MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the... Show moreMR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estima-tion for identified components as well as partial volume segmentations. The aim of this paper was to evaluate the accuracy and repeatability of the SPIJN-MRF parameter estimations and partial volume segmentations. This was done (1) through numerical simulations based on the BrainWeb phantoms and (2) using in vivo acquired MRF data from 5 subjects that were scanned on the same week-day for 8 consecutive weeks. The partial volume segmen-tations of the SPIJN-MRF method were compared to those obtained by two conventional methods: SPM12 and FSL. SPIJN-MRF showed higher accuracy in simulations in comparison to FSL-and SPM12-based segmentations: Fuzzy Tanimoto Coefficients (FTC) comparing these segmentations and Brainweb references were higher than 0.95 for SPIJN-MRF in all the tissues and between 0.6 and 0.7 for SPM12 and FSL in white and gray matter and between 0.5 and 0.6 in CSF. For the in vivo MRF data, the estimated relaxation times were in line with literature and minimal variation was observed. Furthermore, the coefficient of variation (CoV) for estimated tissue volumes with SPIJN-MRF were 10.5% for the myelin water, 6.0% for the white matter, 5.6% for the gray matter, 4.6% for the CSF and 1.1% for the total brain volume. CoVs for CSF and total brain volume measured on the scanned data for SPIJN-MRF were in line with those obtained with SPM12 and FSL. The CoVs for white and gray mat-ter volumes were distinctively higher for SPIJN-MRF than those measured with SPM12 and FSL. In conclusion, the use of SPIJN-MRF provides accurate and precise tissue relaxation parameter estimations taking into account intrinsic partial volume effects. It facilitates obtaining tissue fraction maps of prevalent tissues including myelin water which can be relevant for evaluating diseases affecting the white matter. Show less
Aims: Non-invasive measures of brain iron content would be of great benefit in neurodegeneration with brain iron accumulation (NBIA) to serve as a biomarker for disease progression and evaluation... Show moreAims: Non-invasive measures of brain iron content would be of great benefit in neurodegeneration with brain iron accumulation (NBIA) to serve as a biomarker for disease progression and evaluation of iron chelation therapy. Although magnetic resonance imaging (MRI) provides several quantitative measures of brain iron content, none of these have been validated for patients with a severely increased cerebral iron burden. We aimed to validate R 2 * as a quantitative measure of brain iron content in aceruloplasminemia, the most severely iron-loaded NBIA phenotype. Methods: Tissue samples from 50 gray-and white matter regions of a postmortem aceruloplasminemia brain and control subject were scanned at 1.5 T to obtain R 2 * , and biochemically analyzed with inductively coupled plasma mass spectrometry. For gray matter samples of the aceruloplasminemia brain, sample R 2 * values were compared with postmortem in situ MRI data that had been obtained from the same subject at 3 T - in situ R 2 * . Relationships between R 2 * and tissue iron concentration were determined by linear regression analyses. Results: Median iron concentrations throughout the whole aceruloplasminemia brain were 10 to 15 times higher than in the control subject, and R 2 * was linearly associated with iron concentration. For gray matter samples of the aceruloplasminemia subject with an iron concentration up to 1000 mg/kg, 91% of variation in R 2 * could be explained by iron, and in situ R 2 * at 3 T and sample R 2 * at 1.5 T were highly correlated. For white matter regions of the aceruloplasminemia brain, 85% of variation in R 2 * could be explained by iron. Conclusions: R 2 * is highly sensitive to variations in iron concentration in the severely iron-loaded brain, and might be used as a non-invasive measure of brain iron content in aceruloplasminemia and potentially other NBIA disorders. Show less
Aims: Non-invasive measures of brain iron content would be of great benefit in neurodegeneration with brain iron accumulation (NBIA) to serve as a biomarker for disease progression and evaluation... Show moreAims: Non-invasive measures of brain iron content would be of great benefit in neurodegeneration with brain iron accumulation (NBIA) to serve as a biomarker for disease progression and evaluation of iron chelation therapy. Although magnetic resonance imaging (MRI) provides several quantitative measures of brain iron content, none of these have been validated for patients with a severely increased cerebral iron burden. We aimed to validate R 2 * as a quantitative measure of brain iron content in aceruloplasminemia, the most severely iron-loaded NBIA phenotype. Methods: Tissue samples from 50 gray-and white matter regions of a postmortem aceruloplasminemia brain and control subject were scanned at 1.5 T to obtain R 2 * , and biochemically analyzed with inductively coupled plasma mass spectrometry. For gray matter samples of the aceruloplasminemia brain, sample R 2 * values were compared with postmortem in situ MRI data that had been obtained from the same subject at 3 T - in situ R 2 * . Relationships between R 2 * and tissue iron concentration were determined by linear regression analyses. Results: Median iron concentrations throughout the whole aceruloplasminemia brain were 10 to 15 times higher than in the control subject, and R 2 * was linearly associated with iron concentration. For gray matter samples of the aceruloplasminemia subject with an iron concentration up to 1000 mg/kg, 91% of variation in R 2 * could be explained by iron, and in situ R 2 * at 3 T and sample R 2 * at 1.5 T were highly correlated. For white matter regions of the aceruloplasminemia brain, 85% of variation in R 2 * could be explained by iron. Conclusions: R 2 * is highly sensitive to variations in iron concentration in the severely iron-loaded brain, and might be used as a non-invasive measure of brain iron content in aceruloplasminemia and potentially other NBIA disorders. Show less
Chappell, M.A.; McConnell, F.A.K.; Golay, X.; Gunther, M.; Hernandez-Tamames, J.A.; Osch, M.J. van; Asllani, I. 2021
The mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE),... Show moreThe mismatch in the spatial resolution of Arterial Spin Labeling (ASL) MRI perfusion images and the anatomy of functionally distinct tissues in the brain leads to a partial volume effect (PVE), which in turn confounds the estimation of perfusion into a specific tissue of interest such as gray or white matter. This confound occurs because the image voxels contain a mixture of tissues with disparate perfusion properties, leading to estimated perfusion values that reflect primarily the volume proportions of tissues in the voxel rather than the perfusion of any particular tissue of interest within that volume. It is already recognized that PVE influences studies of brain perfusion, and that its effect might be even more evident in studies where changes in perfusion are co-incident with alterations in brain structure, such as studies involving a comparison between an atrophic patient population vs control subjects, or studies comparing subjects over a wide range of ages. However, the application of PVE correction (PVEc) is currently limited and the employed methodologies remain inconsistent. In this article, we outline the influence of PVE in ASL measurements of perfusion, explain the main principles of PVEc, and provide a critique of the current state of the art for the use of such methods. Furthermore, we examine the current use of PVEc in perfusion studies and whether there is evidence to support its wider adoption. We conclude that there is sound theoretical motivation for the use of PVEc alongside conventional, 'uncorrected', images, and encourage such combined reporting. Methods for PVEc are now available within standard neuroimaging toolboxes, which makes our recommendation straightforward to implement. However, there is still more work to be done to establish the value of PVEc as well as the efficacy and robustness of existing PVEc methods. Show less
