Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA.
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Neuroradiol J. 2023 Jun;36(3):273-288. doi: 10.1177/19714009221122171. Epub 2022 Sep 5.
This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.
Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked -statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject.
fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant ( = 4.88×10 to = 0.042; one = 0.062) increases in consensus -statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in -statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data.
LLR denoising affords robust increases in -statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.
本研究旨在探讨一种局部低秩（LLR）去噪算法在基于任务的功能磁共振成像（fMRI）临床检查的原始源图像上的应用，以便在后处理过程中提高任务激活图的统计置信度。
在经过机构审查委员会（IRB）批准的方案下，11 名健康志愿者接受了基于任务的运动和语言 fMRI 检查。然后，将 LLR 去噪应用于 fMRI 处理前的原始复值图像数据。从常规非去噪（对照）数据生成的激活图与从 LLR 去噪图像数据生成的激活图进行比较。由四位经过董事会认证的神经放射科医生对激活图进行了共识评估；然后，将区域特异性和总体运动和语言共识阈值与非参数统计检验进行了比较。其他评估包括无 LLR 去噪和有 LLR 去噪的回顾性截断检查数据；基于 ROI 的分析跟踪了扫描时间减少时的统计量和时间 SNR（tSNR）。还进行了测试-再测试评估；将再测试数据与初始测试数据匹配，并对一个受试者进行比较。
从 LLR 去噪数据生成的 fMRI 激活图主要表现出运动和语言激活图的共识统计量阈值显著增加（=4.88×10 至 =0.042；单尾 =0.062）。在数据截断后，与对照相比，LLR 数据分别显示出特定任务的统计量和 tSNR 增加，分别超过 20%和 50%。LLR 去噪使在固定阈值下保持簇体积的同时，能够缩短检查持续时间。测试-再测试显示，在匹配初始测试数据时，LLR 数据的阈值较高，激活情况存在差异。
与常规处理相比，LLR 去噪可使 fMRI 激活图的统计量显著增加，并有可能在保持图谱质量的同时缩短扫描时间。
