A Preliminary Study on Quantitative Estimation of Renal R1 Parameter Map in an Animal Model of Unilateral Renal Artery Stenosis Based on Deep Learning of Triple Flip Angle 2D SPGR MRI Sequences

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Journal of Clinical Radiology ›› 2023, Vol. 42 ›› Issue (3) : 450-454.

MI Yue, ZHANG Xiaodong, WU Jingyun, et al

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Objective To investigate the feasibility of a deep learning model-based approach for quantitative estimation of renal R1 parameter map in an animal model of unilateral renal artery stenosis using triple flip-angle 2D SPGR MRI sequences. Methods A total of 12 healthy New Zealand rabbits with an average weight of 3.2 kg were enrolled for evaluation,and each rabbit was subjected to partial ligation of the left renal artery to create an animal model of unilateral renal artery stenosis (RAS),and data were collected at 10 minute intervals before and after RAS,including two time points before and nine time points after surgery,for a total of 11 2D single-layer triple flip angle SPGR MRI data at different time points to obtain R1 parameter maps of the kidney before and after alteration of renal water content levels due to unilateral renal artery stenosis.A total of 127 2D image pairs were obtained,each pair consisting of a three-channel 2D image consisting of a sequence of SPGR images with three flip angles (15°,24° and 33°) at the same slice and a conventional variable flip angle R1 estimation method to obtain R1 parametric map images at the corresponding slice.The encoder-decoder structure is used as the base architecture of the R1 parametric map generation model.Ninety-two cases of data collected after RAS surgery were divided into training dataset (74 pairs) and tuning dataset (18 pairs),the data from 34 cases collected before RAS surgery and 1 case after RAS surgery were used as the testing dataset (35 pairs).The peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) results of the test dataset were used as evaluation metrics for the R1 parameter map generation model. Results In the test dataset,the PNSR (dB) and SSIM (%) of the R1 parametric map generation model were 22.08±2.33 and 79.49±6.49,respectively. Conclusion The deep learning model is feasible for quantitative estimation of R1 parametric maps of rabbit kidney for triple flip angle SPGR MRI sequences to quantitatively assess the water content of its renal parenchyma.

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Deep learning / Artificial intelligence / Spoiled gradient recalled echo / Renal artery stenosis / R1 parameter maps

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MI Yue, ZHANG Xiaodong, WU Jingyun, et al. A Preliminary Study on Quantitative Estimation of Renal R1 Parameter Map in an Animal Model of Unilateral Renal Artery Stenosis Based on Deep Learning of Triple Flip Angle 2D SPGR MRI Sequences[J]. Journal of Clinical Radiology, 2023, 42(3): 450-454

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Received	Revised	Published
2022-04-08	2022-07-19	2023-03-20
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2024-01-16

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