Abstract：
OBJECTIVE: To evaluate the diagnostic performance of CT-like MR images reconstructed with an algorithm combining compressed sense (CS) with deep learning (DL) in patients with suspected osseous shoulder injury compared to conventional CS-reconstructed images.
METHODS: Thirty-two patients (12 women, mean age 46 ± 14.9 years) with suspected traumatic shoulder injury were prospectively enrolled into the study. All patients received MR imaging of the shoulder, including a CT-like 3D T1-weighted gradient-echo (T1 GRE) sequence and in case of suspected fracture a conventional CT. An automated DL-based algorithm, combining CS and DL (CS DL) was used to reconstruct images of the same k-space data as used for CS reconstructions. Two musculoskeletal radiologists assessed the images for osseous pathologies, image quality and visibility of anatomical landmarks using a 5-point Likert scale. Moreover, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated.
RESULTS: Compared to CT, all acute fractures (n = 23) and osseous pathologies were detected accurately on the CS only and CS DL images with almost perfect agreement between the CS DL and CS only images (κ 0.95 (95 %confidence interval 0.82-1.00). Image quality as well as the visibility of the fracture lines, bone fragments and glenoid borders were overall rated significantly higher for the CS DL reconstructions than the CS only images (CS DL range 3.7-4.9 and CS only range 3.2-3.8, P = 0.01-0.04). Significantly higher SNR and CNR values were observed for the CS DL reconstructions (P = 0.02-0.03).
CONCLUSIONS: Evaluation of traumatic shoulder pathologies is feasible using a DL-based algorithm for reconstruction of high-resolution CT-like MR imaging.
METHODS: Thirty-two patients (12 women, mean age 46 ± 14.9 years) with suspected traumatic shoulder injury were prospectively enrolled into the study. All patients received MR imaging of the shoulder, including a CT-like 3D T1-weighted gradient-echo (T1 GRE) sequence and in case of suspected fracture a conventional CT. An automated DL-based algorithm, combining CS and DL (CS DL) was used to reconstruct images of the same k-space data as used for CS reconstructions. Two musculoskeletal radiologists assessed the images for osseous pathologies, image quality and visibility of anatomical landmarks using a 5-point Likert scale. Moreover, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated.
RESULTS: Compared to CT, all acute fractures (n = 23) and osseous pathologies were detected accurately on the CS only and CS DL images with almost perfect agreement between the CS DL and CS only images (κ 0.95 (95 %confidence interval 0.82-1.00). Image quality as well as the visibility of the fracture lines, bone fragments and glenoid borders were overall rated significantly higher for the CS DL reconstructions than the CS only images (CS DL range 3.7-4.9 and CS only range 3.2-3.8, P = 0.01-0.04). Significantly higher SNR and CNR values were observed for the CS DL reconstructions (P = 0.02-0.03).
CONCLUSIONS: Evaluation of traumatic shoulder pathologies is feasible using a DL-based algorithm for reconstruction of high-resolution CT-like MR imaging.
摘要：
目的：评估将压缩感觉（CS）与深度学习（DL）相结合的算法重建的CT样MR图像与常规CS重建图像相比，在疑似骨性肩关节损伤患者中的诊断性能。
方法：32名患者（12名女性，平均年龄46±14.9岁），疑似创伤性肩关节损伤被前瞻性纳入研究。所有患者均接受肩关节MR成像,包括类似CT的3DT1加权梯度回波（T1GRE）序列，如果怀疑骨折，则使用常规CT。基于DL的自动化算法，组合CS和DL(CSDL)用于重建与用于CS重建相同的k空间数据的图像。两名肌肉骨骼放射科医生评估了这些图像的骨病理学，使用5点Likert量表的解剖标志的图像质量和可见性。此外，计算信噪比(SNR)和对比度噪声比(CNR)。
结果：与CT相比，所有急性骨折(n=23)和骨性病变均在仅CS和CSDL图像上准确检测到,CSDL和CSDL图像几乎完全一致(κ0.95(95%置信区间0.82-1.00).图像质量以及断裂线的可见性，CSDL重建的骨碎片和关节盂边界总体评分明显高于仅CS重建的图像(CSDL范围3.7-4.9,CS范围3.2-3.8,P=0.01-0.04).对于CSDL重建观察到显著更高的SNR和CNR值(P=0.02-0.03)。
结论：使用基于DL的重建高分辨率CT样MR成像算法评估创伤性肩关节病变是可行的。
方法：32名患者（12名女性，平均年龄46±14.9岁），疑似创伤性肩关节损伤被前瞻性纳入研究。所有患者均接受肩关节MR成像,包括类似CT的3DT1加权梯度回波（T1GRE）序列，如果怀疑骨折，则使用常规CT。基于DL的自动化算法，组合CS和DL(CSDL)用于重建与用于CS重建相同的k空间数据的图像。两名肌肉骨骼放射科医生评估了这些图像的骨病理学，使用5点Likert量表的解剖标志的图像质量和可见性。此外，计算信噪比(SNR)和对比度噪声比(CNR)。
结果：与CT相比，所有急性骨折(n=23)和骨性病变均在仅CS和CSDL图像上准确检测到,CSDL和CSDL图像几乎完全一致(κ0.95(95%置信区间0.82-1.00).图像质量以及断裂线的可见性，CSDL重建的骨碎片和关节盂边界总体评分明显高于仅CS重建的图像(CSDL范围3.7-4.9,CS范围3.2-3.8,P=0.01-0.04).对于CSDL重建观察到显著更高的SNR和CNR值(P=0.02-0.03)。
结论：使用基于DL的重建高分辨率CT样MR成像算法评估创伤性肩关节病变是可行的。
