ABSTRACT Purpose Magnetic resonance elastography (MRE) of the heart has predominantly utilized breath‐held acquisitions with limited anatomic coverage. This work investigates the feasibility of 3D, free‐breathing cardiac MRE. Methods A 3D hybrid radial and EPI acquisition is utilized and combined with retrospective binning of k ‐space via physiologic monitoring, iterative reconstruction, MRE processing and inversions, and post‐processing specialized for cardiac imaging. Results Feasibility of free‐breathing stiffness mapping of the left ventricular myocardium was demonstrated in 11 participants. Myocardial stiffness estimates were obtained throughout the left ventricle for seven temporal states of the cardiac cycle. Stiffness estimates were presented in participant‐specific images, 17‐segment bullseye plots, and stiffness plots throughout the cardiac cycle. Results indicate increased stiffness in systolic states, with localized stiffness heterogeneity observed between participants, illustrating a key benefit of 3D cardiac MRE acquisition. Mean stiffness estimates obtained through 11 subjects were 4.44 ± 0.68 kPa (begin‐systole), 5.74 ± 0.94, 5.42 ± 0.87 (end‐systole), 3.84 ± 0.71 (begin‐diastole), 3.51 ± 0.74, 3.54 ± 0.52, and 3.55 ± 0.59 kPa (end‐diastole). Statistical testing indicated significant differences in stiffness across cardiac states with one‐way, repeated‐measures ANOVA ( p < 0.001) and Friedman's test ( p < 0.001). Experiments in scanning three participants with and without applied motion indicated that stiffness changes were more robustly detected in systole than in diastole, although patterns were subject‐specific. Comparison of free‐breathing stiffness measurements against reference breath‐held measurements yielded similar systolic stiffness estimates. Conclusions This study establishes the feasibility of free‐breathing, 3D cardiac MRE in healthy volunteers. With the current framework, stiffness estimation appears more robust in systole, and the reliability of diastolic estimates is limited and subject‐specific.
Generative diffusion model enables quantification of calibration‐free arterial spin labeling perfusion magnetic resonance imaging data in an Alzheimer's disease cohort
Abstract INTRODUCTION M0 images were missing in Siemens ASL data in Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, prohibiting cerebral blood flow (CBF) quantification. METHODS A conditional latent diffusion model was trained and evaluated on in‐house datasets, then applied to the Siemens data in ADNI‐3. Regional CBF differences by Alzheimer's disease (AD) stages, their accuracy for AD classification, and CBF trajectory slopes were compared between generated data (Siemens) and acquired data (General Electric). RESULTS The diffusion model generated M0 images with high fidelity (SSIM = 0.918 ± 0.023, PSNR = 31.361 ± 2.537) and minimal CBF bias (mean difference is 0.21 ± 1.58 mL/100 g/min). Both generated and acquired CBF showed similar spatial patterns and decreasing trends with AD progression in specific AD‐related regions. Generated CBF also improved accuracy in classifying AD stages compared to qualitative perfusion images. CONCLUSION This study shows the potential of diffusion models for imputing missing modalities in large‐scale studies exploring the use of ASL as a biomarker of AD. Highlights Using latent diffusion model, we can generate M0 image from control image in arterial spin labeling (ASL) with high fidelity. The generated M0 can be used for cerebral blood flow (CBF) quantification in Alzheimer's Disease Neuroimaging Initiative dataset. The performance of classification between Alzheimer's disease (AD) patients and cognitive normal people is better when using generated CBF maps than using non‐quantitative perfusion images. ASL CBF decreases with AD progression in key AD‐related brain regions.
Abstract The magnetic resonance imaging (MRI) Core has been operating since Alzheimer's Disease Neuroimaging Initiative's (ADNI) inception, providing 20 years of data including reliable, multi‐platform standardized protocols, carefully curated image data, and quantitative measures provided by expert investigators. The overarching purposes of the MRI Core include: (1) optimizing and standardizing MRI acquisition methods, which have been adopted by many multicenter studies and trials worldwide and (2) providing curated images and numeric summary values from relevant MRI sequences/contrasts to the scientific community. Over time, ADNI MRI has become increasingly complex. To remain technically current, the ADNI MRI protocol has changed substantially over the past two decades. The ADNI 4 protocol contains nine different imaging types (e.g., three dimensional [3D] T1‐weighted and fluid‐attenuated inversion recovery [FLAIR]). Our view is that the ADNI MRI data are a greatly underutilized resource. The purpose of this paper is to educate the scientific community on ADNI MRI methods and content to promote greater awareness, accessibility, and use. Highlights The MRI Core provides multi‐platform standardized protocols, carefully curated image data, and quantitative analysis by expert groups. The ADNI MRI protocol has undergone major changes over the past two decades to remain technically current. As of April 25, 2024, the following numbers of image series are available: 17,141 3D T1w; 6877 FLAIR; 3140 T2/PD; 6623 GRE; 3237 dMRI; 2846 ASL; 2968 TF‐fMRI; and 2861 HighResHippo (see Table 1 for abbreviations). As of April 25, 2024, the following numbers of quantitative analyses are available: FreeSurfer 10,997; BSI 6120; tensor based morphometry (TBM) and TBM‐SYN 12,019; WMH 9944; dMRI 1913; ASL 925; TF‐fMRI NFQ 2992; and medial temporal subregion volumes 2726 (see Table 4 for abbreviations). ADNI MRI is an underutilized resource that could be more useful to the research community.
AbstractPhase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI4) magnetic resonance imaging (MRI) protocols aim to maintain longitudinal consistency across two decades of data acquisition, while adopting new technologies. Here we describe and justify the study's design and targeted biomarkers. The ADNI4 MRI protocol includes nine MRI sequences. Some sequences require the latest hardware and software system upgrades and are continuously rolled out as they become available at each site. The main sequence additions/changes in ADNI4 are: (1) compressed sensing (CS) T1‐weighting, (2) pseudo‐continuous arterial spin labeling (ASL) on all three vendors (GE, Siemens, Philips), (3) multiple‐post‐labeling‐delay ASL, (4) 1 mm3 isotropic 3D fluid‐attenuated inversion recovery, and (5) CS 3D T2‐weighted. ADNI4 aims to help the neuroimaging community extract valuable imaging biomarkers and provide a database to test the impact of advanced imaging strategies on diagnostic accuracy and disease sensitivity among individuals lying on the cognitively normal to impaired spectrum.Highlights A summary of MRI protocols for phase four of the Alzheimer's Disease Neuroimaging Initiative (ADNI 4). The design and justification for the ADNI 4 MRI protocols. Compressed sensing and multi‐band advances have been applied to improve scan time. ADNI4 protocols aim to streamline safety screening and therapy monitoring. The ADNI4 database will be a valuable test bed for academic research.
Sex Differences in Aging-related Myocardial Stiffening Quantitatively Measured with MR Elastography
Abstract Functional MRI (fMRI) is widely used to spatially localize neural activity in the brain associated with functional stimuli. Functional MR Elastography (fMRE) has recently been introduced as a complementary approach that measures the mechanical response to functional stimulus. The hypothesis of the current study is that the stiffness change in fMRE is proportional to the underlying neural activity. This hypothesis is tested by measuring the median stiffness change in the visual cortex as a function of luminance-matched contrast intensity of a checkerboard visual stimulus in 16 healthy subjects. The fMRE signal in the visual cortex was observed to be proportional to the contrast intensity of the visual stimulus. In regions of activation, fMRE signal increased in the range of 2 ± 1% to 5.8 ± 1% and fMRI signal increased by the expected 0.4 ± 0.2% to 0.9 ± 0.2%, for contrast levels of 5% to 100%, respectively. In conclusion, this study shows that the fMRE signal in the visual cortex can be directly modulated by the contrast intensity of a visual stimulus. The presence of some overlap between fMRI and fMRE regions of activation may suggest two distinct mechanisms governing the fMRI and fMRE signals, which will be investigated in future studies.
Magnetic Susceptibility in Progressive Supranuclear Palsy Variants, Parkinson's Disease, and Corticobasal Syndrome
BackgroundLocalized regions of left–right image intensity asymmetry (LRIA) were incidentally observed on T2‐weighted (T2‐w) and T1‐weighted (T1‐w) diagnostic magnetic resonance imaging (MRI) images. Suspicion of herpes encephalitis resulted in unnecessary follow‐up imaging. A nonbiological imaging artifact that can lead to diagnostic uncertainty was identified.PurposeTo investigate whether systematic LRIA exist for a range of scanner models and to determine if LRIA can introduce diagnostic uncertainty.Study TypeA retrospective study using the Alzheimer's Disease Neuroimaging Initiative (ADNI) data base.SubjectsOne thousand seven hundred fifty‐three (median age: 72, males/females: 878/875) unique participants with longitudinal data were included.Field Strength3T.SequencesT1‐w three‐dimensional inversion‐recovery spoiled gradient‐echo (IR‐SPGR) or magnetization‐prepared rapid gradient‐echo (MP‐RAGE) and T2‐w fluid‐attenuated inversion recovery (FLAIR) long tau fast spin echo inversion recovery (LT‐FSE‐IR). Only General Electric, Philips, and Siemens' product sequences were used.AssessmentLRIA was calculated as the left–right percent difference with respect to the mean intensity from automated anatomical atlas segmented regions. Three neuroradiologists with 37 (**), 32 (**), and 3 (**) years of experience rated the clinical impact of 30 T2‐w three‐dimensional FLAIR exams with LRIA to determine the diagnostic uncertainty. Statistical comparisons between retrospective intensity normalized T1m and original T1‐w images were made.Statistical TestsFor each image type, a linear mixed effects model was fit using LRIA scores from all scanners, regions, and participants as the outcome and age and sex as predictors. Statistical significance was defined as having a P‐value <0.05.ResultsLRIA scores were significantly different from zero on most scanners. All clinicians were uncertain or recommended definite diagnostic follow‐up in 62.5% of cases with LRIA >10%. Individuals with acute brain pathology or focal neurologic deficits are not enrolled in ADNI; therefore, focal signal abnormalities were considered false positives.Data ConclusionLRIA is system specific, systematic, creates diagnostic uncertainty, and impacts IR‐SPGR, MP‐RAGE, and LT‐FSE‐IR product sequences.Level of Evidence: 2Technical Efficacy Stage: 3
MRI quantitative susceptibility mapping of the substantia nigra as an early biomarker for Lewy body disease
ABSTRACT Background and Purpose Neurodegeneration of the substantia nigra in Lewy body disease is associated with iron deposition, which increases the magnetic susceptibility of the substantia nigra on MRI. Our objective was to measure iron deposition in the substantia nigra in patients with probable dementia with Lewy bodies (pDLB) and patients who are at risk for pDLB by quantitative susceptibility mapping (QSM). Methods Participants included pDLB ( n = 36), mild cognitive impairment with at least one core feature of DLB (MCI‐LB; n = 15), idiopathic rapid eye movement sleep behavior disorder (iRBD; n = 11), and an age‐and gender‐matched clinically unimpaired control group ( n = 102). QSM was derived from multi‐echo 3D gradient recalled echo MRI at 3T, and groups were compared on mean susceptibility values of the substantia nigra and its relation to parkinsonism severity. Results Patients with pDLB had higher susceptibility in the substantia nigra compared to controls ( p < 0.001) and MCI‐LB ( p = 0.043). The susceptibility of substantia nigra showed an increasing trend from controls to iRBD and MCI‐LB, and to pDLB ( p < 0.001). Parkinsonism severity was not associated with the mean susceptibility in the substantia nigra in the patient groups. Conclusions Our data suggested that QSM is sensitive to the increased magnetic susceptibility due to higher iron content in the substantia nigra in pDLB. The trend of increasing susceptibility from controls to iRBD and MCI‐LB, and to pDLB suggests that iron deposition in the substantia nigra starts to increase as early as the prodromal stage in DLB and continues to increase as the disease progresses, independent of parkinsonism severity.
Harnessing brain waves: a review of brain magnetic resonance elastography for clinicians and scientists entering the field
Brain magnetic resonance elastography (MRE) is an imaging technique capable of accurately and non-invasively measuring the mechanical properties of the living human brain. Recent studies have shown that MRE has potential to provide clinically useful information in patients with intracranial tumors, demyelinating disease, neurodegenerative disease, elevated intracranial pressure, and altered functional states. The objectives of this review are: (1) to give a general overview of the types of measurements that have been obtained with brain MRE in patient populations, (2) to survey the tools currently being used to make these measurements possible, and (3) to highlight brain MRE-based quantitative biomarkers that have the highest potential of being adopted into clinical use within the next 5 to 10 years. The specifics of MRE methodology strategies are described, from wave generation to material parameter estimations. The potential clinical role of MRE for characterizing and planning surgical resection of intracranial tumors and assessing diffuse changes in brain stiffness resulting from diffuse neurological diseases and altered intracranial pressure are described. In addition, the emerging technique of functional MRE, the role of artificial intelligence in MRE, and promising applications of MRE in general neuroscience research are presented.