减少 GABA 编辑 MRS 采集时间的 2023 ISBI 挑战赛结果

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic Resonance Materials in Physics, Biology and Medicine Pub Date : 2024-04-13 DOI:10.1007/s10334-024-01156-9
Rodrigo Pommot Berto, Hanna Bugler, Gabriel Dias, Mateus Oliveira, Lucas Ueda, Sergio Dertkigil, Paula D. P. Costa, Leticia Rittner, Julian P. Merkofer, Dennis M. J. van de Sande, Sina Amirrajab, Gerhard S. Drenthen, Mitko Veta, Jacobus F. A. Jansen, Marcel Breeuwer, Ruud J. G. van Sloun, Abdul Qayyum, Cristobal Rodero, Steven Niederer, Roberto Souza, Ashley D. Harris
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引用次数: 0

摘要

目的采用会议挑战赛的形式,使用完整扫描过程中通常获取的四分之一瞬时数据,比较基于机器学习的γ-氨基丁酸(GABA)编辑磁共振波谱(MRS)重建模型:第 1 轨:模拟数据;第 2 轨:相同采集参数与体内数据;第 3 轨:不同采集参数与体内数据。使用均方误差、信噪比、线宽和提议的形状评分指标来量化模型性能。挑战赛组织者提供了基线模型、模拟无噪声数据、添加合成噪声指南和体内数据的开放访问权限。第 1 赛道的协方差矩阵卷积神经网络模型最为成功。在频谱图数据表示上运行的视觉转换器模型在轨道 2 和轨道 3 上最为成功。与传统的 320 个瞬态重构相比,使用 80 个瞬态的深度学习(DL)重构在信噪比、线宽和拟合误差方面取得了相同或更好的效果。结论基于深度学习的重建管道有望减少 GABA 编辑 MRS 所需的瞬态数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time

Purpose

Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan.

Methods

There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data.

Results

Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics.

Conclusion

DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

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来源期刊
CiteScore
4.60
自引率
0.00%
发文量
58
审稿时长
>12 weeks
期刊介绍: MAGMA is a multidisciplinary international journal devoted to the publication of articles on all aspects of magnetic resonance techniques and their applications in medicine and biology. MAGMA currently publishes research papers, reviews, letters to the editor, and commentaries, six times a year. The subject areas covered by MAGMA include: advances in materials, hardware and software in magnetic resonance technology, new developments and results in research and practical applications of magnetic resonance imaging and spectroscopy related to biology and medicine, study of animal models and intact cells using magnetic resonance, reports of clinical trials on humans and clinical validation of magnetic resonance protocols.
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