Neuromelanin-MRI using 2D GRE and deep learning: considerations for improving the visualization of substantia nigra and locus coeruleus

An optimized clinically feasible neuromelanin-MRI imaging protocol for visualising the SN and LC simultaneously using deep learning reconstruction is presented. We optimize flip-angle for optimal combined SN and LC depiction. We also experimented with combinations of anisotropic and isotropic in-plane resolution, partial vs full echoes and the number of averages. Phantom and in-vivo experiments on three healthy volunteers illustrate that high-resolution imaging combined with deep-learning denoising shows good depiction of the SN and LC with a clinically feasible sequence of around 7 minutes.Comment: An article based on ECR and ISMRM abstracts, with more text & figure

Metagenomics reveals our incomplete knowledge of global diversity

Financial support was provided by projects BFU2006-06003 from the Spanish Ministry of Education and Science (MEC) and GV/2007/050 from the Generalitat Valenciana, Spain. J.T. is a recipient of a contract in the FIS Program from ISCIII, Spanish Ministry of Health.Pignatelli, M.; Aparicio Pla, G.; Blanquer Espert, I.; Hernández García, V.; Moya, A.; Tamames, J. (2008). Metagenomics reveals our incomplete knowledge of global diversity. Bioinformatics. 24(18):2124-2125. https://doi.org/10.1093/bioinformatics/btn355S212421252418Koski, L. B., & Golding, G. B. (2001). The Closest BLAST Hit Is Often Not the Nearest Neighbor. Journal of Molecular Evolution, 52(6), 540-542. doi:10.1007/s002390010184Krause, L., Diaz, N. N., Goesmann, A., Kelley, S., Nattkemper, T. W., Rohwer, F., … Stoye, J. (2008). Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Research, 36(7), 2230-2239. doi:10.1093/nar/gkn038Mavromatis, K., Ivanova, N., Barry, K., Shapiro, H., Goltsman, E., McHardy, A. C., … Kyrpides, N. C. (2007). Use of simulated data sets to evaluate the fidelity of metagenomic processing methods. Nature Methods, 4(6), 495-500. doi:10.1038/nmeth1043Tamames, J., & Moya, A. (2008). Estimating the extent of horizontal gene transfer in metagenomic sequences. BMC Genomics, 9(1), 136. doi:10.1186/1471-2164-9-136Tringe, S. G. (2005). Comparative Metagenomics of Microbial Communities. Science, 308(5721), 554-557. doi:10.1126/science.1107851Tringe, S. G., Zhang, T., Liu, X., Yu, Y., Lee, W. H., Yap, J., … Ruan, Y. (2008). The Airborne Metagenome in an Indoor Urban Environment. PLoS ONE, 3(4), e1862. doi:10.1371/journal.pone.0001862COLE, F. N. (1900). AMERICAN MATHEMATICAL SOCIETY. Science, 11(263), 66-67. doi:10.1126/science.11.263.6

Fast pseudo-CT synthesis from MRI T1-weighted images using a patch-based approach

MRI-based bone segmentation is a challenging task because bone tissue and air both present low signal intensity on MR images, making it difficult to accurately delimit the bone boundaries. However, estimating bone from MRI images may allow decreasing patient ionization by removing the need of patient-specific CT acquisition in several applications. In this work, we propose a fast GPU-based pseudo-CT generation from a patient-specific MRI T1-weighted image using a group-wise patch-based approach and a limited MRI and CT atlas dictionary. For every voxel in the input MR image, we compute the similarity of the patch containing that voxel with the patches of all MR images in the database, which lie in a certain anatomical neighborhood. The pseudo-CT is obtained as a local weighted linear combination of the CT values of the corresponding patches. The algorithm was implemented in a GPU. The use of patch-based techniques allows a fast and accurate estimation of the pseudo-CT from MR T1-weighted images, with a similar accuracy as the patient-specific CT. The experimental normalized cross correlation reaches 0.9324±0.0048 for an atlas with 10 datasets. The high NCC values indicate how our method can accurately approximate the patient-specific CT. The GPU implementation led to a substantial decrease in computational time making the approach suitable for real applications

Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

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 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.Radiolog

A neuroradiologist's guide to arterial spin labeling MRI in clinical practice

Arterial spin labeling (ASL) is a non-invasive MRI technique to measure cerebral blood flow (CBF). This review provides a practical guide and overview of the clinical applications of ASL of the brain, as well its potential pitfalls. The technical and physiological background is also addressed. At present, main areas of interest are cerebrovascular disease, dementia and neuro-oncology. In cerebrovascular disease, ASL is of particular interest owing to its quantitative nature and its capability to determine cerebral arterial territories. In acute stroke, the source of the collateral blood supply in the penumbra may be visualised. In chronic cerebrovascular disease, the extent and severity of compromised cerebral perfusion can be visualised, which may be used to guide therapeutic or preventative intervention. ASL has potential for the detection and follow-up of arteriovenous malformations. In the workup of dementia patients, ASL is proposed as a diagnostic alternative to PET. It can easily be added to the routinely performed structural MRI examination. In patients with established Alzheimer's disease and frontotemporal dementia, hypoperfusion patterns are seen that are similar to hypometabolism patterns seen with PET. Studies on ASL in brain tumour imaging indicate a high correlation between areas of increased CBF as measured with ASL and increased cerebral blood volume as measured with dynamic susceptibility contrast-enhanced perfusion imaging. Major advantages of ASL for brain tumour imaging are the fact that CBF measurements are not influenced by breakdown of the blood-brain barrier, as well as its quantitative nature, facilitating multicentre and longitudinal studies

Implementation and validation of ASL perfusion measurements for population imaging

Purpose: Pseudocontinuous arterial spin labeling (pCASL) allows for noninvasive measurement of regional cerebral blood flow (CBF), which has the potential to serve as biomarker for neurodegenerative and cardiovascular diseases. This work aimed to implement and validate pCASL on the dedicated MRI system within the population-based Rotterdam Study, which was installed in 2005 and for which software and hardware configurations have remained fixed. Methods: Imaging was performed on two 1.5T MRI systems (General Electric); (I) the Rotterdam Study system, and (II) a hospital-based system with a product pCASL sequence. An in-house implementation of pCASL was created on scanner I. A flow phantom and three healthy volunteers (<27 years) were scanned on both systems for validation purposes. The data of the first 30 participants (86 ± 4 years) of the Rotterdam Study undergoing pCASL scans on scanner I only were analyzed with and without partial volume correction for gray matter. Results: The validation study showed a difference in blood flow velocity, sensitivity, and spatial coefficient of variation of the perfusion-weighted signal between the two scanners, which was accounted for during post-processing. Gray matter CBF for the Rotterdam Study participants was 52.4 ± 8.2 ml/100 g/min, uncorrected for partial volume effects of gray matter. In this elderly cohort, partial volume correction for gray matter had a variable effect on measured CBF in a range of cortical and sub-cortical regions of interest. Conclusion: Regional CBF measurements are now included to investigate novel biomarkers in the Rotterdam Study. This work highlights that when it is not feasible to purchase a novel ASL sequence, an in-house implementation is valuable

Partial volume correction in arterial spin labeling perfusion MRI: A method to disentangle anatomy from physiology or an analysis step too far?

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), 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

Fractional order vs. exponential fitting in UTE MR imaging of the patellar tendon

Purpose: Quantification of the T2 ∗ relaxation time constant is relevant in various magnetic resonance imaging applications. Mono- or bi-exponential models are typically used to determine these parameters. However, in case of complex, heterogeneous tissues these models could lead to inaccurate results. We compared a model, provided by the fractional-order extension of the Bloch equation with the conventional models. Methods: Axial 3D ultra-short echo time (UTE) scans were acquired using a 3.0 T MRI and a 16-channel surface coil. After image registration, voxel-wise T2 ∗ was quantified with mono-exponential, bi-exponential and fractional-order fitting. We evaluated all three models repeatability and the bias of their derived parameters by fitting at various noise levels. To investigate the effect of the SNR for the different models, a Monte-Carlo experiment with 1000 repeats was performed for different noise levels for one subject. For a cross-sectional investigation, we used the mean fitted values of the ROIs in five volunteers. Results: Comparing the mono-exponential and the fractional order T2 ∗ maps, the fractional order fitting method yielded enhanced contrast and an improved delineation of the different tissues. In the case of the bi-exponential method, the long T2 ∗ component map demonstrated the anatomy clearly with high contrast. Simulations showed a nonzero bias of the parameters for all three mathematical models. ROI based fitting showed that the T2 ∗ values were different depending on the applied method, and they differed most for the patellar tendon in all subjects. Conclusions: In high SNR cases, the fractional order and bi-exponential models are both performing well with low bias. However, in all observed cases, one of the bi-exponential components has high standard deviation in T2 ∗. The bi-exponential model is suitable for T2 ∗ mapping, but we recommend using the fractional order model for cases of low SNR

T2mapping of healthy knee cartilage: Multicenter multivendor reproducibility

Background: T2 mapping is increasingly used to quantify cartilage degeneration in knee osteoarthritis (OA), yet reproducibility studies in a multicenter setting are limited. The purpose of this study was to determine the longitudinal reproducibility and multicenter variation of cartilage T2 mapping, using various MRI equipment and acquisition protocols. Methods: In this prospective multicenter study, four traveling, healthy human subjects underwent T2 mapping twice at five different centers with a 6-month-interval. Centers had various MRI scanners, field strengths, and T2 mapping acquisition protocols. Mean T2 values were calculated in six cartilage regions of interest (ROIs) as well as an average value per patient. A phantom was scanned once at each center. To evaluate longitudinal reproducibility, intraclass correlation coefficients (ICC), root-mean-square coefficient of variation (RMS-CV), and a Bland-Altman plot were used. To assess the variation of in vivo and phantom T2 values across centers, ANOVA was performed. Results: ICCs of the T2 mapping measurements per ROI and the ROI's combined ranged from 0.73 to 0.91, indicating good to excellent longitudinal reproducibility. RMS-CVs ranged from 1.1% to 1.5% (per ROI) and 0.6% to 1.6% (ROIs combined) across the centers. A Bland-Altman plot did not reveal a systematic error. Evident, but consistent, discrepancies in T2values were observed across centers, both in vivo and in the phantom. Conclusions: The results of this study suggest that T2mapping can be used to longitudinal assess cartilage degeneration in multicenter studies. Given the differences in absolute cartila