gammaSTAR: A framework for the development of dynamic, real-time capable MR sequences.

IF 3 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Magnetic Resonance in Medicine Pub Date : 2025-05-20 DOI:10.1002/mrm.30573

Simon Konstandin, Matthias Günther, Daniel C Hoinkiss

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引用次数: 0

Abstract

Purpose: To present the real-time capability and advanced MR sequence library of the MR sequence development framework gammaSTAR.

Methods: The presented platform consists of four different components: (1) a frontend for sequence development combined with a Python backend for sequence generation; (2) a Lua backend for the creation of hardware instructions; (3) a vendor-specific driver for translation of these instructions into scanner-specific objects; and (4) an interface for real-time feedback capability. In vivo measurements of the same volunteer were performed for comparison of imaging and spectroscopy sequences implemented in this framework with those of one main vendor (Siemens Healthineers) at magnetic field strengths of 3 T and 1.5 T. Prospective motion correction was integrated into a spin echo EPI sequence to demonstrate the real-time feedback capability.

Results: The imaging and spectroscopy results of the gammaSTAR sequences show very similar image contrasts and qualities compared to those by the vendor. ADC maps were calculated and show values of (0.80 ± 0.14)10^-3 mm²/s in white matter. Results of pseudo-continuous spin-echo (pCASL GRASE) and 3D radial UTE imaging demonstrate the ability to run complex sequences without long sequence preparation times. Prospective motion correction is possible by means of real-time feedback and shows much fewer movement artifacts with mean voxel displacement of 1.63 mm (uncorrected) versus 0.37 mm (corrected). All images were reconstructed using the vendor's reconstruction pipeline.

Conclusion: The platform gammaSTAR allows for MR sequence development with real-time feedback capability demonstrated by a large number of MR sequences and applications.

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gammaSTAR：一个开发动态、实时MR序列的框架。

目的：介绍磁共振序列开发框架gammaSTAR的实时性和先进的磁共振序列库。方法：呈现的平台由四个不同的组件组成：(1)用于序列开发的前端与用于序列生成的Python后端相结合；(2)用于创建硬件指令的Lua后端；(3)将这些指令转换为扫描仪特定对象的特定供应商驱动程序；(4)实时反馈接口。对同一志愿者进行体内测量，以比较在该框架下实施的成像和光谱序列与一个主要供应商（Siemens Healthineers）在磁场强度为3 T和1.5 T时的成像和光谱序列，并将预期运动校正集成到自旋回波EPI序列中，以展示实时反馈能力。结果：与供应商相比，gammaSTAR序列的成像和光谱结果显示非常相似的图像对比度和质量。计算ADC图，显示白质值为（0.80±0.14）10-3 mm2/s。伪连续自旋回波（pCASL GRASE）和三维径向UTE成像的结果表明，无需长时间的序列准备就可以运行复杂的序列。通过实时反馈，可以实现前瞻性的运动校正，并且显示出更少的运动伪影，平均体素位移为1.63 mm（未校正），而不是0.37 mm（校正）。所有图像都使用供应商的重建管道进行重建。结论：gammaSTAR平台允许磁共振序列开发，具有大量磁共振序列和应用证明的实时反馈能力。

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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来源期刊

Magnetic Resonance in Medicine 医学-核医学

CiteScore

6.70

自引率

24.20%

发文量

376

审稿时长

2-4 weeks

期刊介绍： Magnetic Resonance in Medicine (Magn Reson Med) is an international journal devoted to the publication of original investigations concerned with all aspects of the development and use of nuclear magnetic resonance and electron paramagnetic resonance techniques for medical applications. Reports of original investigations in the areas of mathematics, computing, engineering, physics, biophysics, chemistry, biochemistry, and physiology directly relevant to magnetic resonance will be accepted, as well as methodology-oriented clinical studies.