Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.
Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging.
复制标题
评估高质量T2加权乳腺磁共振成像的深度学习重建。
DOI：
10.3390/tomography9050152
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发表时间：
2023-10-18
期刊：
影响因子：
--
通讯作者：
Holmes JH
中科院分区：
文献类型：
--
作者：
Allen TJ;Henze Bancroft LC;Unal O;Estkowski LD;Cashen TA;Korosec F;Strigel RM;Kelcz F;Fowler AM;Gegios A;Thai J;Lebel RM;Holmes JH
用于改善MR图像质量的深度学习（DL）重建技术正在商业化，希望它们将适用于多个成像应用部位和采集协议。然而，在临床实施之前，这些方法必须针对特定用例进行验证。在这项工作中，质量标准的护理（SOC）T2 w和高空间分辨率（HR）的乳房成像进行了评估，有和没有原型DL重建。使用从体模采集的数据、20项回顾性采集的SOC患者检查和56项前瞻性采集的SOC和HR患者检查进行研究。通过信噪比（SNR）、对比度噪声比（CNR）和边缘清晰度定量评估图像质量。在质量方面，所有体内图像均由两名或四名放射科医师阅片员使用5分制Likert量表在以下类别中进行评分：伪影、感知清晰度、感知SNR和总体质量。对阅片员评分的差异进行了显著性检验。还评估了读片者的偏好和对信号强度变化的感知。DL的应用导致更高的平均SNR（1.2-2.8倍）、CNR（1.0-1.8倍）和图像清晰度（1.2-1.7倍）。质量方面，与非DL图像相比，使用DL的SOC采集导致所有类别的图像质量评分显著改善。与非DL SOC和非DL HR图像相比，使用DL的HR采集显著提高了SNR、清晰度和整体质量。与SOC采集相比，HR数据的采集时间仅需增加20%，读片员通常更喜欢DL图像而不是非DL图像。总体而言，DL重建证明了临床乳腺MRI中T2 w图像质量的改善。
Deep learning (DL) reconstruction techniques to improve MR image quality are becoming commercially available with the hope that they will be applicable to multiple imaging application sites and acquisition protocols. However, before clinical implementation, these methods must be validated for specific use cases. In this work, the quality of standard-of-care (SOC) T2w and a high-spatial-resolution (HR) imaging of the breast were assessed both with and without prototype DL reconstruction. Studies were performed using data collected from phantoms, 20 retrospectively collected SOC patient exams, and 56 prospectively acquired SOC and HR patient exams. Image quality was quantitatively assessed via signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge sharpness. Qualitatively, all in vivo images were scored by either two or four radiologist readers using 5-point Likert scales in the following categories: artifacts, perceived sharpness, perceived SNR, and overall quality. Differences in reader scores were tested for significance. Reader preference and perception of signal intensity changes were also assessed. Application of the DL resulted in higher average SNR (1.2–2.8 times), CNR (1.0–1.8 times), and image sharpness (1.2–1.7 times). Qualitatively, the SOC acquisition with DL resulted in significantly improved image quality scores in all categories compared to non-DL images. HR acquisition with DL significantly increased SNR, sharpness, and overall quality compared to both the non-DL SOC and the non-DL HR images. The acquisition time for the HR data only required a 20% increase compared to the SOC acquisition and readers typically preferred DL images over non-DL counterparts. Overall, the DL reconstruction demonstrated improved T2w image quality in clinical breast MRI.
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影响因子：
19.7
作者：
Kim, Minjae;Kim, Ho Sung;Lebel, Marc R.
通讯作者：
Lebel, Marc R.
影响因子：
8.4
作者：
Sardanelli, Francesco;Boetes, Carla;Wilson, Robin
通讯作者：
Wilson, Robin
影响因子：
3.3
作者：
Wessling, Daniel;Gassenmaier, Sebastian;Preibsch, Heike
通讯作者：
Preibsch, Heike
DOI：
10.1097/rmr.0b013e31818a40a5
发表时间：
2008-06-01
期刊：
Topics in magnetic resonance imaging : TMRI
影响因子：
--
作者：
DeMartini, Wendy;Lehman, Constance
通讯作者：
Lehman, Constance
使用基于深度学习的重建算法及其对心肌疤痕量化的影响，改善了晚期Gadolinium增强图像质量。
影响因子：
5.9
作者：
van der Velde N;Hassing HC;Bakker BJ;Wielopolski PA;Lebel RM;Janich MA;Kardys I;Budde RPJ;Hirsch A
通讯作者：
Hirsch A