A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI

Arterial spin labeling (ASL) MRI is a non-invasive technique for the quantification of cerebral perfusion, and pseudo-continuous arterial spin labeling (PCASL) has been recommended as the standard implementation by a recent consensus of the community. Due to the low spatial resolution of ASL images, perfusion quantification is biased by partial volume effects. Consequently, several partial volume correction (PVEc) methods have been developed to reduce the bias in gray matter (GM) perfusion quantification. The efficacy of these methods relies on both the quality of the ASL data and the accuracy of partial volume estimates. Here we systematically investigate the sensitivity of different PVEc methods to variability in both the ASL data and partial volume estimates using simulated PCASL data and in vivo PCASL data from a reproducibility study. We examined the PVEc methods in two ways: the ability to preserve spatial details and the accuracy of GM perfusion estimation. Judging by the root-mean-square error (RMSE) between simulated and estimated GM CBF, the spatially regularized method was superior in preserving spatial details compared to the linear regression method (RMSE of 1.2 vs 5.1 in simulation of GM CBF with short scale spatial variations). The linear regression method was generally less sensitive than the spatially regularized method to noise in data and errors in the partial volume estimates (RMSE 6.3 vs 23.4 for SNR = 5 simulated data), but this could be attributed to the greater smoothing introduced by the method. Analysis of a healthy cohort dataset indicates that PVEc, using either method, improves the repeatability of perfusion quantification (within-subject coefficient of variation reduced by 5% after PVEc)

Sensitivity of partial volume correction methods for pcASL MRI

This deposit contains the data underlying the figures found in the paper: "A Systematic Study of the Sensitivity of Partial Volume Correction Methods for the Quantification of Perfusion from Pseudo-continuous Arterial Spin Labeling MRI". Full details of these data were generated, including the methods by which simulated images were generated, can be found therein. The files are all Excel spreadsheets containing the values underlying the quantitative results figures within the above work

Sensitivity of partial volume correction methods for pcASL MRI

This deposit contains the data underlying the figures found in the paper: "A Systematic Study of the Sensitivity of Partial Volume Correction Methods for the Quantification of Perfusion from Pseudo-continuous Arterial Spin Labeling MRI". Full details of these data were generated, including the methods by which simulated images were generated, can be found therein. The files are all Excel spreadsheets containing the values underlying the quantitative results figures within the above work

A visual quality control scale for clinical arterial spin labeling images

BACKGROUND: Image-quality assessment is a fundamental step before clinical evaluation of magnetic resonance images. The aim of this study was to introduce a visual scoring system that provides a quality control standard for arterial spin labeling (ASL) and that can be applied to cerebral blood flow (CBF) maps, as well as to ancillary ASL images. METHODS: The proposed image quality control (QC) system had two components: (1) contrast-based QC (cQC), describing the visual contrast between anatomical structures; and (2) artifact-based QC (aQC), evaluating image quality of the CBF map for the presence of common types of artifacts. Three raters evaluated cQC and aQC for 158 quantitative signal targeting with alternating radiofrequency labelling of arterial regions (QUASAR) ASL scans (CBF, T1 relaxation rate, arterial blood volume, and arterial transient time). Spearman correlation coefficient (r), intraclass correlation coefficients (ICC), and receiver operating characteristic analysis were used. RESULTS: Intra/inter-rater agreement ranged from moderate to excellent; inter-rater ICC was 0.72 for cQC, 0.60 for aQC, and 0.74 for the combined QC (cQC + aQC). Intra-rater ICC was 0.90 for cQC; 0.80 for aQC, and 0.90 for the combined QC. Strong correlations were found between aQC and CBF maps quality (r = 0.75), and between aQC and cQC (r = 0.70). A QC score of 18 was optimal to discriminate between high and low quality clinical scans. CONCLUSIONS: The proposed QC system provided high reproducibility and a reliable threshold for discarding low quality scans. Future research should compare this visual QC system with an automatic QC system

A visual quality control scale for clinical arterial spin labeling images

Background: Image-quality assessment is a fundamental step before c linical evaluation of mag netic resonance images. The aim of this study was to introduce a vi sual scoring system that provides a qual ity control standard for arterial spin labeling (ASL) and that can be applied to cerebral blood flow (CBF) maps, as well as to ancillary ASL images. Methods: The proposed image quality control (QC) system had two components: (1) contrast-based QC (cQC), describing the visual contrast between anatomical structures; a nd (2) artifact-based QC (aQC), evaluating image quality of theCBFmapforthepresenceofcommontypesofartifacts. Three raters evaluated cQC an d aQC for 158 quantitative signal targeting with alternating radiofrequency labelling o f arterial regions (QUASAR) ASL scans (CBF, T1 relaxation rate, arterial blood volume, and arterial transie nt time). Spearman correlation coefficient ( r ), intraclass correlation coefficients (ICC), and receiver operating characteristic analysis were used. Results: Intra/inter-rater agreement ranged from moderate to excellent; inter-rater ICC was 0.72 for cQC, 0.60 for aQC, and 0.74 for the combined QC (cQC + aQC). Intra-rater ICC was 0.90 for cQC; 0.80 for aQC, and 0.90 for the combined QC. Strong correlations were found between aQC and CBF maps quality ( r = 0.75), and between aQC and cQC ( r = 0.70). A QC score of 18 was optimal to discriminate between high and low quality clinical scans. Conclusions: The proposed QC system provided high reproducibility and a reliable threshold for discarding low quality scans. Future research should compare this v isualQCsystemwithanautomaticQCsystem

Toblerone: Surface-Based Partial Volume Estimation

Partial volume effects (PVE) present a source of confound for the analysis of functional imaging data. Correction for PVE requires estimates of the partial volumes (PVs) present in an image. These estimates are conventionally obtained via volumetric segmentation, but such an approach may not be accurate for complex structures such as the cortex. An alternative is to use surface-based segmentation, which is well-established within the literature. Toblerone is a new method for estimating PVs using such surfaces. It uses a purely geometric approach that considers the intersection between a surface and the voxels of an image. In contrast to existing surface-based techniques, Toblerone is not restricted to use with any particular structure or modality. Evaluation in a neuroimaging context has been performed on simulated surfaces, simulated T1-weighted MRI images and finally a Human Connectome Project test-retest dataset. A comparison has been made to two existing surface-based methods; in all analyses Toblerone's performance either matched or surpassed the comparator methods. Evaluation results also show that compared to an existing volumetric method (FSL FAST), a surface-based approach with Toblerone offers improved robustness to scanner noise and field non-uniformity, and better inter-session repeatability in brain volume. In contrast to volumetric methods, a surface-based approach negates the need to perform resampling which is advantageous at the resolutions typically used for neuroimaging

BASIL: A Toolbox for Perfusion Quantification using Arterial Spin Labelling

Arterial Spin Labelling (ASL) MRI is now an established non-invasive method to quantify cerebral blood flow and is increasingly being used in a variety of neuroimaging applications. With standard ASL acquisition protocols widely available, there is a growing interest in advanced options that offer added quantitative precision and information about haemodynamics beyond perfusion. In this article we introduce the BASIL toolbox, a research tool for the analysis of ASL data included within the FMRIB Software Library (FSL) and explain its operation in a variety of typical use cases. BASIL is not offered as a clinical tool, and nor is this work intended to guide the clinical application of ASL. Built around a Bayesian model-based inference algorithm, the toolbox is designed to quantify perfusion and other haemodynamic measures, such as arterial transit times, from a variety of possible ASL input data, particularly exploiting the information available in more advanced multi-delay acquisitions. At its simplest, the BASIL toolbox offers a graphical user interface that provides the analysis options needed by most users; through command line tools, it offers more bespoke options for users needing customised analyses. As part of FSL, the toolbox exploits a range of complementary neuroimaging analysis tools so that ASL data can be easily integrated into neuroimaging studies and used alongside other modalities

Changes in volumetric and metabolic parameters relate to differences in exposure to sub-concussive head impacts

Structural and calibrated magnetic resonance imaging data were acquired on 44 collegiate football players prior to the season (PRE), following the first four weeks in-season (PTC) and one month after the last game (POST). Exposure data collected from g-Force accelerometers mounted to the helmet of each player were used to split participants into HIGH (N = 22) and LOW (N = 22) exposure groups, based on the frequency of impacts sustained by each athlete. Significant decreases in grey-matter volume specific to the HIGH group were documented at POST (P = 0.009), compared to baseline. Changes in resting cerebral blood flow (CBF0), corrected for partial volume effects, were observed within the HIGH group, throughout the season (P < 0.0001), suggesting that alterations in perfusion may follow exposure to sub-concussive collisions. Co-localized significant increases in cerebral metabolic rate of oxygen consumption (CMRO2|0) mid-season were also documented in the HIGH group, with respect to both PRE- and POST values. No physiological changes were observed in the LOW group. Therefore, cerebral metabolic demand may be elevated in players with greater exposure to head impacts. These results provide novel insight into the effects of sub-concussive collisions on brain structure and cerebrovascular physiology and emphasize the importance of multi-modal imaging for a complete characterization of cerebral health

A systematic study of the sensitivity of partial volume correction methods for the quantification of perfusion from pseudo-continuous arterial spin labeling MRI

Arterial spin labeling (ASL) MRI is a non-invasive technique for the quantification of cerebral perfusion, and pseudo-continuous arterial spin labeling (PCASL) has been recommended as the standard implementation by a recent consensus of the community. Due to the low spatial resolution of ASL images, perfusion quantification is biased by partial volume effects. Consequently, several partial volume correction (PVEc) methods have been developed to reduce the bias in gray matter (GM) perfusion quantification. The efficacy of these methods relies on both the quality of the ASL data and the accuracy of partial volume estimates. Here we systematically investigate the sensitivity of different PVEc methods to variability in both the ASL data and partial volume estimates using simulated PCASL data and in vivo PCASL data from a reproducibility study. We examined the PVEc methods in two ways: the ability to preserve spatial details and the accuracy of GM perfusion estimation. Judging by the root-mean-square error (RMSE) between simulated and estimated GM CBF, the spatially regularized method was superior in preserving spatial details compared to the linear regression method (RMSE of 1.2 vs 5.1 in simulation of GM CBF with short scale spatial variations). The linear regression method was generally less sensitive than the spatially regularized method to noise in data and errors in the partial volume estimates (RMSE 6.3 vs 23.4 for SNR=5 simulated data), but this could be attributed to the greater smoothing introduced by the method. Analysis of a healthy cohort dataset indicates that PVEc, using either method, improves the repeatability of perfusion quantification (within-subject coefficient of variation reduced by 5% after PVEc)