Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach

Li, Fan; Qian, Pengjiang; Su, Kuan-Hao; Baydoun, Atallah; Leisser, Asha; Hedent, Steven Van; Kuo, Jung‐Wen; Zhao, Kun; Parikh, Parag J.; Lu, Yutong; Traughber, Bryan; Muzic, Raymond F.

doi:10.1016/j.artmed.2018.07.001

Cited by 61 publications

(40 citation statements)

References 24 publications

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“…In online ART, the treatment plan is re‐optimized according to anatomical or positional changes in OARs on a daily basis, significantly improving the accuracy of radiation dose delivery and a substantial reduction in the irradiated volume of normal tissue. In this way, online ART facilitates local dose escalation and the reduction of normal tissue toxicities through real‐time, risk‐adaptive, and high‐precision radiotherapy …”

Section: Introductionmentioning

confidence: 99%

“…In this way, online ART facilitates local dose escalation and the reduction of normal tissue toxicities through real-time, risk-adaptive, and high-precision radiotherapy. [5][6][7][8] 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. [9][10][11][12] 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).…”

Section: Introductionmentioning

confidence: 99%

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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%

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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“…Nowadays, most contour research only uses geometric indices to evaluate the acceptability of contouring results. With the aim of using an objective evaluation method (for geometric indices), it is generally believed that a DSC value greater than 0.7 means the contouring result is clinically acceptable [24][25][26][27], while for distance-type geometric indices (HD, HD mean , HD95), smaller values mean that the contouring result is clinically acceptable, and the closer the value is to zero, the better the contouring result is. However, this statement is ambiguous.…”

Section: Discussionmentioning

confidence: 99%

“…However, this statement is ambiguous. The article about the study of automatic segmentation of OARs showed that an (Liver) HD = 15.770 ± 1.0 mm had a high accuracy [27], while another article indicated that an HD = 37.7 ± 13.8 mm also showed a high accuracy [28].…”

Section: Discussionmentioning

confidence: 99%

Clinically oriented contour evaluation using geometric and dosimetric indices based on geometric transformation

Xian

Xiao

et al. 2020

Preprint

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Background : In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring; However, the ability of geometric indices to identify the error of contouring results is limited, and there is lack of clinical background. Based on the reference contouring, we systematically introduced geometric errors to study the relationship between geometric and dosimetric indices and evaluated the clinical feasibility of assessing the accuracy of contouring based on geometric indices alone. Materials and Methods: A C-shaped target, organ at risk (Core), and intensity-modulated radiotherapy (IMRT) plan outlined in the American Association of Physicists in Medicine (AAPM) TG-119 report (The report of Task Group 119 of the AAPM) were used as references. Translation, scaling, rotation (except for the Core), and sine function transformation were performed to simulate the contouring results. The corresponding dosimetric indices were obtained from the original dose distribution of the radiotherapy plan, and correlations (R²) between geometric indices and dosimetric indices were quantified through linear regression. The clinical applicability of the threshold for geometric indices was analyzed by combining the geometric indices and dose difference diagram. Results: The correlations between the geometric and dosimetric indices were different and inconsistent for the contouring of the target and Core after the geometric transformation simulation. Except for the sine function transformation (R²: 0.04–0.023, p > 0.05), the other three geometric indices of the planning target volume (PTV) had strong correlations with the dosimetric indices D98% and D mean (R²: 0.689–0.988), 80% of which were strongly correlated with a p < 0.001. The correlation results for the other geometric transformations in the Core were similar to those in the PTV except for the down shift transformation. Conclusions: The dosimetric indices are heavily influenced by the contour differences, thus highlighting their importance in the evaluation process. Clinically, an assessment of the contour accuracy of the region of interest is not feasible based on geometric indices alone, and should be combined with dosimetric indices. Keywords: Contour evaluation, Geometric indices, Dosimetric indices, Geometric transformation simulation

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“…ML algorithms have been developed to identify patients that may benefit from adaptive re-planning for head and neck cancer and prostate cancer (103)(104)(105). ML algorithms for online adaptive magnetic resonance guided radiotherapy have been developed for patients with gastrointestinal cancers in which a daily optimized plan is generated before each treatment based on changes in anatomy seen on the magnetic resonance imaging scan (35,106). Interested readers are referred to a detailed review on the role of machine learning in adaptive radiotherapy (107).…”

Section: Adaptive Treatment Planning Supportmentioning

confidence: 99%

Artificial intelligence in radiation oncology treatment planning: a brief overview

Kiser¹,

Fuller²,

Reed³

2019

J Med Artif Intell

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Among medical specialties, radiation oncology has long been an innovator and early adopter of therapeutic technologies. This specialty is now situated in prime position to be revolutionized by advances in artificial intelligence (AI), especially machine and deep learning. AI has been investigated by radiation oncologists and physicists in both general and niche radiotherapy planning tasks and has often demonstrated performance that is indistinguishable from human experts, while substantially shortening the time required to complete these tasks. We sought to review applications of AI to domains germane to radiation oncology, namely: image segmentation, treatment plan generation and optimization, normal tissue complication probability modeling, quality assurance (QA), and adaptive re-planning. We sought likewise to consider obstacles to AI adoption in the radiotherapy clinic, now primarily political, legal, and ethical rather than technical in nature.

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Abdominal, multi-organ, auto-contouring method for online adaptive magnetic resonance guided radiotherapy: An intelligent, multi-level fusion approach

Cited by 61 publications

References 24 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

Clinically oriented contour evaluation using geometric and dosimetric indices based on geometric transformation

Artificial intelligence in radiation oncology treatment planning: a brief overview

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