Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging,NeuroImage: Clinical

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Fully automated detection of paramagnetic rims in multiple sclerosis lesions on 3T susceptibility-based MR imaging
NeuroImage: Clinical ( IF 4.2 ) Pub Date : 2021-08-27 , DOI: 10.1016/j.nicl.2021.102796
Carolyn Lou ₁ , Pascal Sati ₂ , Martina Absinta ₃ , Kelly Clark ₁ , Jordan D Dworkin ₄ , Alessandra M Valcarcel ₁ , Matthew K Schindler ₅ , Daniel S Reich ₃ , Elizabeth M Sweeney ₆ , Russell T Shinohara ₇

Affiliation

Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH), Bethesda, MD, USA; Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA.
Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; New York State Psychiatric Institute, New York, NY, USA.
Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA; Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.

Background and Purpose

The presence of a paramagnetic rim around a white matter lesion has recently been shown to be a hallmark of a particular pathological type of multiple sclerosis lesion. Increased prevalence of these paramagnetic rim lesions is associated with a more severe disease course in MS, but manual identification is time-consuming. We present APRL, a method to automatically detect paramagnetic rim lesions on 3T T2*-phase images.

Methods

T1-weighted, T2-FLAIR, and T2*-phase MRI of the brain were collected at 3T for 20 subjects with MS. The images were then processed with automated lesion segmentation, lesion center detection, lesion labelling, and lesion-level radiomic feature extraction. A total of 951 lesions were identified, 113 (12%) of which contained a paramagnetic rim. We divided our data into a training set (16 patients, 753 lesions) and a testing set (4 patients, 198 lesions), fit a random forest classification model on the training set, and assessed our ability to classify paramagnetic rim lesions on the test set.

Results

The number of paramagnetic rim lesions per subject identified via our automated lesion labelling method was highly correlated with the gold standard count per subject, r = 0.86 (95% CI [0.68, 0.94]). The classification algorithm using radiomic features classified lesions with an area under the curve of 0.82 (95% CI [0.74, 0.92]).

Conclusion

This study develops a fully automated technique, APRL, for the detection of paramagnetic rim lesions using standard T1 and FLAIR sequences and a T2*phase sequence obtained on 3T MR images.

中文翻译：

在基于 3T 磁敏性的 MR 成像上全自动检测多发性硬化病灶中的顺磁边缘

背景和目的

最近已证明白质病变周围存在顺磁性边缘是特定病理类型的多发性硬化病变的标志。这些顺磁性边缘病变的患病率增加与 MS 中更严重的病程有关，但手动识别非常耗时。我们提出了 APRL，一种在 3T T2* 相位图像上自动检测顺磁边缘病变的方法。

方法

在 3T 时收集了 20 名 MS 受试者的 T1 加权、T2-FLAIR 和 T2* 期 MRI。然后通过自动病灶分割、病灶中心检测、病灶标记和病灶级放射组学特征提取对图像进行处理。共鉴定出 951 个病灶，其中 113 个 (12%) 包含顺磁性边缘。我们将数据分为训练集（16 名患者，753 个病灶）和测试集（4 名患者，198 个病灶），在训练集上拟合随机森林分类模型，并评估我们在测试中对顺磁边缘病灶进行分类的能力放。