Transfer learning-based synthetic CT generation for MR-only proton therapy planning in children with pelvic sarcomas

Wang, Chuang; Uh, Jinsoo; He, Xiuxiu; Hua, Chia‐Ho; Acharya, Sahaja

doi:10.1117/12.2579767

Cited by 3 publications

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“…Efforts have been made to derive synthetic CT (sCT) in lieu of a real CT (rCT)for MR-only proton therapy planning. [3][4][5][6][7][8][9] Recent breakthroughs largely stem from deep learning, specifically generative adversarial networks (GANs), [9][10][11][12][13][14][15] which expeditiously transform adult MRI to CT and vice versa. An MR-only proton therapy planning workflow would not only eliminate the cost, time, and radiation exposure associated with CT acquisition, but also eliminate uncertainties involved in MR-to-CT registration, and enable time-efficient adaptive proton therapy.…”

Section: Introductionmentioning

confidence: 99%

“…Proton stopping power cannot be measured directly with MRI, hence the necessity of CT scans. Efforts have been made to derive synthetic CT (sCT) in lieu of a real CT (rCT)for MR‐only proton therapy planning 3–9 . Recent breakthroughs largely stem from deep learning, specifically generative adversarial networks (GANs), 9–15 which expeditiously transform adult MRI to CT and vice versa.…”

Section: Introductionmentioning

confidence: 99%

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Toward MR‐only proton therapy planning for pediatric brain tumors: Synthesis of relative proton stopping power images with multiple sequence MRI and development of an online quality assurance tool

Wang

Patni

et al. 2022

Medical Physics

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Purpose To generate synthetic relative proton stopping power (sRPSP) images from magnetic resonance imaging (MRI) sequence(s) and develop an online quality assurance (QA) tool for sRPSP to facilitate safe integration of magnetic resonance (MR)‐only proton planning into clinical practice. Materials and methods Planning computed tomography (CT) and MR images of 195 pediatric brain tumor patients were utilized (training: 150, testing: 45). Seventeen consistent‐cycle generative adversarial network (ccGAN) models were trained separately using paired CT‐converted RPSP and MRI datasets to transform a subject's MRI into sRPSP. T1‐weighted (T1W), T2‐weighted (T2W), and FLAIR MRI were permutated to form 17 combinations, with or without preprocessing, for determining the optimal training sequence(s). For evaluation, sRPSP images were converted to synthetic CT (sCT) and compared to the real CT in terms of mean absolute error (MAE) in Hounsfield units (HU). For QA, sCT was deformed and compared to a reference template built from training dataset to produce a flag map, highlighting pixels that deviate by >100 HU and fall outside the mean ± standard deviation reference intensity. The gamma intensity analysis (10%/3 mm) of the deformed sCT against the QA template on the intensity difference was investigated as a surrogate of sCT accuracy. Results The sRPSP images generated from a single T1W or T2W sequence outperformed that generated from multi‐MRI sequences in terms of MAE (all p < 0.05). Preprocessing with N4 bias and histogram matching reduced MAE of T2W MRI‐based sCT (54 ± 21 HU vs. 42 ± 13 HU, p = 0.002). The gamma intensity analysis of sCT against the QA template was highly correlated with the MAE of sCT against the real CT in the testing cohort (r = ‐0.89 for T1W sCT; r = ‐0.93 for T2W sCT). Conclusion Accurate sRPSP images can be generated from T1W/T2W MRI for proton planning. A QA tool highlights regions of inaccuracy, flagging problematic cases unsuitable for clinical use.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward MR‐only proton therapy planning for pediatric brain tumors: Synthesis of relative proton stopping power images with multiple sequence MRI and development of an online quality assurance tool

Wang

Patni

et al. 2022

Medical Physics

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“…All identified articles were published over the last five years and the publication number increased every year with the highest number in 2022 [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ]. This increasing trend was in line with the one in radiology [ 1 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ,…”

Section: Resultsmentioning

confidence: 99%

“…All low-quality ones were conference papers ( n = 12) [ 79 , 80 , 81 , 83 , 85 , 86 , 91 , 93 , 94 , 95 , 104 , 108 ]. The GAN was commonly applied to MRI ( n = 18) [ 77 , 78 , 83 , 84 , 87 , 90 , 97 , 101 , 103 , 104 , 105 , 106 , 108 , 109 , 110 , 111 , 112 , 113 ] and X-ray ( n = 13) [ 79 , 80 , 89 , 91 , 92 , 94 , 95 , 96 , 98 , 99 , 100 , 102 , 107 ], and the others included computed tomography (CT) ( n = 4) [ 82 , 86 , 93 , 97 ], ultrasound ( n = 2) [ 85 , 88 ] and positron emission tomography (PET) ( n = 1) [ 81 ]. Although the basic GAN architecture was still popular among the included studies ( n = 11) [ 77 …”

Section: Resultsmentioning

confidence: 99%

“…The GAN was commonly applied to MRI ( n = 18) [ 77 , 78 , 83 , 84 , 87 , 90 , 97 , 101 , 103 , 104 , 105 , 106 , 108 , 109 , 110 , 111 , 112 , 113 ] and X-ray ( n = 13) [ 79 , 80 , 89 , 91 , 92 , 94 , 95 , 96 , 98 , 99 , 100 , 102 , 107 ], and the others included computed tomography (CT) ( n = 4) [ 82 , 86 , 93 , 97 ], ultrasound ( n = 2) [ 85 , 88 ] and positron emission tomography (PET) ( n = 1) [ 81 ]. Although the basic GAN architecture was still popular among the included studies ( n = 11) [ 77 , 78 , 80 , 82 , 83 , 84 , 89 , 94 , 97 , 99 , 106 ], its variant, cycle-consistent GAN (CycleGAN), was the second most common ( n = 10) [ 101 ,…”

Section: Resultsmentioning

confidence: 99%

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Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

2023

Children

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Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

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Artificial Intelligence-Based Computer Vision on Medical Images

M.,

Zahraddeen Yakuu

2024

Applications of Parallel Data Processing for Biomedical Imaging

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The trajectory in medical imaging is characterized by a growing volume of data that is increasingly considered an inherent demand across various medical disciplines. With advancements in computer technology has become swifter, resulting in a surge of data for physicians to analyze. Artificial Intelligence (AI) falls within the realm of computer science. Its primary goal is to create machines capable of cognitive functions such as perception, learning, and problem-solvingthat surpass human capabilities. AI encompasses the spectrum of computer technology that displays characteristics reminiscent of human intelligence. Computer vision applied to medical images is an influential domain that utilizes various methods, including image processing and deep learning, to analyze, interpret, and extract valuable insights from medical imagery.

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Transfer learning-based synthetic CT generation for MR-only proton therapy planning in children with pelvic sarcomas

Cited by 3 publications

References 0 publications

Toward MR‐only proton therapy planning for pediatric brain tumors: Synthesis of relative proton stopping power images with multiple sequence MRI and development of an online quality assurance tool

Toward MR‐only proton therapy planning for pediatric brain tumors: Synthesis of relative proton stopping power images with multiple sequence MRI and development of an online quality assurance tool

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

Artificial Intelligence-Based Computer Vision on Medical Images

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