benchmark the electronic encoders within the stepper and were later expanded to evaluate the needle free length calculation accuracy. Needle reconstruction accuracy was rigorously evaluated at the treatment planning level. The calibration length of each probe was redundantly checked between the calculated and measured needle free length, which was found to be within 1 mm for a variety of scenarios. Needle placement relative to a reference fiducial and coincidence of imaging coordinate origins were verified to within 1 mm in both sagittal and transverse imaging planes. The source strength was also calibrated within the interstitial needle and was found to be 1.14% lower than when measured in a plastic needle. Dose calculations in the TPS and secondary dose calculation software were benchmarked against manual TG-43 calculations. Calculations among the three calculation methods agreed within 1% for all calculated points. Source positioning and dummy coincidence was tested following the recommendations of the TG-40 report. Finally, the development of the clinical workflow, checklists, and planning objectives are discussed and included within this report. The commissioning of real-time, US-guided HDR prostate systems requires careful consideration among several facets including the image quality, dosimetric, and mechanical accuracy. The TPS relies on each of these components to develop and administer a treatment plan, and as such, should be carefully examined.

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Dataset Information

Improved prostate delineation in prostate HDR brachytherapy with TRUS-CT deformable registration technology: A pilot study with MRI validation.

Publications

Improved prostate delineation in prostate HDR brachytherapy with TRUS-CT deformable registration technology: A pilot study with MRI validation.

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