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从定量 MRI 合成的 FLAIR 图像上改善病变外观:一种快速的混合方法。

Improving the lesion appearance on FLAIR images synthetized from quantitative MRI: a fast, hybrid approach.

机构信息

Computational Imaging Group for MR Diagnostics & Therapy, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.

Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.

出版信息

MAGMA. 2024 Dec;37(6):1021-1030. doi: 10.1007/s10334-024-01198-z. Epub 2024 Aug 24.

Abstract

OBJECTIVE

The image quality of synthetized FLAIR (fluid attenuated inversion recovery) images is generally inferior to its conventional counterpart, especially regarding the lesion contrast mismatch. This work aimed to improve the lesion appearance through a hybrid methodology.

MATERIALS AND METHODS

We combined a full brain 5-min MR-STAT acquisition followed by FLAIR synthetization step with an ultra-under sampled conventional FLAIR sequence and performed the retrospective and prospective analysis of the proposed method on the patient datasets and a healthy volunteer.

RESULTS

All performance metrics of the proposed hybrid FLAIR images on patient datasets were significantly higher than those of the physics-based FLAIR images (p < 0.005), and comparable to those of conventional FLAIR images. The small difference between prospective and retrospective analysis on a healthy volunteer demonstrated the validity of the retrospective analysis of the hybrid method as presented for the patient datasets.

DISCUSSION

The proposed hybrid FLAIR achieved an improved lesion appearance in the clinical cases with neurological diseases compared to the physics-based FLAIR images, Future prospective work on patient data will address the validation of the method from a diagnostic perspective by radiological inspection of the new images over a larger patient cohort.

摘要

目的

合成 FLAIR(液体衰减反转恢复)图像的质量通常不如常规 FLAIR 图像,特别是在病变对比度不匹配方面。本研究旨在通过混合方法来改善病变的外观。

材料与方法

我们结合了全脑 5 分钟 MR-STAT 采集,然后是 FLAIR 合成步骤,以及超欠采样的常规 FLAIR 序列,并对患者数据集和健康志愿者进行了该方法的回顾性和前瞻性分析。

结果

患者数据集上的混合 FLAIR 图像的所有性能指标均显著高于基于物理的 FLAIR 图像(p<0.005),并且与常规 FLAIR 图像相当。对健康志愿者进行前瞻性和回顾性分析的差异较小,证明了回顾性分析该方法的有效性,该方法适用于患者数据集。

讨论

与基于物理的 FLAIR 图像相比,所提出的混合 FLAIR 方法在患有神经疾病的临床病例中实现了病变外观的改善。未来将在患者数据上进行前瞻性工作,通过对新图像进行更大的患者队列的放射学检查,从诊断角度验证该方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b7b/11582199/ec9b26956cab/10334_2024_1198_Fig1_HTML.jpg

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