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.Neuro Imaging Researc

An optimal acquisition and post-processing pipeline for hybrid IVIM-DKI in head and neck

Purpose: To optimize the diffusion-weighting b values and postprocessing pipeline for hybrid intravoxel incoherent motion diffusion kurtosis imaging in the head and neck region. Methods: Optimized diffusion-weighting b value sets ranging between 5 and 30 b values were constructed by optimizing the Cramér-Rao lower bound of the hybrid intravoxel incoherent motion diffusion kurtosis imaging model. With this model, the perfusion fraction, pseudodiffusion coefficient, diffusion coefficient, and kurtosis were estimated. Sixteen volunteers were scanned with a reference b value set and 3 repeats of the optimized sets, of which 1 with volunteers swallowing on purpose. The effects of (1) b value optim

APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

Purpose: To improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). Methods: APIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment. Results: Compared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results. Conclusion: Compared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging

Autocalibrated parallel imaging reconstruction with sampling pattern optimization for GRASE: APIR4GRASE

Purpose: To reduce artifacts and scan time of GRASE imaging by selecting an optimal sampling pattern and jointly reconstructing gradient echo and spin echo images. Methods: We jointly reconstruct images for the different echo types by considering these as additional virtual coil channels in the novel Autocalibrated Parallel Imaging Reconstruction with Sampling Pattern Optimization for GRASE (APIR4GRASE) method. Besides image reconstruction, we identify optimal sampling patterns for the acquisition. The selected optimal patterns were validated on phantom and in-vivo acquisitions. Comparison to the conventional GRASE without acceleration, and to the GRAPPA reconstruction with a single echo type was also performed. Results: Using identified optimal sampling patterns, APIR4GRASE eliminated modulation artifacts in both phantom and in-vivo experiments; mean square error (MSE) was reduced by 78% and 94%, respectively, compared to the conventional GRASE with similar scan time. Both artifacts and g-factor were reduced compared to the GRAPPA reconstruction with a single echo type. Conclusion: APIR4GRASE substantially improves the speed and quality of GRASE imaging over the state-of-the-art, and is able to reconstruct both spin echo and gradient echo images

Clinical performance and future potential of magnetic resonance thermometry in hyperthermia

Hyperthermia treatments in the clinic rely on accurate temperature measurements to guide treatments and evaluate clinical outcome. Currently, magnetic resonance thermometry (MRT) is the only clinical option to non-invasively measure 3D temperature distributions. In this review, we evaluate the status quo and emerging approaches in this evolving technology for replacing conventional dosimetry based on intraluminal or invasively placed probes. First, we define standard-ized MRT performance thresholds, aiming at facilitating transparency in this field when comparing MR temperature mapping performance for the various scenarios that hyperthermia is currently applied in the clinic. This is based upon our clinical experience of treating nearly 4000 patients with superficial and deep hyperthermia. Second, we perform a systematic literature review, assessing MRT performance in (I) clinical and (II) pre-clinical papers. From (I) we identify the current clinical status of MRT, including the problems faced and from (II) we extract promising new techniques with the potential to accelerate progress. From (I) we found that the basic requirements for MRT during hyperthermia in the clinic are largely met for regions without motion, for example extremities. In more challenging regions (abdomen and thorax), progress has been stagnating after the clinical introduction of MRT-guided hyperthermia over 20 years ago. One clear difficulty for advancement is that performance is not or not uniformly reported, but also that studies often omit important details regarding their approach. Motion was found to be the common main issue hindering accurate MRT. Based on (II), we reported and highlighted promising developments to tackle the issues resulting from motion (directly or indirectly), including new developments as well as optimization of already existing strategies. Combined, these may have the potential to facilitate improvement in MRT in the form of more stable and reliable measurements via better stability and accuracy

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

Dependency of R-2 and R-2* relaxation on Gd-DTPA concentration in arterial blood: influence of hematocrit and magnetic field strength

Dynamic susceptibility contrast (DSC) MRI is clinically used to measure brain perfusion by monitoring the dynamic passage of a bolus of contrast agent through the brain. For quantitative analysis of the DSC images, the arterial input function is required. It is known that the original assumption of a linear relation between the R-2((*)) relaxation and the arterial contrast agent concentration is invalid, although the exact relation is as of yet unknown. Studying this relation in vitro is time-consuming, because of the widespread variations in field strengths, MRI sequences, contrast agents, and physiological conditions. This study aims to simulate the R-2((*)) versus contrast concentration relation under varying physiological and technical conditions using an adapted version of an open-source simulation tool. The approach was validated with previously acquired data in human whole blood at 1.5 T by means of a gradient-echo sequence (proof-of-concept). Subsequently, the impact of hematocrit, field strength, and oxygen saturation on this relation was studied for both gradient-echo and spin-echo sequences. The results show that for both gradient-echo and spin-echo sequences, the relaxivity increases with hematocrit and field strength, while the hematocrit dependency was nonlinear for both types of MRI sequences. By contrast, oxygen saturation has only a minor effect. In conclusion, the simulation setup has proven to be an efficient method to rapidly calibrate and estimate the relation between R-2((*)) and gadolinium concentration in whole blood. This knowledge will be useful in future clinical work to more accurately retrieve quantitative information on brain perfusion.Cardiovascular Aspects of Radiolog

MR imaging for the quantitative assessment of brain iron in aceruloplasminemia: a postmortem validation study

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 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.Metals in Catalysis, Biomimetics & Inorganic Material

Technical challenges of quantitative chest MRI data analysis in a large cohort pediatric study

Objectives: This study was conducted in order to evaluate the effect of geometric distortion (GD) on MRI lung volume quantification and evaluate available manual, semi-automated, and fully automated methods for lung segmentation. Methods: A phantom was scanned with MRI and CT. GD was quantified as the difference in phantom’s volume between MRI and CT, with CT as gold standard. Dice scores were used to measure overlap in shapes. Furthermore, 11 subjects from a prospective population-based cohort study each underwent four chest MRI acquisitions. The resulting 44 MRI scans with 2D and 3D Gradwarp were used to test five segmentation methods. Intraclass correlation coefficient, Bland–Altman plots, Wilcoxon, Mann–Whitney U, and paired t tests were used for statistics. Results: Using phantoms, volume differences between CT and MRI varied according to MRI positions and 2D and 3D Gradwarp correction. With the phantom located at the isocenter, MRI overestimated the volume relative to CT by 5.56 ± 1.16 to 6.99 ± 0.22% with body and torso coils, respectively. Higher Dice scores and smaller intraobject differences were found for 3D Gradwarp MR images. In subjects, semi-automated and fully automated segmentation tools showed high agreement with manual segmentations (ICC = 0.971–0.993 for end-inspiratory scans; ICC = 0.992–0.995 for end-expiratory scans). Manual segmentation time per scan was approximately 3–4 h and 2–3 min for fully automated methods. Conclusions: Volume overestimation of MRI due to GD can be quantified. Semi-automated and fully automated segmentation methods allow accurate, reproducible, and fast lung volume quantification. Chest MRI can be a valid radiation-free imaging modality for lung segmentation and volume quantification in large cohort studies. Key Points: • Geometric distortion varies according to MRI setting and patient positioning. • Automated segmentation methods allow fast and accurate lung volume quantification. • MR

Compressed Sensing 3D-GRASE for faster High-Resolution MRI

textabstractPurpose: High-resolution three-dimensional (3D) structural MRI is useful for delineating complex or small structures of the body. However, it requires long acquisition times and high SAR, limiting its clinical use. The purpose of this work is to accelerate the acquisition of high-resolution images by combining compressed sensing and parallel imaging (CSPI) on a 3D-GRASE sequence and to compare it with a (CS)PI 3D-FSE sequence. Several sampling patterns were investigated to assess their influence on image quality. Methods: The proposed k-space sampling patterns are based on two undersampled k-space grids, variable density (VD) Poisson-disc, and VD pseudo-random Gaussian, and five different trajectories described in the literature. Bloch simulations are performed to obtain the transform point spread function and evaluate the coherence of each sampling pattern. Image resolution was assessed by the full-width at half-maximum (FWHM). Prospective CSPI 3D-GRASE phantom and in vivo experiments in knee and brain are carried out to assess image quality, SNR, SAR, and acquisition time compared to PI 3D-GRASE, PI 3D-FSE, and CSPI 3D-FSE acquisitions. Results: Sampling patterns with VD Poisson-disc obtain the lowest coherence for both PD-weighted and T2 -weighted acquisitions. VD pseudo-random Gaussian obtains lower FWHM, but higher sidelobes than VD Poisson-disc. CSPI 3D-GRASE reduces acquisition time (43% for PD-weighted and 40% for T2 -weighted) and SAR (∼45% for PD-weighted and T2 -weighted) compared to CSPI 3D-FSE. Conclusions: CSPI 3D-GRASE reduces acquisition time compared to a CSPI 3DFSE acquisition, preserving image quality. The design of the sampling pattern is crucial for image quality in CSPI 3D-GRASE image acquisitions