Afat Saif, Wessling Daniel, Afat Carmen, Nickel Dominik, Arberet Simon, Herrmann Judith, Othman Ahmed E, Gassenmaier Sebastian
From the Departments of Diagnostic and Interventional Radiology.
Internal Medicine I, Eberhard Karls University Tuebingen, Tuebingen.
Invest Radiol. 2022 Mar 1;57(3):157-162. doi: 10.1097/RLI.0000000000000825.
The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradient echo imaging of the abdomen.
Fifty consecutive patients who underwent a 1.5 T contrast-enhanced magnetic resonance imaging examination of the abdomen between April and May 2021 were included in this retrospective study. After acquisition of a conventional T1-weighted volumetric interpolated breath-hold examination using Dixon for water-fat separation (VIBEStd), the acquired data were reprocessed including a superresolution algorithm that was optimized for partial Fourier acquisitions (VIBESR). To accelerate theoretically the acquisition process, a more aggressive partial Fourier setting was applied in VIBESR reconstructions practically corresponding to a shorter acquisition for the data included in the retrospective reconstruction. Precontrast, dynamic contrast-enhanced, and postcontrast data sets were processed. Image analysis was performed by 2 radiologists independently in a blinded random order without access to clinical data regarding the following criteria using a Likert scale ranging from 1 to 4 with 4 being the best: noise levels, sharpness and contrast of vessels, sharpness and contrast of organs and lymph nodes, overall image quality, diagnostic confidence, and lesion conspicuity.Wilcoxon signed rank test for paired data was applied to test for significance.
Mean patient age was 61 ± 14 years. Mean acquisition time for the conventional VIBEStd sequence was 15 ± 1 seconds versus theoretical 13 ± 1 seconds of acquired data used for the VIBESR reconstruction. Noise levels were evaluated to be better in VIBESR with a median of 4 (4-4) versus a median of 3 (3-3) in VIBEStd by both readers (P < 0.001). Sharpness and contrast of vessels as well as organs and lymph nodes were also evaluated to be superior in VIBESR compared with VIBEStd with a median of 4 (4-4) versus a median of 3 (3-3) (P < 0.001). Diagnostic confidence was also rated superior in VIBESR with a median of 4 (4-4) versus a median of 3.5 (3-4) in VIBEStd by reader 1 and with a median of 4 (4-4) for VIBESR and a median of 4 (4-4) for VIBEStd by reader 2 (both P < 0.001).
Image enhancement using deep learning-based superresolution tailored to partial Fourier acquisitions of T1-weighted gradient echo imaging of the abdomen provides improved image quality and diagnostic confidence in combination with more aggressive partial Fourier settings leading to shorter scan time.
本研究旨在探讨一种专为部分傅里叶变换定制的新型深度学习超分辨率算法的可行性及其影响，该算法能够在腹部1.5T T1加权梯度回波成像中实现理论上回顾性采集时间的减少。
本回顾性研究纳入了2021年4月至5月期间连续50例行1.5T腹部对比增强磁共振成像检查的患者。在使用狄克逊水脂分离技术进行常规T1加权容积内插屏气检查（VIBEStd）后，对采集的数据进行重新处理，包括针对部分傅里叶采集优化的超分辨率算法（VIBESR）。为从理论上加速采集过程，在VIBESR重建中应用了更激进的部分傅里叶设置，实际对应于回顾性重建中包含的数据的更短采集时间。对对比前、动态对比增强和对比后数据集进行处理。由2名放射科医生独立地以盲法随机顺序进行图像分析，在不获取临床数据的情况下，根据以下标准使用1至4分的李克特量表（4分为最佳）进行评估：噪声水平、血管的清晰度和对比度、器官和淋巴结的清晰度和对比度、整体图像质量、诊断信心以及病变的可见性。采用配对数据的Wilcoxon符号秩检验来检验显著性。
患者平均年龄为61±14岁。常规VIBEStd序列的平均采集时间为15±1秒，而用于VIBESR重建的采集数据的理论时间为13±1秒。两名读者均评估VIBESR的噪声水平更好，中位数为4（4 - 4），而VIBEStd的中位数为3（3 - 3）（P < 0.001）。与VIBEStd相比，VIBESR中血管以及器官和淋巴结的清晰度和对比度也被评估为更优，中位数为4（4 - 4），而VIBEStd的中位数为3（3 - 3）（P < 0.001）。读者1评估VIBESR的诊断信心也更高，中位数为4（4 - 4），而VIBEStd的中位数为3.5（3 - 4）；读者2评估VIBESR的中位数为4（4 - 4），VIBEStd的中位数为4（4 - 4）（两者P < 0.001）。
使用专为腹部T1加权梯度回波成像的部分傅里叶采集定制的基于深度学习的超分辨率进行图像增强，结合更激进的部分傅里叶设置可缩短扫描时间，从而提高图像质量和诊断信心。
