深度学习图像重建算法在膝关节加速MRI中的临床应用研究

Clinical feasibility of 2D FSE sequences of the knee MRI protocol using deep-learning image reconstruction

目的 评估使用和不使用深度学习重建(deep learning reconstruction,DLR)算法的膝关节加速二维(two dimensional,2D)快速自旋回波(fast spin echo,FSE)序列的图像质量和诊断效能。材料与方法前瞻性纳入92名怀疑有膝关节病变的患者,采用3.0 T MRI并行采集(parallel imaging,PI)基于K空间域重建(autocalibrating reconstruction for Cartesian sampling,ARC)算法进行膝关节加速2D FSE序列扫描,设置加速因子为2.0。扫描结束后系统自动保存为不使用DLR的原始图像(original images of FSE,FSE_O)和使用DLR后的FSE(deep learning reconstruction images of FSE,FSE_(DL))两组图像。采用主观(李克特5分量表,内容包括图像的整体质量、清晰度、诊断置信度)与客观定量测定图像信噪比(signal-to-noise ratio,SNR)与对比噪声比(contrast-to-noise ratio,CNR)相结合的方法对两组图像质量进行综合评价。分别测量比较膝关节质子密度加权成像(proton density weighted imaging,PDWI)、T1WI矢状位股骨下端骨髓腔、软骨、滑膜液、髌下脂肪垫、前交叉韧带各组织的SNR和软骨/滑膜液CNR。基于两组图像分别对膝关节结构异常进行评分,同时评估观察者间和观察者内评分一致性。结果四个临床标准方位加速2D FSE序列的MRI采集时间为4 min 39 s。FSE_(DL)的图像整体质量、清晰度及诊断置信度评分均高于FSE_O,其中对FSE_(DL)、FSE_O的图像清晰度评分差异有统计学意义(P<0.05)。两名医师对图像质量主观评价的一致性组内相关系数在0.710~0.898之间。使用DLR的PDWI、T1WI(PDWI_(DL)、T1WI_(DL))图像上股骨外侧髁、股骨外侧髁软骨、滑膜液、髌下脂肪垫SNR明显高于不使用DLR的PDWI、T1WI原始图像(PDWI_O、T1WI_O),PDWI_(DL)图像上软骨/滑膜液CNR明显高于PDWI_O,差异均具有统计学意义(P<0.05)。两名医师分别基于FSE_O及FSE_(DL)对膝关节结构异常进行评分,具有极好的一致性,κ值在0.954~1.000之间。比较同一名医师对两组图像的诊断结果,发现其对软骨缺损的检测和评估具有较好的一致性,κ值分别为0.769和0.771。对半月板、韧带、骨髓及滑膜液的检测和评估,诊断具有极好的一致性,κ值在0.944~1.000之间,FSE_(DL)、FSE_O对上述结构异常的检测无临床相关性差异。结论 DLR可用于膝关节PI ARC技术,在提高图像质量、保证临床诊断效能的同时5 min内完成图像采集,适用于临床各种膝关节疾病患者。

Objective:To propose a rapid knee imaging based on two-dimensional fast spin echo sequence and examined the reliability and diagnostic performance of deep learning-based reconstruction images on knee joint pathology via comparison of images with and without deep learning reconstruction algorithm(DLR).Materials and Methods:A total of 92 patients,a protocol including accelerated two dimensional(2D)fast spin echo(FSE)sequences with autocalibrating reconstruction for cartesian sampling(ARC)as a kind of parallel imaging were enrolled in this prospective study.All MR data was reconstructed with and without DLR as original images of FSE(FSEO)and deep learning reconstruction images of FSE(FSEDL),respectively.Two radiologists subjectively assessed images at the aspects of overall image quality,sharpness and diagnostic confidence using a Likert scale(1-5,5=best),and also objectively evaluated signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR).SNR of femoral marrow,cartilage,synovial fluid,infrapatellar fat pad,anterior cruciate ligament and CNR of cartilage/synovial fluid were measured on proton density weighted imaging(PDWI)sequence and T1 weighted imaging(T1WI)sequence of the knee.Inter-observer and intra-observer subjective score consistency were also computed.Results:The overall image quality,sharpness and diagnostic confidence for FSEDL were higher compared to FSE0,showing significantly improved sharpness(P<0.05).Inter-and intra-reader agreement was substantial to almost perfect(ICC:0.710-0.898).For objective evaluation,SNR and CNR of PDWIDL and T1WIDL images were significantly higher than that of PDWI0 and T1WI0 images(P<0.05).Two radiologists respectively assessed the sequences regarding structural abnormalities of the knee based on FSE0 and FSEDL.Inter-and intra-reader agreement were excellent consistent(κ:0.954-1.000)for the detection of internal derangement.Intra-reader agreement was substantial to almost perfect(κ=0.769,0.771)for the assessment of cartilage defects and almost perfect(κ:0.944-1.000)for the assessment of meniscal,ligament,bone marrow,syn-ovial fluid.There were no detection differences of structural abnormalities between FSEDL and FSE0.Conclusions:DLR can be used for knee joint PI ARC technology,which can improve the image quality and ensure the clinical diagnosis efficiency at the same time to complete the image acquisition within 5 min,suitable for clinical patients with various knee joint diseases.