Clinical evaluation of an MRI-to-ultrasound deformable image registration algorithm for prostate brachytherapy

Shaaer, Amani; Davidson, Melanie; Semple, Mark; Nicolae, Alexandru; Mendez, Lucas C.; Chung, Hans T.; Loblaw, Andrew; Tseng, Chia‐Lin; Morton, Gerard; Ravi, Ananth

doi:10.1016/j.brachy.2018.08.006

Cited by 24 publications

(27 citation statements)

References 44 publications

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“…All T2 examinations before and after moisturization were treated in the same way: manual measurements for obtaining T2 values of SC, epidermis and dermis on both exams, and thickness of SC and epidermis on skins examinations before moisturization. Registration was performed between each patient examinations (image after moisturization was registered with the first acquisition), and dice index and Hausdorff distance were obtained for both types of semiautomatic and automatic methods (only evaluating the contour of the skin layers) . The segmentation was done on each examination before and after moisturization.…”

Section: Methodsmentioning

confidence: 99%

“…Registration was performed between each patient examinations (image after moisturization was registered with the first acquisition), and dice index and Hausdorff distance were obtained for both types of semiautomatic and automatic methods (only evaluating the contour of the skin layers). 19,20 The segmentation was done on each examination before and after moisturization. Semiautomatic measurements obtained T2 values of SC, epidermis and dermis on both exams.…”

Section: Course Of the Studymentioning

confidence: 99%

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Edge detector‐based automatic segmentation of the skin layers and application to moisturization in high‐resolution 3 Tesla magnetic resonance imaging

Mesrar

Benhoumich

Miséry

et al. 2019

Skin Research and Technology

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Introduction Previous studies have demonstrated the feasibility to explore moisturization with quantification imaging based on T2 mapping. The aim of this study was to describe and validate the first robust automated method to segment the first layers of the skin. Materials and Methods Data were picked from a previous study that included 35 healthy subjects who underwent a 3T MRI (multi spin echo calculation T2‐weighted sequence) with a microscopic coil on the left heel before and one hour after moisturization. The automatic algorithm was composed of the T2 map generation, a Canny filter, a selection of boundaries, and a local regression to delimitate stratum corneum, epidermis, and dermis. An automated affine registration was applied between the exams before and after moisturization. Results The failure rate of the algorithm was below 5%. Mean computation time was 139.12s. There was a significant and strong correlation between the automatic measurements and the manual ones for the T2 values (ρ: 0.905, P < 0.001) and for the thickness measurements (ρ: 0.8663; P < 0.001). For registration, mean of the Dice index was 0.64 [0.47; 0.80] and of the Hausdorff distance was 0.29 mm 95% CI: [0.28; 0.30]. Conclusion The proposed automatic method to study the first skin layers in 3T MRI using micro‐coils was robust and described T2 values and thickness measurements with a strong correlation to manual measurements. The use of an automated affine registration could also permit the generation of a mapping for a visual assessment of moisturization.

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Section: Methodsmentioning

confidence: 99%

Section: Course Of the Studymentioning

confidence: 99%

Edge detector‐based automatic segmentation of the skin layers and application to moisturization in high‐resolution 3 Tesla magnetic resonance imaging

Mesrar

Benhoumich

Miséry

et al. 2019

Skin Research and Technology

View full text Add to dashboard Cite

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“…Significant recent developments in high dose-rate (HDR) brachytherapy include the use of image guidance [1], new applicators [2,3], and dose calculation algorithms [4][5][6]. These innovations have led to improved treatments that have been shown by recent studies to have excellent clinical outcomes [7,8].…”

Section: Introductionmentioning

confidence: 99%

In vivo dosimetry in brachytherapy: Requirements and future directions for research, development, and clinical practice

Fonseca

Johansen

Smith

et al. 2020

Physics and Imaging in Radiation Oncology

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Brachytherapy can deliver high doses to the target while sparing healthy tissues due to its steep dose gradient leading to excellent clinical outcome. Treatment accuracy depends on several manual steps making brachytherapy susceptible to operational mistakes. Currently, treatment delivery verification is not routinely available and has led, in some cases, to systematic errors going unnoticed for years. The brachytherapy community promoted developments in in vivo dosimetry (IVD) through research groups and small companies. Although very few of the systems have been used clinically, it was demonstrated that the likelihood of detecting deviations from the treatment plan increases significantly with time-resolved methods. Time-resolved methods could interrupt a treatment avoiding gross errors which is not possible with time-integrated dosimetry. In addition, lower experimental uncertainties can be achieved by using source-tracking instead of direct dose measurements. However, the detector position in relation to the patient anatomy remains a main source of uncertainty. The next steps towards clinical implementation will require clinical trials and systematic reporting of errors and nearmisses. It is of utmost importance for each IVD system that its sensitivity to different types of errors is well understood, so that end-users can select the most suitable method for their needs. This report aims to formulate requirements for the stakeholders (clinics, vendors, and researchers) to facilitate increased clinical use of IVD in brachytherapy. The report focuses on high dose-rate IVD in brachytherapy providing an overview and outlining the need for further development and research. ☆ During the 1st ESTRO Physics Workshop celebrated in November 2017 in Glasgow, Scotland, a task group was created to stimulate the wider adoption of in vivo dosimetry for radiotherapy. The members of this task group, authors of this report, were selected on the basis of their expertise to contribute relevant input to the area of study and their long-term experience in the clinical implementation of in vivo dosimetry systems.

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“…Some alternative, although less accurate, approaches for validation of DIL registration include assessing the accuracy of the propagation of intraprostatic anatomical landmarks using deformable-image-registration-generated MRI contours to TRUS contours as a surrogate for the DIL. These landmarks include the urethra, proximal seminal vesicles, as well as the patient-specific landmarks such as nodules, cysts, fluid pockets, and calcifications.Additionally, comparison of cognitive contouring of the DIL on TRUS images to deformable-image-registration-propagated DIL contours from the MRI can be a means of checking for gross errors in the registrations 9.…”

mentioning

confidence: 99%

MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy

McGeachy

Watt

Husain

et al. 2021

J Applied Clin Med Phys

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Purpose: High-dose-rate (HDR) prostate brachytherapy is an established technique for whole-gland treatment. For transrectal ultrasound (TRUS)-guided HDR prostate brachytherapy, image fusion with a magnetic resonance image (MRI) can be performed to make use of its soft-tissue contrast. The MIM treatment planning system has recently introduced image registration specifically for HDR prostate brachytherapy and has incorporated a Predictive Fusion workflow, which allows clinicians to attempt to compensate for differences in patient positioning between imaging modalities. In this study, we investigate the accuracy of the MIM algorithms for MRI-TRUS fusion, including the Predictive Fusion workflow.Materials and Methods: A radiation oncologist contoured the prostate gland on both TRUS and MRI. Four registration methodologies to fuse the MRI and the TRUS images were considered: rigid registration (RR), contour-based (CB) deformable registration, Predictive Fusion followed by RR (pfRR), and Predictive Fusion followed by CB deformable registration (pfCB). Registrations were compared using the mean distance to agreement and the Dice similarity coefficient for the prostate as contoured on TRUS and the registered MRI prostate contour.Results: Twenty patients treated with HDR prostate brachytherapy at our center were included in this retrospective evaluation. For the cohort, mean distance to agreement was 2.1 ± 0.8 mm, 0.60 ± 0.08 mm, 2.0 ± 0.5 mm, and 0.59 ± 0.06 mm for RR, CB, pfRR, and pfCB, respectively. Dice similarity coefficients were 0.80 ± 0.05, 0.93 ± 0.02, 0.81 ± 0.03, and 0.93 ± 0.01 for RR, CB, pfRR, and pfCB, respectively. The inclusion of the Predictive Fusion workflow did not significantly improve the quality of the registration. Conclusions:The CB deformable registration algorithm in the MIM treatment planning system yielded the best geometric registration indices. MIM offers a commercial platform allowing for easier access and integration into clinical departments with

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Clinical evaluation of an MRI-to-ultrasound deformable image registration algorithm for prostate brachytherapy

Cited by 24 publications

References 44 publications

Edge detector‐based automatic segmentation of the skin layers and application to moisturization in high‐resolution 3 Tesla magnetic resonance imaging

Edge detector‐based automatic segmentation of the skin layers and application to moisturization in high‐resolution 3 Tesla magnetic resonance imaging

In vivo dosimetry in brachytherapy: Requirements and future directions for research, development, and clinical practice

MRI‐TRUS registration methodology for TRUS‐guided HDR prostate brachytherapy

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