Data_Sheet_1_Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people.PDF

BackgroundPortable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people.
MethodsT1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9–26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network (‘SynthSR’), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches.
ResultsSingle pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60–0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = −0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85–0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness.
ConclusionApplying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

背景
便携式低场强磁共振成像（magnetic resonance imaging, MRI）系统是传统高场强磁共振系统的极具前景的替代方案，具备将磁共振技术大规模推广至资源匮乏地区的潜力。然而，较低的图像质量与分辨率可能限制此类设备的科研与临床应用潜力。本研究针对低场强磁共振系统测试了两种超分辨率（super-resolution）方法以提升图像质量，并将处理后的图像与高场强系统采集的图像在青年人群样本中进行了一致性对比。

方法
本研究从70名受试者（平均年龄20.39岁，年龄范围9~26岁）的低场强（64mT）Hyperfine系统与高场强（3T）西门子（Siemens）系统中采集了T1加权与T2加权结构磁共振图像。本研究测试了两种用于提升高低场强磁共振图像一致性的超分辨率方法：（1）基于卷积神经网络（convolutional neural network）的"SynthSR"处理方法，（2）多方位图像平均法。我们提取了脑区体积、皮层厚度以及皮层表面积的估算值。本研究采用皮尔逊相关分析检验上述指标的一致性，并通过Steiger Z检验对比标准成像与超分辨率方法的一致性差异。

结果
低场强采集的单组T1加权与T2加权图像在总颅内体积、皮层表面积、皮层体积、皮层下体积以及全脑体积的估算值上，与高场强图像呈现出较高的一致性（相关系数r范围为0.60~0.88）。但大脑白质体积的一致性相对较低（r=0.32，p=0.007，q=0.009），而平均皮层厚度的一致性无统计学意义（r=-0.05，p=0.664，q=0.664）。采用"SynthSR"处理图像可显著提升全脑体积、白质体积、总表面积、皮层下体积、皮层体积以及总颅内体积的一致性（相关系数r范围为0.85~0.97），但全脑平均皮层厚度的一致性未获显著提升（r=0.14）。另一种多方位图像平均法可提升大脑白质体积与全脑体积的一致性。采用"SynthSR"处理还可显著改善多脑区的皮层体积、表面积与皮层下体积估算值的一致性，同时在孤立的前额叶与颞叶区域的皮层厚度估算值上也提升了一致性。

结论
将超分辨率方法应用于低场强成像，可提升青年人群的脑区体积与表面积测量准确性。但受限于低场强系统的分辨率，更精细的脑测量指标（如皮层厚度）仍难以获得准确结果。