Validation of a deformable <scp>MRI</scp> to <scp>CT</scp> registration algorithm employing same day planning <scp>MRI</scp> for surrogate analysis

Padgett, Kyle R.; Stoyanova, Radka; Pirozzi, Sara; Johnson, Perry B.; Piper, Jon; Dogan, Nesrin; Pollack, Alan

doi:10.1002/acm2.12296

Cited by 10 publications

(4 citation statements)

References 19 publications

(30 reference statements)

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“…In future, we will train PxCGAN with an increased number of our CT-MRI datasets. Another limitation is that the ground truth in image quality and geometric assessment for sMRI can have inaccuracies from a rigid image registration between CT and T2-weighted MRI as performed in the clinic (Hamdan et al 2017, Padgett et al 2018, Tyyger et al 2020. Even though the anatomical changes between the two scans are as small as possible, the bladder-filling difference might have confused the training of PxCGAN.…”

Section: Discussionmentioning

confidence: 99%

Prostate segmentation accuracy using synthetic MRI for high-dose-rate prostate brachytherapy treatment planning

et al. 2023

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Objective: Both CT and MRI images are acquired for HDR prostate brachytherapy patients at our institution. CT is used to identify catheters and MRI is used to segment the prostate. To address scenarios of limited MRI access, we developed a novel Generative Adversarial Network (GAN) to generate synthetic MRI (sMRI) from CT with sufficient soft-tissue contrast to provide accurate prostate segmentation without MRI (rMRI).Approach: Our hybrid GAN, PxCGAN was trained utilizing 58 paired CT-MRI datasets from our HDR prostate patients. Using 20 independent CT-MRI datasets, the image quality of sMRI was tested using MAE, MSE, PSNR, and SSIM. These metrics were compared with the metrics of sMRI generated using Pix2Pix and CycleGAN. The accuracy of prostate segmentation on sMRI was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD) on the prostate delineated by three radiation oncologists (RO) on sMRI vs. rMRI. To estimate inter-observer variability (IOV), these metrics between prostate contours delineated by each RO on rMRI and the prostate delineated by treating RO on rMRI ("gold standard") were calculated.Main results: Qualitatively, sMRI images show enhanced soft-tissue contrast at the prostate boundary compared with CT scans. For MAE and MSE, PxCGAN and CycleGAN have similar results, while the MAE of PxCGAN is smaller than that of Pix2Pix. PSNR and SSIM of PxCGAN are significantly higher than Pix2Pix and CycleGAN (p<0.01). The DSC for sMRI vs. rMRI is within the range of the IOV, while HD for sMRI vs. rMRI is smaller than the HD for IOV for all ROs (p≤0.03).Significance: PxCGAN generates sMRI images from the treatment planning CT scans which depicts enhanced soft-tissue contrast at the prostate boundary. The accuracy of prostate segmentation on sMRI compared to rMRI is within the segmentation variation on rMRI between different radiation oncologists.

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Section: Discussionmentioning

confidence: 99%

Prostate segmentation accuracy using synthetic MRI for high-dose-rate prostate brachytherapy treatment planning

et al. 2023

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“…However, for practical clinic study, different MRI scans of the same patient often have mismatches at different time and/or with different scan settings. Thus, rigid and deformable registration [ 46 , 47 ] will be needed. Secondly, for MRI images, there is no correction for geometric artifacts.…”

Section: Discussionmentioning

confidence: 99%

Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net

Liu

Song

2022

Int. J. Mach. Learn. & Cyber.

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Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-of-interest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

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“…Differences in signal characteristics and spatial intensity distribution of different image modalities are the main problem of registration of multimodal images (MRI to CT, etc.). As these metrics are often used in the registration optimization process, uncertainties increase when the organ deformation is added to inherent difficulties of the process [ 25 , 26 ]. Kirby et al .…”

Section: Discussionmentioning

confidence: 99%

Automatic contouring using deformable image registration for tandem-ring or tandem-ovoid brachytherapy

Yedekci¹,

Gültekin²,

Sarı³

et al. 2022

jcb

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Purpose: To investigate the effectiveness of deformable image registration (DIR)-based automatic contouring for tandem-ring (T-R) or tandem-ovoid (T-O) 3-dimensional computed tomography (CT)-based image-guided brachytherapy (IGBT).Material and methods: CT images of 28 patients with intact cervical cancer were retrospectively analyzed. Selected group had T-R or T-O insertion for IGBT. Hybrid DIR was performed between first fraction CT and subsequent CTs for IGBT. First IGBT CT images were reference images. All DIRs were performed based on these first IGBT CT scans. Contour similarities between manual and automated segmentations were evaluated with dice similarity coefficient (DSC) score. Mean volumes of the structures were delineated manually and automatically compared. Finally, dosimetric comparisons were performed in order to obtain how contour differences affect the doses to target and organs at risk (OARs).Results: In general, mean volumes of the automatic contours were larger than manual contours for both T-R and T-O insertions. However, the difference in volume was statistically significant for the small bowel only (p < 0.05 and p < 0.01 for T-R and T-O, respectively). The DSC scores were small for the small bowel and the sigmoid in both applicator sets. When the two different applicator sets were compared, the performance of DIR-based contour propagation for the rectum was worse in T-O compared to T-R application. Dosimetric comparisons showed that volume differences between the manual and propagated contours did not affect dose-volume parameters. The treatment plans based on manually contoured targets also well-covered DIR contours. The average time for DIR was 2.0 ±0.1 minutes per fraction compared to 14.0 ±0.4 minutes in manual contouring (p < 0.001).Conclusions: DIR-based automatic contouring of the structures seems successful for both the T-R and T-O applications in cervical IGBT. DIR significantly decreased the time for contouring. Our results indicate that automatic contouring in IGBT is safe and time-saving.

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Validation of a deformable MRI to CT registration algorithm employing same day planning MRI for surrogate analysis

Cited by 10 publications

References 19 publications

Prostate segmentation accuracy using synthetic MRI for high-dose-rate prostate brachytherapy treatment planning

Prostate segmentation accuracy using synthetic MRI for high-dose-rate prostate brachytherapy treatment planning

Brain tumor segmentation based on region of interest-aided localization and segmentation U-Net

Automatic contouring using deformable image registration for tandem-ring or tandem-ovoid brachytherapy

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