Evaluation of 3D T1-weighted spoiled gradient echo MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

IF 2.4 3区 医学 Q2 CLINICAL NEUROLOGY
Neuroradiology Pub Date : 2024-10-01 Epub Date: 2024-07-05 DOI:10.1007/s00234-024-03417-9
Usha D Nagaraj, Jonathan R Dillman, Jean A Tkach, Joshua S Greer, James L Leach
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引用次数: 0

Abstract

Purpose: To assess image quality and diagnostic confidence of 3D T1-weighted spoiled gradient echo (SPGR) MRI using artificial intelligence (AI) reconstruction.

Materials and methods: This prospective, IRB-approved study enrolled 50 pediatric patients (mean age = 11.8 ± 3.1 years) undergoing clinical brain MRI. In addition to standard of care (SOC) compressed SENSE (CS = 2.5), 3D T1-weighted SPGR images were obtained with higher CS acceleration factors (5 and 8) to evaluate the ability of AI reconstruction to improve image quality and reduce scan time. Images were reviewed independently on dedicated research PACS workstations by two neuroradiologists. Quantitative analysis of signal intensities to calculate apparent grey and white matter signal to noise (aSNR) and grey-white matter apparent contrast to noise ratios (aCNR) was performed.

Results: AI improved overall image quality compared to standard CS reconstruction in 35% (35/100) of evaluations in CS = 2.5 (average scan time = 221 ± 6.9 s), 100% (46/46) of CS = 5 (average scan time = 113.3 ± 4.6 s) and 94% (47/50) of CS = 8 (average scan time = 74.1 ± 0.01 s). Quantitative analysis revealed significantly higher grey matter aSNR, white matter aSNR and grey-white matter aCNR with AI reconstruction compared to standard reconstruction for CS 5 and 8 (all p-values < 0.001), however not for CS 2.5.

Conclusions: AI reconstruction improved overall image quality and gray-white matter qualitative and quantitative aSNR and aCNR in highly accelerated (CS = 5 and 8) 3D T1W SPGR images in the majority of pediatric patients.

Abstract Image

利用人工智能图像重建技术评估小儿脑部三维T1加权破坏梯度回波磁共振图像质量。
目的:评估使用人工智能(AI)重建的三维 T1 加权破坏梯度回波(SPGR)磁共振成像的图像质量和诊断可信度:这项经 IRB 批准的前瞻性研究招募了 50 名接受临床脑部 MRI 检查的儿科患者(平均年龄 = 11.8 ± 3.1 岁)。除了标准护理(SOC)压缩 SENSE(CS = 2.5)外,还使用更高的 CS 加速因子(5 和 8)获得了三维 T1 加权 SPGR 图像,以评估人工智能重建提高图像质量和缩短扫描时间的能力。图像由两名神经放射学专家在专用研究 PACS 工作站上独立审查。对信号强度进行定量分析,计算灰质和白质表观信噪比(aSNR)和灰白质表观对比度与噪声比(aCNR):与标准CS重建相比,人工智能提高了35%(35/100)CS=2.5(平均扫描时间=221±6.9秒)、100%(46/46)CS=5(平均扫描时间=113.3±4.6秒)和94%(47/50)CS=8(平均扫描时间=74.1±0.01秒)的整体图像质量。定量分析显示,与 CS 5 和 CS 8 的标准重建相比,AI 重建的灰质 aSNR、白质 aSNR 和灰白质 aCNR 明显更高(所有 p 值均为结论):人工智能重建提高了大多数儿科患者高度加速(CS = 5 和 8)三维 T1W SPGR 图像的整体质量以及灰白质定性和定量 aSNR 和 aCNR。
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来源期刊
Neuroradiology
Neuroradiology 医学-核医学
CiteScore
5.30
自引率
3.60%
发文量
214
审稿时长
4-8 weeks
期刊介绍: Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.
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