Evaluation of super-resolution on 50 pancreatic cancer patients with real-time cine MRI from 0.35T MRgRT

Chun, Jaehee; Lewis, Benjamin; Ji, Zhen; Shin, Ju Hye; Park, Justin C.; Kim, Jin Sung; Kim, Sang Woo

doi:10.1088/2057-1976/ac1c51

Cited by 7 publications

(6 citation statements)

References 26 publications

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“…These metrics are correlated to the naturalness, edge sharpness, presence of distortion, and noise level, and have often been used to evaluate medical image quality. 31,35,[45][46][47] All scores predicted by three metrics range from 0 to 100, lower scores indicating higher perceptual quality. Two reference-based evaluators, structural similarity index measure (SSIM) 48 and peak signal-to-noise ratio (PSNR) scores were also used for quantitative assessment.…”

Section: Image Qualitymentioning

confidence: 99%

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI‐guided radiation therapy

Dong,

Yang,

Wen

et al. 2023

Medical Physics

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BackgroundCine magnetic resonance (MR) images have been used for real‐time MR guided radiation therapy (MRgRT). However, the onboard MR systems with low‐field strength face the problem of limited image quality.PurposeTo improve the quality of cine MR images in MRgRT using prior image information provided by the patient planning and positioning MR images.MethodsThis study employed MR images from 18 pancreatic cancer patients who received MR‐guided stereotactic body radiation therapy. Planning 3D MR images were acquired during the patient simulation, and positioning 3D MR images and 2D sagittal cine MR images were acquired before and during the beam delivery, respectively. A deep learning‐based framework consisting of two cycle generative adversarial networks (CycleGAN), Denoising CycleGAN and Enhancement CycleGAN, was developed to establish the mapping between the 3D and 2D MR images. The Denoising CycleGAN was trained to first denoise the cine images using the time domain cine image series, and the Enhancement CycleGAN was trained to enhance the spatial resolution and contrast by taking advantage of the prior image information from the planning and positioning images. The denoising performance was assessed by signal‐to‐noise ratio (SNR), structural similarity index measure, peak SNR, blind/reference‐less image spatial quality evaluator (BRISQUE), natural image quality evaluator, and perception‐based image quality evaluator scores. The quality enhancement performance was assessed by the BRISQUE and physician visual scores. In addition, the target contouring was evaluated on the original and processed images.ResultsSignificant differences were found for all evaluation metrics after Denoising CycleGAN processing. The BRISQUE and visual scores were also significantly improved after sequential Denoising and Enhancement CycleGAN processing. In target contouring evaluation, Dice similarity coefficient, centroid distance, Hausdorff distance, and average surface distance values were significantly improved on the enhanced images. The whole processing time was within 20 ms for a typical input image size of 512 × 512.ConclusionTaking advantage of the prior high‐quality positioning and planning MR images, the deep learning‐based framework enhanced the cine MR image quality significantly, leading to improved accuracy in automatic target contouring. With the merits of both high computational efficiency and considerable image quality enhancement, the proposed method may hold important clinical implication for real‐time MRgRT.

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Section: Image Qualitymentioning

confidence: 99%

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI‐guided radiation therapy

Dong,

Yang,

Wen

et al. 2023

Medical Physics

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“…9,10 For the current commercially available magnetic resonance linear accelerators (MR-Linac)s, 2,5 intra-fractional motion monitoring is facilitated by 2D+t cine MR imaging. 5 In the past few years, evaluation of cine-MR has been focused on the spatial/temporal resolution, 11 geometric distortion 12,13 and artifacts. 14 In contrast to static imaging, cine-MR depicts moving structures, and this involves a motion-dependent sampling of the raw image data during acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…For the current commercially available magnetic resonance linear accelerators (MR‐Linac)s, 2,5 intra‐fractional motion monitoring is facilitated by 2D+t cine MR imaging 5 . In the past few years, evaluation of cine‐MR has been focused on the spatial/temporal resolution, 11 geometric distortion 12,13 and artifacts 14 …”

Section: Introductionmentioning

confidence: 99%

Intra‐frame motion deterioration effects and deep‐learning‐based compensation in MR‐guided radiotherapy

Sui,

Palaniappan,

Brenner

et al. 2023

Medical Physics

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BackgroundCurrent commercially available hybrid magnetic resonance linear accelerators (MR‐Linac) use 2D+t cine MR imaging to provide intra‐fractional motion monitoring. However, given the limited temporal resolution of cine MR imaging, target intra‐frame motion deterioration effects, resulting in effective time latency and motion artifacts in the image domain, can be appreciable, especially in the case of fast breathing.PurposeThe aim of this work is to investigate intra‐frame motion deterioration effects in MR‐guided radiotherapy (MRgRT) by simulating the motion‐corrupted image acquisition, and to explore the feasibility of deep‐learning‐based compensation approaches, relying on the intra‐frame motion information which is spatially and temporally encoded in the raw data (k‐space).MethodsAn intra‐frame motion model was defined to simulate motion‐corrupted MR images, with 4D anthropomorphic digital phantoms being exploited to provide ground truth 2D+t cine MR sequences. A total number of 10 digital phantoms were generated for lung cancer patients, with randomly selected eight patients for training or validation and the remaining two for testing. The simulation code served as the data generator, and a dedicated motion pattern perturbation scheme was proposed to build the intra‐frame motion database, where three degrees of freedom were designed to guarantee the diversity of intra‐frame motion trajectories, enabling a thorough exploration in the domain of the potential anatomical structure positions. U‐Nets with three types of loss functions: L1 or L2 loss defined in image or Fourier domain, referred to as NNImgLoss‐L1, NNFloss‐L1 and NNL2‐Loss were trained to extract information from the motion‐corrupted image and used to estimate the ground truth final‐position image, corresponding to the end of the acquisition. Images before and after compensation were evaluated in terms of (i) image mean‐squared error (MSE) and mean absolute error (MAE), and (ii) accuracy of gross tumor volume (GTV) contouring, based on optical‐flow image registration.ResultsImage degradation caused by intra‐frame motion was observed: for a linearly and fully acquired Cartesian readout k‐space trajectory, intra‐frame motion resulted in an imaging latency of approximately 50% of the acquisition time; in comparison, the motion artifacts exhibited only a negligible contribution to the overall geometric errors. All three compensation models led to a decrease in image MSE/MAE and GTV position offset compared to the motion‐corrupted image. In the investigated testing dataset for GTV contouring, the average dice similarity coefficients (DSC) improved from 88% to 96%, and the 95th percentile Hausdorff distance (HD95) dropped from 4.8 mm to 2.1 mm. Different models showed slight performance variations across different intra‐frame motion amplitude categories: NNImgLoss‐L1 excelled for small/medium amplitudes, whereas NNFloss‐L1 demonstrated higher DSC median values at larger amplitudes. The saliency maps of the motion‐corrupted image highlighted the major contribution of the later acquired k‐space data, as well as the edges of the moving anatomical structures at their final positions, during the model inference stage.ConclusionsOur results demonstrate the deep‐learning‐based approaches have the potential to compensate for intra‐frame motion by utilizing the later acquired data to drive the convergence of the earlier acquired k‐space components.

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“…In oncology, MRI-guided radiotherapy improved by super resolution with a machine learning framework improved the tumor contour quality without significant acquisition or post-processing delay. 6 Also, in nodules affecting the lung, the proposed multiview 2D network is an effective algorithm for detecting nodules that are isolated, linked to a vessel, or attached to the lung wall. 7 Although neural networks provide a better resolution, there is a risk of introducing non-existing pathology and removing existing pathophysiological changes.…”

mentioning

confidence: 99%

“…There are some very promising directions of research for super‐resolution imaging. In oncology, MRI‐guided radiotherapy improved by super resolution with a machine learning framework improved the tumor contour quality without significant acquisition or post‐processing delay 6 . Also, in nodules affecting the lung, the proposed multiview 2D network is an effective algorithm for detecting nodules that are isolated, linked to a vessel, or attached to the lung wall 7 …”

mentioning

confidence: 99%

Editorial for “Super‐Resolution Cine Image Enhancement for Fetal Cardiovascular Magnetic Resonance Imaging”

Gaga¹

2021

Magnetic Resonance Imaging

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Evaluation of super-resolution on 50 pancreatic cancer patients with real-time cine MRI from 0.35T MRgRT

Cited by 7 publications

References 26 publications

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI‐guided radiation therapy

Improvement of 2D cine image quality using 3D priors and cycle generative adversarial network for low field MRI‐guided radiation therapy

Intra‐frame motion deterioration effects and deep‐learning‐based compensation in MR‐guided radiotherapy

Editorial for “Super‐Resolution Cine Image Enhancement for Fetal Cardiovascular Magnetic Resonance Imaging”

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