Effects of systematic partial volume errors on the estimation of gray matter cerebral blood flow with arterial spin labeling MRI

Objective

Partial volume (PV) correction is an important step in arterial spin labeling (ASL) MRI that is used to separate perfusion from structural effects when computing the mean gray matter (GM) perfusion. There are three main methods for performing this correction: (1) GM-threshold, which includes only voxels with GM volume above a preset threshold; (2) GM-weighted, which uses voxel-wise GM contribution combined with thresholding; and (3) PVC, which applies a spatial linear regression algorithm to estimate the flow contribution of each tissue at a given voxel. In all cases, GM volume is obtained using PV maps extracted from the segmentation of the T1-weighted (T1w) image. As such, PV maps contain errors due to the difference in readout type and spatial resolution between ASL and T1w images. Here, we estimated these errors and evaluated their effect on the performance of each PV correction method in computing GM cerebral blood flow (CBF).

Materials and methods

Twenty-two volunteers underwent scanning using 2D echo planar imaging (EPI) and 3D spiral ASL. For each PV correction method, GM CBF was computed using PV maps simulated to contain estimated errors due to spatial resolution mismatch and geometric distortions which are caused by the mismatch in readout between ASL and T1w images. Results were analyzed to assess the effect of each error on the estimation of GM CBF from ASL data.

Results

Geometric distortion had the largest effect on the 2D EPI data, whereas the 3D spiral was most affected by the resolution mismatch. The PVC method outperformed the GM-threshold even in the presence of combined errors from resolution mismatch and geometric distortions. The quantitative advantage of PVC was 16% without and 10% with the combined errors for both 2D and 3D ASL. Consistent with theoretical expectations, for error-free PV maps, the PVC method extracted the true GM CBF. In contrast, GM-weighted overestimated GM CBF by 5%, while GM-threshold underestimated it by 16%. The presence of PV map errors decreased the calculated GM CBF for all methods.

Conclusion

The quality of PV maps presents no argument for the preferential use of the GM-threshold method over PVC in the clinical application of ASL.

     

A systematic review on the use of quantitative imaging to detect cancer therapy adverse effects in normal-appearing brain tissue

Cancer therapy for both central nervous system (CNS) and non-CNS tumors has been previously associated with transient and long-term cognitive deterioration, commonly referred to as ‘chemo fog’. This therapy-related damage to otherwise normal-appearing brain tissue is reported using post-mortem neuropathological analysis. Although the literature on monitoring therapy effects on structural magnetic resonance imaging (MRI) is well established, such macroscopic structural changes appear relatively late and irreversible. Early quantitative MRI biomarkers of therapy-induced damage would potentially permit taking these treatment side effects into account, paving the way towards a more personalized treatment planning.

This systematic review (PROSPERO number 224196) provides an overview of quantitative tomographic imaging methods, potentially identifying the adverse side effects of cancer therapy in normal-appearing brain tissue. Seventy studies were obtained from the MEDLINE and Web of Science databases. Studies reporting changes in normal-appearing brain tissue using MRI, PET, or SPECT quantitative biomarkers, related to radio-, chemo-, immuno-, or hormone therapy for any kind of solid, cystic, or liquid tumor were included. The main findings of the reviewed studies were summarized, providing also the risk of bias of each study assessed using a modified QUADAS-2 tool. For each imaging method, this review provides the methodological background, and the benefits and shortcomings of each method from the imaging perspective. Finally, a set of recommendations is proposed to support future research.