Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions.
Sensitivity of portable low-field magnetic resonance imaging for multiple sclerosis lesions.
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DOI：
10.1016/j.nicl.2022.103101
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发表时间：
2022
影响因子：
4.2
通讯作者：
Stein, Joel M.
中科院分区：
文献类型：
--
作者：
Arnold, Campbell;Tu, Danni;Okar, Serhat, V;Nair, Govind;By, Samantha;Kawatra, Karan D.;Robert-Fitzgerald, Timothy E.;Desiderio, Lisa M.;Schindler, Matthew K.;Shinohara, Russell T.;Reich, Daniel S.;Stein, Joel M.
在33例MS患者中分析了配对、同日、3 T和64 mT MRI研究。64 mT MRI对3 T确诊病例中任何病变的检出敏感性为94%。与3 T相比，64 mT下检测到的最小病变直径更大。总病变体积估计值在3 T和64 mT扫描之间具有很强的相关性。便携式低场MRI检测到白色病变，但可能会错过较小的病变。磁共振成像（MRI）是诊断和管理神经系统疾病（如多发性硬化症（MS））的基本工具。新的便携式低场强MRI扫描仪可能会降低神经成像的经济和技术障碍，并覆盖服务不足或残疾人群，但这些设备对MS病变的敏感性尚不清楚。我们试图确定是否可以在便携式64 mT扫描仪上检测到白色病变，比较配对的3 T和64 mT扫描之间的自动病变分割和总病变体积，识别有助于病变检测准确性的特征，并探索低场超分辨率成像。在这项前瞻性横断面研究中，（FLAIR，T1 w和T2 w）扫描收集了36名成人（32名女性;平均年龄，50 ± 14岁），使用Siemens 3 T诊断已知或疑似MS（FLAIR：1 mm各向同性，T1 w：1 mm各向同性，T2 w：0.34- 0.5 × 0.34-0.5 × 3-5 mm）和Hyperfine 64 mT（FLAIR：1.6 × 1.6 × 5 mm，T1 w：1.5 × 1.5 × 5 mm和T2 w：1.5 × 1.5 × 5 mm）扫描仪。神经放射科医生对图像进行了审查。手动测量MS病变，并使用自动算法进行分割。统计分析评估了自动体积测量中跨扫描仪分割的准确性和可变性以及系统性扫描仪偏倚。在94%（31/33）的确诊MS患者中，64 mT扫描识别出病变。手动检测到的平均最小病变最大直径在64 mT时为5.7 ± 1.3 mm，在3 T时为2.1 ± 0.6 mm，接近相应扫描仪序列（3 T：1 mm，64 mT：5 mm层厚）的空间分辨率。3 T和64 mT扫描之间的自动病变体积估计高度相关（r = 0.89，p < 0.001）。Bland-Altman分析确定了64 mT分割的偏倚（平均值= 1.6 ml，标准误差= 5.2 ml，一致性限度= -19.0-15.9 ml），高估了低病变体积，低估了高体积（r = 0.74，p < 0.001）。目视检查显示过度分割是64 mT T2-FLAIR上静脉高信号驱动的。病变大小驱动分割准确性，93%的病变> 1.0 ml，所有病变> 1.5 ml均被检测到。使用多次采集体积平均，我们能够在64 mT器械上生成1.6 mm各向同性图像。总体而言，我们的研究结果表明，在已建立的MS中，便携式64 mT MRI扫描仪可以识别白色病变，并且总病变体积的自动估计与3 T扫描的测量结果相关。
Paired, same-day, 3T and 64mT MRI studies were analyzed in 33 MS patients. 64mT MRI showed 94% sensitivity for detecting any lesions in 3T confirmed cases. The diameter of the smallest detected lesion was larger at 64mT compared to 3T. Total lesion volume estimates were strongly correlated between 3T and 64mT scans. Portable low-field MRI detects white matter lesions, but smaller lesions may be missed. Magnetic resonance imaging (MRI) is a fundamental tool in the diagnosis and management of neurological diseases such as multiple sclerosis (MS). New portable, low-field strength, MRI scanners could potentially lower financial and technical barriers to neuroimaging and reach underserved or disabled populations, but the sensitivity of these devices for MS lesions is unknown. We sought to determine if white matter lesions can be detected on a portable 64mT scanner, compare automated lesion segmentations and total lesion volume between paired 3T and 64mT scans, identify features that contribute to lesion detection accuracy, and explore super-resolution imaging at low-field. In this prospective, cross-sectional study, same-day brain MRI (FLAIR, T1w, and T2w) scans were collected from 36 adults (32 women; mean age, 50 ± 14 years) with known or suspected MS using Siemens 3T (FLAIR: 1 mm isotropic, T1w: 1 mm isotropic, and T2w: 0.34–0.5 × 0.34–0.5 × 3–5 mm) and Hyperfine 64mT (FLAIR: 1.6 × 1.6 × 5 mm, T1w: 1.5 × 1.5 × 5 mm, and T2w: 1.5 × 1.5 × 5 mm) scanners at two centers. Images were reviewed by neuroradiologists. MS lesions were measured manually and segmented using an automated algorithm. Statistical analyses assessed accuracy and variability of segmentations across scanners and systematic scanner biases in automated volumetric measurements. Lesions were identified on 64mT scans in 94% (31/33) of patients with confirmed MS. The average smallest lesions manually detected were 5.7 ± 1.3 mm in maximum diameter at 64mT vs 2.1 ± 0.6 mm at 3T, approaching the spatial resolution of the respective scanner sequences (3T: 1 mm, 64mT: 5 mm slice thickness). Automated lesion volume estimates were highly correlated between 3T and 64mT scans (r = 0.89, p < 0.001). Bland-Altman analysis identified bias in 64mT segmentations (mean = 1.6 ml, standard error = 5.2 ml, limits of agreement = –19.0–15.9 ml), which over-estimated low lesion volume and under-estimated high volume (r = 0.74, p < 0.001). Visual inspection revealed over-segmentation was driven venous hyperintensities on 64mT T2-FLAIR. Lesion size drove segmentation accuracy, with 93% of lesions > 1.0 ml and all lesions > 1.5 ml being detected. Using multi-acquisition volume averaging, we were able to generate 1.6 mm isotropic images on the 64mT device. Overall, our results demonstrate that in established MS, a portable 64mT MRI scanner can identify white matter lesions, and that automated estimates of total lesion volume correlate with measurements from 3T scans.
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影响因子：
5.7
作者：
Jovicich J;Czanner S;Han X;Salat D;van der Kouwe A;Quinn B;Pacheco J;Albert M;Killiany R;Blacker D;Maguire P;Rosas D;Makris N;Gollub R;Dale A;Dickerson BC;Fischl B
通讯作者：
Fischl B
影响因子：
16.6
作者：
Mazurek MH;Cahn BA;Yuen MM;Prabhat AM;Chavva IR;Shah JT;Crawford AL;Welch EB;Rothberg J;Sacolick L;Poole M;Wira C;Matouk CC;Ward A;Timario N;Leasure A;Beekman R;Peng TJ;Witsch J;Antonios JP;Falcone GJ;Gobeske KT;Petersen N;Schindler J;Sansing L;Gilmore EJ;Hwang DY;Kim JA;Malhotra A;Sze G;Rosen MS;Kimberly WT;Sheth KN
通讯作者：
Sheth KN
影响因子：
4.7
作者：
Deoni SCL;D'Sa V;Volpe A;Beauchemin J;Croff JM;Elliott AJ;Pini N;Lucchini M;Fifer WP
通讯作者：
Fifer WP
影响因子：
9.9
作者：
Hagens MHJ;Burggraaff J;Kilsdonk ID;de Vos ML;Cawley N;Sbardella E;Andelova M;Amann M;Lieb JM;Pantano P;Lissenberg-Witte BI;Killestein J;Oreja-Guevara C;Ciccarelli O;Gasperini C;Lukas C;Wattjes MP;Barkhof F;MAGNIMS Study Group
通讯作者：
MAGNIMS Study Group
影响因子：
3.5
作者：
Mattay, R. R.;Davtyan, K.;Mamourian, A. C.
通讯作者：
Mamourian, A. C.