论文标题
准确的超分辨率低场脑MRI
Accurate super-resolution low-field brain MRI
论文作者
论文摘要
最近将便携式低场MRI（LF-MRI）引入临床环境的潜力可能会改变神经影像学。但是，LF-MRI受到较低分辨率和信噪比的限制，导致大脑区域的表征不完整。为了应对这一挑战，机器学习的最新进展促进了从一个或多个下分辨率扫描得出的较高分辨率图像的综合。在这里，我们报告了机器学习超分辨率（SR）算法的扩展，以合成从LF-MRI T1加权和T2加权序列中的1 mM各向同性Mprage样扫描。我们对LF和高场（HF，1.5T-3T）配对数据集的初步结果表明：（i）直接将可用的自动分割工具直接应用于LF-MRI图像中；但是（ii）分割工具在应用于HF-MRI的金标准测量高度相关的SR图像时成功（例如，海马体积的r = 0.85，丘脑的r = 0.84，整个小脑的r = 0.92）。这项工作表明了原则后处理的后处理图像增强了LF-MRI序列的增强。这些结果为未来的工作奠定了基础，以增强LF处正常和异常图像发现的检测，并最终提高LF-MRI的诊断性能。我们的工具可在FreeSurfer（Surfer.nmr.mgh.harvard.edu/）上公开使用。
The recent introduction of portable, low-field MRI (LF-MRI) into the clinical setting has the potential to transform neuroimaging. However, LF-MRI is limited by lower resolution and signal-to-noise ratio, leading to incomplete characterization of brain regions. To address this challenge, recent advances in machine learning facilitate the synthesis of higher resolution images derived from one or multiple lower resolution scans. Here, we report the extension of a machine learning super-resolution (SR) algorithm to synthesize 1 mm isotropic MPRAGE-like scans from LF-MRI T1-weighted and T2-weighted sequences. Our initial results on a paired dataset of LF and high-field (HF, 1.5T-3T) clinical scans show that: (i) application of available automated segmentation tools directly to LF-MRI images falters; but (ii) segmentation tools succeed when applied to SR images with high correlation to gold standard measurements from HF-MRI (e.g., r = 0.85 for hippocampal volume, r = 0.84 for the thalamus, r = 0.92 for the whole cerebrum). This work demonstrates proof-of-principle post-processing image enhancement from lower resolution LF-MRI sequences. These results lay the foundation for future work to enhance the detection of normal and abnormal image findings at LF and ultimately improve the diagnostic performance of LF-MRI. Our tools are publicly available on FreeSurfer (surfer.nmr.mgh.harvard.edu/).
