Novel Super-Resolution Approach to Time-Resolved Volumetric 4-Dimensional Magnetic Resonance Imaging With High Spatiotemporal Resolution for Multi-Breathing Cycle Motion Assessment

Li, Guang; Wei, Jie; Kadbi, Mo; Moody, Jason; Sun, August; Zhang, Shirong; Markova, Svetlana V.; Zakian, Kristen L.; Hunt, Margie A.; Deasy, Joseph O.

doi:10.1016/j.ijrobp.2017.02.016

Cited by 32 publications

(58 citation statements)

References 35 publications

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“…Despite the superior features of MRI compared to other image guiding modalities (e.g., CT or cone beam CT), it faces the persistent problem of limited spatial resolution . High spatial resolution in MRI comes at the expense of longer scanning time, reduced field of view (FOV), and reduced signal to noise ratio (SNR) . In MR‐IGRT, the resolution of MRI fundamentally determines the setup uncertainty, target delineation, and tracking accuracy.…”

Section: Introductionmentioning

confidence: 99%

MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model

et al. 2019

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Purpose Deep learning (DL)‐based super‐resolution (SR) reconstruction for magnetic resonance imaging (MRI) has recently been receiving attention due to the significant improvement in spatial resolution compared to conventional SR techniques. Challenges hindering the widespread implementation of these approaches remain, however. Low‐resolution (LR) MRIs captured in the clinic exhibit complex tissue structures obfuscated by noise that are difficult for a simple DL framework to handle. Moreover, training a robust network for a SR task requires abundant, perfectly matched pairs of LR and high‐resolution (HR) images that are often unavailable or difficult to collect. The purpose of this study is to develop a novel SR technique for MRI based on the concept of cascaded DL that allows for the reconstruction of high‐quality SR images in the presence of insufficient training data, an unknown translation model, and noise. Methods The proposed framework, based on the concept named cascaded deep learning, consists of three components: (a) a denoising autoencoder (DAE) trained using clinical LR noisy MRI scans that have been processed with a nonlocal means filter that generates denoised LR data; (b) a down‐sampling network (DSN) trained with a small amount of paired LR/HR data from volunteers that allows for the generation of perfectly paired LR/HR data for the training of a generative model; and (c) the proposed SR generative model (p‐SRG) trained with data generated by the DSN that maps from LR inputs to HR outputs. After training, LR clinical images may be fed through the DAE and p‐SRG to yield SR reconstructions of the LR input. The application of this framework was explored in two settings: 3D breath‐hold MRI axial SR reconstruction from LR axial scans (<3 sec/vol) and in the enhancement of the spatial resolution of LR 4D‐MRI acquisitions (0.5 sec/vol). Results The DSN produces LR scans from HR inputs with a higher fidelity to true, LR clinical scans compared to conventional k‐space down‐sampling methods based on the metrics of root mean square error (RMSE) and structural similarity index (SSIM). Furthermore, HR outputs generated by the p‐SRG exhibit improved scores in the peak signal‐to‐noise ratio, normalized RMSE, SSIM, and in the blind/reference‐less image spatial quality evaluator assessment compared to conventional approaches to MRI SR. Conclusions The robust, SR reconstruction method for MRI based on the novel cascaded deep learning framework is an end‐to‐end method for producing detail‐preserving SR reconstructions from noisy, LR clinical MRI scans. Fourfold enhancements in spatial resolution facilitate target delineation and motion management during radiation therapy, enabling precise MRI‐guided radiation therapy with 3D LR breath‐hold MRI and 4D‐MRI in a clinically feasible time frame.

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Section: Introductionmentioning

confidence: 99%

MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model

et al. 2019

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“…The registration log file recorded the number of iterations and associated time of DIR at each level of resolution. Additional detailed MR scanning conditions can be found in a previous report …”

Section: Methodsmentioning

confidence: 99%

“…As the a priori motion model may be limited by RC‐4DMRI image quality, motion range, and motion pattern, the reconstructed TR‐4DMRI provides an approximation to tumor motion . Recently, a super‐resolution (SR) approach was reported to reconstruct TR‐4DMRI by combining two complementary image sets with high‐spatial resolution at breath‐hold (BH) and high‐temporal resolution (3D cine) in free‐breathing (FB) using DIR, providing multi‐breath images with motion irregularities . This SR‐based method reconstructs TR‐4DMRI by deforming the BH image using low‐resolution 3D cine MR images as guidance without assuming periodic motion, allowing imaging of irregular motion.…”

Section: Introductionmentioning

confidence: 99%

“…Deformable image registration has been extensively studied in the past decade for radiation therapy applications, including patient‐specific motion modeling, organ contour propagation, radiation dose mapping, and adaptive radiation therapy . Among many algorithms, the intensity‐based demons algorithm is useful for single‐modality registration, such as TR‐4DMRI reconstruction . The demons‐based DIR has been widely implemented in programs, such as MATLAB ® (Mathwork, Inc.), ITK (Insight Segmentation and Registration Toolkits), and graphical processing unit based codes .…”

Section: Introductionmentioning

confidence: 99%

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Introduction of a pseudo demons force to enhance deformation range for robust reconstruction of super‐resolution time‐resolved 4DMRI

Sun

Nie

et al. 2018

Medical Physics

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“…In addition, time‐resolved 4DMRI over multi‐breathing cycles have also been reported, producing more than 10‐phase clinical motion data 24, 25, 26. Because four‐dimensional MRI is an emerging 4D imaging modality with great potential benefits in radiotherapy applications, it is of paramount importance to perform the preclinical evaluation, especially more imaging data, prior to clinical use in radiotherapy planning.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of automatic contour propagation in T2‐weighted 4DMRI for normal‐tissue motion assessment using internal organ‐at‐risk volume (IRV)

Zhang

Markova

García

et al. 2018

J Applied Clin Med Phys

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PurposeThe purpose of this study was to evaluate the quality of automatically propagated contours of organs at risk (OARs) based on respiratory‐correlated navigator‐triggered four‐dimensional magnetic resonance imaging (RC‐4DMRI) for calculation of internal organ‐at‐risk volume (IRV) to account for intra‐fractional OAR motion.Methods and MaterialsT2‐weighted RC‐4DMRI images were of 10 volunteers acquired and reconstructed using an internal navigator‐echo surrogate and concurrent external bellows under an IRB‐approved protocol. Four major OARs (lungs, heart, liver, and stomach) were delineated in the 10‐phase 4DMRI. Two manual‐contour sets were delineated by two clinical personnel and two automatic‐contour sets were propagated using free‐form deformable image registration. The OAR volume variation within the 10‐phase cycle was assessed and the IRV was calculated as the union of all OAR contours. The OAR contour similarity between the navigator‐triggered and bellows‐rebinned 4DMRI was compared. A total of 2400 contours were compared to the most probable ground truth with a 95% confidence level (S95) in similarity, sensitivity, and specificity using the simultaneous truth and performance level estimation (STAPLE) algorithm.ResultsVisual inspection of automatically propagated contours finds that approximately 5–10% require manual correction. The similarity, sensitivity, and specificity between manual and automatic contours are indistinguishable (P > 0.05). The Jaccard similarity indexes are 0.92 ± 0.02 (lungs), 0.89 ± 0.03 (heart), 0.92 ± 0.02 (liver), and 0.83 ± 0.04 (stomach). Volume variations within the breathing cycle are small for the heart (2.6 ± 1.5%), liver (1.2 ± 0.6%), and stomach (2.6 ± 0.8%), whereas the IRV is much larger than the OAR volume by: 20.3 ± 8.6% (heart), 24.0 ± 8.6% (liver), and 47.6 ± 20.2% (stomach). The Jaccard index is higher in navigator‐triggered than bellows‐rebinned 4DMRI by 4% (P < 0.05), due to the higher image quality of navigator‐based 4DMRI.ConclusionAutomatic and manual OAR contours from Navigator‐triggered 4DMRI are not statistically distinguishable. The navigator‐triggered 4DMRI image provides higher contour quality than bellows‐rebinned 4DMRI. The IRVs are 20–50% larger than OAR volumes and should be considered in dose estimation.

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Novel Super-Resolution Approach to Time-Resolved Volumetric 4-Dimensional Magnetic Resonance Imaging With High Spatiotemporal Resolution for Multi-Breathing Cycle Motion Assessment

Cited by 32 publications

References 35 publications

MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model

MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model

Introduction of a pseudo demons force to enhance deformation range for robust reconstruction of super‐resolution time‐resolved 4DMRI

Evaluation of automatic contour propagation in T2‐weighted 4DMRI for normal‐tissue motion assessment using internal organ‐at‐risk volume (IRV)

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