Deep-Learning-Aided Evaluation of Spondylolysis Imaged with Ultrashort Echo Time Magnetic Resonance Imaging.
Deep-Learning-Aided Evaluation of Spondylolysis Imaged with Ultrashort Echo Time Magnetic Resonance Imaging.
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DOI：
10.3390/s23188001
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发表时间：
2023-09-21
期刊：
影响因子：
--
通讯作者：
Bae WC
中科院分区：
文献类型：
--
作者：
Achar S;Hwang D;Finkenstaedt T;Malis V;Bae WC
峡部脊椎峡部裂会导致腰椎关节间部骨折，这种情况发生在多达一半患有持续性腰痛的青少年运动员身上。虽然计算机断层扫描 (CT) 是诊断椎弓峡部裂的黄金标准，但在年轻受试者的生殖器官附近使用电离辐射是不可取的。虽然磁共振成像 (MRI) 更可取，但它检测病情的灵敏度较低。最近，研究表明，与传统 MRI 相比，超短回波时间 (UTE) MRI 可以提供显着改善的骨对比度。为了进一步研究 UTE MRI，我们开发了监督深度学习工具，利用尸体脊柱的离体制备，从 UTE MRI 中生成 (1) 类似 CT 的图像和 (2) 骨折概率的显着图。我们进一步比较了UTE MRI（倒置以使外观与CT相似）和CT之间以及类CT图像和CT之间的对比噪声比（CNR）、均方误差（MSE）、峰值信噪比（PSNR）和结构相似指数（SSIM）的定量指标。定性结果证明了从 UTE MRI 成功生成类似 CT 图像的可行性，由于图像对比度和 CNR 的提高，可以更轻松地解释骨折。从定量角度来看，UTE MRI、类 CT 和 CT 图像的骨相对于缺损填充组织的平均 CNR 分别为 35、97 和 146，类 CT 图像明显高于 UTE MRI 图像。对于使用 CT 图像作为参考的图像相似性度量，与 UTE MRI 图像相比，类 CT 图像提供了显着较低的平均 MSE（0.038 对 0.0528）、更高的平均 PSNR（28.6 对 16.5）和更高的 SSIM（0.73 对 0.68）。此外，显着图通过向读者提供视觉提示，可以快速检测可能发生骨折的位置。这项概念验证研究仅限于来自离体样本的数据，并且需要对椎弓峡部裂的人类受试者进行额外的工作来完善临床使用的模型。尽管如此，这项研究表明，UTE MRI 和深度学习工具的使用对于峡部峡部裂的评估非常有用。
Isthmic spondylolysis results in fracture of pars interarticularis of the lumbar spine, found in as many as half of adolescent athletes with persistent low back pain. While computed tomography (CT) is the gold standard for the diagnosis of spondylolysis, the use of ionizing radiation near reproductive organs in young subjects is undesirable. While magnetic resonance imaging (MRI) is preferable, it has lowered sensitivity for detecting the condition. Recently, it has been shown that ultrashort echo time (UTE) MRI can provide markedly improved bone contrast compared to conventional MRI. To take UTE MRI further, we developed supervised deep learning tools to generate (1) CT-like images and (2) saliency maps of fracture probability from UTE MRI, using ex vivo preparation of cadaveric spines. We further compared quantitative metrics of the contrast-to-noise ratio (CNR), mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) between UTE MRI (inverted to make the appearance similar to CT) and CT and between CT-like images and CT. Qualitative results demonstrated the feasibility of successfully generating CT-like images from UTE MRI to provide easier interpretability for bone fractures thanks to improved image contrast and CNR. Quantitatively, the mean CNR of bone against defect-filled tissue was 35, 97, and 146 for UTE MRI, CT-like, and CT images, respectively, being significantly higher for CT-like than UTE MRI images. For the image similarity metrics using the CT image as the reference, CT-like images provided a significantly lower mean MSE (0.038 vs. 0.0528), higher mean PSNR (28.6 vs. 16.5), and higher SSIM (0.73 vs. 0.68) compared to UTE MRI images. Additionally, the saliency maps enabled quick detection of the location with probable pars fracture by providing visual cues to the reader. This proof-of-concept study is limited to the data from ex vivo samples, and additional work in human subjects with spondylolysis would be necessary to refine the models for clinical use. Nonetheless, this study shows that the utilization of UTE MRI and deep learning tools could be highly useful for the evaluation of isthmic spondylolysis.
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影响因子：
2.1
作者：
Geiger, Daniel;Bae, Won C.;Chung, Christine B.
通讯作者：
Chung, Christine B.
影响因子：
4.4
作者：
Eckert, MP;Bradley, AP
通讯作者：
Bradley, AP
影响因子：
6.7
作者：
Finkenstaedt, Tim;Siriwanarangsun, Palanan;Bae, Won C.
通讯作者：
Bae, Won C.
影响因子：
--
作者：
Li, Jian;Wu, Lin-Hua;Xu, Meng-Ying;Ren, Jia-Liang;Li, Zhihao;Liu, Jin-Rui;Wang, Ai Jun;Chen, Bing
通讯作者：
Chen, Bing
影响因子：
3.3
作者：
Boroojeni, Parna Eshraghi;Chen, Yasheng;Commean, Paul K.;Eldeniz, Cihat;Skolnick, Gary B.;Merrill, Corinne;Patel, Kamlesh B.;An, Hongyu
通讯作者：
An, Hongyu