Mean Apparent Propagator MRI Research Articles

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

Correspondence of mean apparent propagator MRI metrics with phosphorylated tau and astrogliosis in chronic traumatic encephalopathy.

Chronic traumatic encephalopathy is a neurodegenerative disease that is diagnosed and staged based on the localization and extent of phosphorylated tau pathology. Although its identification remains the primary diagnostic criteria to distinguish chronic traumatic encephalopathy from other tauopathies, the hyperphosphorylated tau that accumulates in neurofibrillary tangles in cortical grey matter and perivascular regions is often accompanied by concomitant pathology such as astrogliosis. Mean apparent propagator MRI is a clinically feasible diffusion MRI method that is suitable to characterize microstructure of complex biological media efficiently and comprehensively. We performed quantitative correlations between propagator metrics and underlying phosphorylated tau and astroglial pathology in a cross-sectional study of 10 ex vivo human tissue specimens with 'high chronic traumatic encephalopathy' at 0.25 mm isotropic voxels. Linear mixed effects analysis of regions of interest showed significant relationships of phosphorylated tau with propagator-estimated non-Gaussianity in cortical grey matter (P = 0.002) and of astrogliosis with propagator anisotropy in superficial cortical white matter (P = 0.0009). The positive correlation between phosphorylated tau and non-Gaussianity was found to be modest but significant (R2 = 0.44, P = 6.0 × 10-5) using linear regression. We developed an unsupervised clustering algorithm with non-Gaussianity and propagator anisotropy as inputs, which was able to identify voxels in superficial cortical white matter that corresponded to astrocytes that were accumulated at the grey-white matter interface. Our results suggest that mean apparent propagator MRI at high spatial resolution provides a means to not only identify phosphorylated tau pathology but also detect regions with astrocytic pathology and may therefore prove diagnostically valuable in the evaluation of concomitant pathology in cortical tissue with complex microstructure.

Improving Early Detection of White Matter Degeneration in Aging and Dementia along the AD continuum

AbstractBackgroundDiffusion tensor imaging (DTI) has been commonly used to characterize microstructural changes associated with aging and Alzheimer’s disease (AD) dementia. However, DTI ignores non‐Gaussian diffusion, which limits its ability to describe the more restricted diffusion present within cellular structures. Neurite Orientation Dispersion and Density Imaging (NODDI) and Mean Apparent Propagator (MAP) MRI are two alternative diffusion MRI (DWI) models which can be used to calculate parameters that better characterize alterations observed in tissue microstructure.To investigate the sensitivity of DTI, NODDI, and MAP to white matter changes associated with aging and dementia, we examined the relationship between 15 DWI parameters, age, and AD clinical status in 418 aging adults along the AD continuum.Methods418 participants (328 cognitively unimpaired [CU], 60 Mild Cognitive Impairment [MCI], 30 AD) from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center were imaged with multi‐shell DWI (Table 1). 15 DWI parameters were extracted from the cingulum, including 4 DTI (FA, MD, RD, AxD), 3 NODDI (ODI, NDI, ISO) and 8 MAP (RTOP, RTAP, RTPP, MSD, QIV, NG, NG||, NG⊥) metrics. All measures were fit to the following linear model:Diffusion Metric = β0+β1 *(age)+ β2 *(sex)+β3 *(status)+β4*(age*status)+ΕResultsDWI age trajectories and model parameters are provided in Figures 1‐2 and Tables 2‐4, respectively. 12 of 15 DWI metrics were significantly associated with age, including all 8 MAP measures. Additionally, 6 metrics were associated with AD and 8 were associated with MCI. The interaction between age and AD was significant for RTOP, NDI, FA, and AxD, while the interaction between age and MCI was significant for RTOP, RTPP, MSD, NG, NG⊥, ISO, MD, RD, and AxD (Pcorr = 0.05).ConclusionThe significant effects of clinical status and interactions between age and clinical status, may indicate that these metrics are sensitive to microstructural alterations specific to dementia. Furthermore, the meaningful interactions between age and MCI in 5 of 8 MAP parameters suggests that MAP MRI may be particularly useful for detecting early WM degeneration along the AD continuum. Future work will translate this analysis to larger cohorts and assess MAP parameters in gray matter.

Applying MAP-MRI to Identify the WHO Grade and Main Genetic Features of Adult-type Diffuse Gliomas: A Comparison of Three Diffusion-weighted MRI Models

Currently, there is no noninvasive method to effectively judge the genotype of diffuse gliomas. We explored the association between mean apparent propagator-MRI (MAP-MRI) and WHO grade 2/3, IDH 1/2 mutations, and chromosome 1p/19q combined deletion genotypes in adult-type diffuse gliomas and compared it with the diagnostic efficiency of diffusion tensor imaging (DTI) and diffusional kurtosis imaging (DKI). We prospectively recruited 67 participantshistopathologically diagnosed with adult-type diffuse gliomas. Routine MRI, DKI, and DSI were performed before surgery. The extreme and average partial diffusion indexes of solid tumors were measured. A comprehensive assessment of statistically significant diffusion parameters was performed after Bonferroni correction, including ROC curves, correct classification percentage (CCP), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and k-fold cross validation. For differentiating WHO grade 2/3, q-space inverse variance (QIV), mean kurtosis (MK), non-Gaussianity (NG), and return to the origin probability (RTOP) were different (p' < .05), with the mean QIV exhibiting the best diagnostic efficacy and stability (AUC=0.973, CCP=0.906). We observed significant differences in mean diffusivity (MD), mean square displacement, QIV, MK, and RTOP between the IDH wild-type and IDH mutant groups (p' < .001) (AUC, 0.806-0.978) and MAP-MRI showed a higher IDI than DTI and DKI (0.094-0.435, NRI > 0, respectively). For the chromosome 1p/19q combined deletion, the minimum QIV was different between the overall (p' < .05) and no significant differences in MD and MK was observed. MAP-MRI effectively predicts the WHO grade 2/3, IDH 1/2 mutations, and chromosome 1p/19q combined deletion in adult-type diffuse gliomas, and it may perform better than DTI and DKT.

Mean apparent propagator-MRI in evaluation of glioma grade, cellular proliferation, and IDH-1 gene mutation status.

To evaluate the glioma grade, Ki-67 expression, and IDH-1 mutation status using mean apparent propagator (MAP) MRI. Forty enrolled glioma patients underwent structural and diffusion MRI. The diffusion metric values including fractional anisotropy (FA), mean diffusivity (MD), mean squared displacement (MSD), q-space inverse variance (QIV), return-to-origin probability (RTOP), return-to-axis probability (RTAP), and return-to-plane probability (RTPP) in tumor parenchyma (TP) and contralateral normal-appearing white matter (NAWM) were calculated. The TP/NAWM ratios of diffusion metric values were correlated with tumor grades, Ki-67, and IDH-1 mutation statuses, and the diagnostic performance was assessed. QIV were significantly higher, whereas RTAP and RTOP were significantly lower in low-grade gliomas (LGGs) than those in high-grade gliomas (HGGs); QIV and MD were significantly higher, whereas RTAP and RTOP were significantly lower in lower-grade gliomas (grade II and III) than those in grade IV gliomas (p < 0.05 for all). RTAP performed best in grading gliomas. MSD, QIV, and MD were significantly higher, whereas RTAP, RTOP, RTPP, and FA were significantly lower in the IDH-1 mutant gliomas than those in the IDH-1 wild-type ones both for all gliomas and lower-grade gliomas (p < 0.05 for all). RTAP performed best in all gliomas, while QIV performed best in lower-grade gliomas. Additionally, RTAP, RTOP, and FA correlated positively, whereas MSD, QIV, and MD correlated negatively with Ki-67 (p < 0.05 for all). MAP-MRI is a potent approach in evaluating the microstructural changes in gliomas with different grades, cellular proliferation, and IDH-1 mutation statuses. • MAP-MRI, a newly developed diffusion technique, accurately reveals microstructure-related features in the complex white matter by recovering important microstructural tissue parameters. • MAP-MRI is a potent approach in evaluating the glioma grade, IDH-1 mutation status, and Ki-67 expression. • Compared with DTI, MAP-MRI seems to demonstrate higher diagnostic performance.

Associations between diffusion MRI microstructure and cerebrospinal fluid markers of Alzheimer's disease pathology and neurodegeneration along the Alzheimer's disease continuum.

White matter (WM) degeneration is a critical component of early Alzheimer's disease (AD) pathophysiology. Diffusion-weighted imaging (DWI) models, including diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAP-MRI), have the potential to identify early neurodegenerative WM changes associated with AD. We imaged 213 (198 cognitively unimpaired) aging adults with DWI and used tract-based spatial statistics to compare 15 DWI metrics of WM microstructure to 9 cerebrospinal fluid (CSF) markers of AD pathology and neurodegeneration treated as continuous variables. We found widespread WM injury in AD, as indexed by robust associations between DWI metrics and CSF biomarkers. MAP-MRI had more spatially diffuse relationships with Aβ42/40 and pTau, compared with NODDI and DTI. Our results suggest that WM degeneration may be more pervasive in AD than is commonly appreciated and that innovative DWI models such as MAP-MRI may provide clinically viable biomarkers of AD-related neurodegeneration in the earliest stages of AD progression.

Associations between mean apparent propagator MRI and cerebrospinal fluid markers of AD pathology, neurodegeneration, and glial activation, in cognitively unimpaired adults

AbstractBackgroundNumerous studies have reported correlations between diffusion tensor imaging (DTI) metrics and cerebrospinal (CSF) biomarkers of Alzheimer’s disease (AD) pathology. However, the assumption of Gaussian diffusion in DTI limits the ability to characterize white matter (WM) microstructural changes. Mean apparent propagator (MAP) MRI is a model that attempts to overcome this limitation by allowing for the estimation of parameters that convey more precise information about WM microstructure.To investigate MAP MRI’s sensitivity to early WM degeneration associated with preclinical, asymptomatic AD pathology, we examined the relationship between CSF biomarkers and MAP MRI microstructural parameters in 92 cognitively unimpaired adults.Method92 cognitively unimpaired controls from the Wisconsin Registry for Alzheimer’s Prevention and the Wisconsin Alzheimer’s Disease Research Center were imaged with multi‐shell diffusion‐weighted MRI. DTI metrics (FA, MD, RD, AxD) were computed and the MAP MRI model was employed to calculate various microstructural parameters: Return to origin probability (RTOP), return to axis probability (RTAP), return to plane probability (RTPP), mean squared displacement (MSD), Non‐Gaussianity (NG), and q‐space inverse variance (QIV). DTI and MAP parameter values were extracted from the cingulum and correlated to Aβ42, P‐Tau, P‐Tau/Aβ42, YKL‐40, and neurofilament light chain (NFL) levels in lumbar cerebrospinal fluid samples.ResultMAP parameters were moderately and significantly correlated to most CSF measures. RTPP was significantly correlated to all 5 CSF measures, while MSD, NG, and QIV were significantly correlated to 4 of 5 CSF measures. NFL, a known marker for axonal degeneration, was significantly related to 5 of the 6 MAP parameters assessed. Meanwhile, correlations between DTI and CSF measures were weak and non‐significant.ConclusionThese preliminary results highlight the potential of MAP MRI to detect early WM deterioration indicative of preclinical, asymptomatic AD and associated neurodegeneration. MAP metrics extracted from a commonly affected WM tract in AD were more strongly and significantly correlated with CSF measures than DTI metrics were, suggesting that MAP MRI may be more useful than DTI for identifying the earliest WM microstructural changes associated with AD. Future work will incorporate longitudinal data and assess the effects of CSF and molecular imaging markers on MAP age trajectories.

Effect of APOE4 and APOE2 genotype on white matter microstructure

AbstractBackgroundThe apolipoprotein E4 (APOE4) genotype is a major risk factor for late‐onset Alzheimer’s disease (AD). APOE2, on the other hand, has a protective effect. While there is evidence supporting an association between APOE genotype and white matter (WM) microstructure, findings have been inconsistent. Studies assessing AD risk in WM microstructure have largely used diffusion tensor imaging (DTI), but more recent diffusion MRI models may offer additional sensitivity to microstructural properties. Here, we characterized the effects of APOE4 and APOE2 genotypes on WM microstructure in a large, population‐based sample of adults from the UK Biobank.MethodDiffusion MRI data from 17,896 UK Biobank participants (age range: 45‐80 years; Fig 1a) were analyzed. Ten diffusion measures were derived from 4 models: 1) DTI, 2) the tensor distribution function (TDF), 3) mean apparent propagator MRI (MAPMRI), and 4) neurite orientation dispersion and density imaging (NODDI). Each measure was averaged across voxels within 25 WM regions (Fig 1c). Linear regressions were used to compare regional measures between individuals with an E3E3 genotype and E4 (E3E4+E4E4) or E2 (E2E3+E2E2) carriers, adjusting for age, sex, age‐by‐sex interaction, education, parents with dementia, and the first four genetic ancestry principal components. E4 or E2 carrier‐by‐age interactions were also tested, covarying for APOE genotype. FDR (q=0.05) was used to correct for multiple comparisons across 25 regions.ResultCompared to E3E3 carriers, E4 carriers showed widespread, yet subtle WM differences (Fig 2). The largest effects were detected with signal‐based model measures that reflect increased mean diffusivity (higher DTI‐MD, higher MSD, lower TDF‐FA). Significant E4‐by‐age interactions were detected with NODDI‐ISOVF, with E4 carriers showing a greater age‐related increase in ISOVF (Fig 3). Compared to E3E3 carriers, E2 carriers showed higher NODDI‐ICVF throughout the WM; differences were not detected with other measures. No significant E2‐by‐age interactions were detected (Fig 4).ConclusionAPOE4 carriers showed microstructural WM differences in measures reflecting less restricted diffusion, which could indicate neuronal loss or inflammation, particularly in posterior WM. Age‐dependent APOE4 effects were only detected with multi‐compartment diffusion measures. Greater neuronal density in APOE2 carriers may suggest a protective effect in WM, which was also detectable only with a multi‐compartment model.

Advanced diffusion‐weighted MRI methods demonstrate improved sensitivity to white matter aging: Percentile charts for over 15,000 UK Biobank participants

AbstractBackgroundAging and Alzheimer’s disease are both associated with alterations in the brain’s white matter. Understanding how the brain’s white matter changes as we age may also improve our understanding of processes involved in Alzheimer’s disease. Here we investigated the ability of both traditional and advanced diffusion‐weighted MRI methods to capture age effects on white matter microstructure in a large‐scale, population‐based sample of middle‐aged and older adults.MethodDiffusion‐weighted MRI (dMRI) scans were analyzed from 15,628 UK Biobank participants (age: 45‐80 years; 47.6% male). Diffusivity metrics were derived using four dMRI reconstruction models, including traditional diffusion tensor imaging (DTI) and the following advanced dMRI models: the tensor distribution function (TDF), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator MRI (MAPMRI). Metrics were projected to a standard white matter skeleton using publicly available ENIGMA protocols based on FSL’s tract‐based spatial statistics and the JHU atlas. Mean values for each dMRI metric were extracted from the whole white matter skeleton (full WM) and for the corpus callosum (CC). We investigated effects of age, sex, and their interaction on each dMRI metric for the full WM and the CC separately, to rank the sensitivity of these different metrics. Sex‐stratified centile reference curves were created for each dMRI metric to provide normative charts of white matter aging for this population.ResultParticipant age and sex were both significantly associated with full WM and CC microstructure for nearly all dMRI metrics, with TDF exhibiting the greatest sensitivity to age and NODDI the greatest sensitivity to participant sex (Figure 1). A significant age by sex interaction was observed in the full WM and the CC for many dMRI measures; NODDI detected this interaction with the greatest sensitivity. (Figure 1). Normative centile reference curves are presented for all dMRI indices separately for men and women (Figures 2 and 3).ConclusionParticipant age and sex robustly impact the brain’s white matter microstructure, with advanced dMRI models displaying the greatest sensitivity to such differences. These findings offer a normative reference to guide future studies and treatment of Alzheimer’s disease.

Age effects on white matter microstructure in individuals of self‐identified Indian ancestry from the UK Biobank

AbstractBackgroundThe incidence of dementia in India is 14%, and its prevalence doubles with every five‐year increase in age. Given the lack of ethnic diversity in most brain research to date, it is valuable to study cohorts with diverse genetic and environmental backgrounds, to identify predictors of health and disease that can be generalized to other ethnic groups than the commonly studied populations of European ancestry. To address this gap in the literature, we modeled factors that affect aging patterns in the brain’s white matter in individuals with Indian ancestry using advanced diffusion‐weighted MRI (dMRI).MethodCross‐sectional dMRI data from 123 individuals (males, n=78; females, n=45) of self‐reported Indian ancestry (born in India: 44%; mean age: 60.1±8.3 SD, range: 45‐80) from the UK Biobank were analyzed using diffusion tensor imaging (DTI), the tensor distribution function (TDF), neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator MRI (MAPMRI). Main effects of age, sex, and age‐by‐sex interaction were investigated to characterize trajectories in whole‐brain white matter averages and the corpus callosum (CC), parcellated using the JHU‐ICBM atlas, covarying for years of education, waist/hip ratio, population structure (Figure 1) and the Townsend index. Normative lifespan charts were created to visualize white matter aging trajectories for the major diffusivity metrics.ResultNormative centile curves (Figures 2‐3) show, with increasing age, declining white matter integrity, increased diffusivity, lower white matter density and diffusion restriction at the whole‐brain level and CC (Table 1; Figure 4). There was no detectable effect of sex, nor age‐by‐sex interaction, across whole‐brain metrics. In the CC, a steeper aging trajectory, evidenced by more pronounced white matter changes, was observed in males relative to females (Table 1; Figure 5).ConclusionUsing advanced dMRI modeling, we report diffuse white matter changes with respect to age in a subsample of UK Biobank individuals of self‐reported Indian ancestry. Future studies should include larger sample sizes, comparisons with Indian ancestry individuals residing in India, consider vascular factors, and examine genotype interactions (e.g., with apolipoprotein E genotype), to further characterize risk factors and better understand brain aging in diverse populations with varied genetic and environmental backgrounds.

Diffusion propagator metrics are biased when simultaneous multi-slice acceleration is used

Advanced diffusion MRI models are being explored to study the complex microstructure of the brain with higher accuracy. However, these techniques require long acquisition times. Simultaneous Multi-Slice (SMS) accelerates data acquisition by exciting multiple image slices simultaneously and separating the overlapping slices using a mathematical model, which makes use of the distinct information coming from an array of receive coils. However, SMS acceleration introduces increased noise in reconstructed images and crosstalk between simultaneously excited slices. These compounded effects from SMS acceleration could affect quantitative MRI techniques such as diffusion imaging.In this study, the effects of SMS acceleration on the accuracy of propagator metrics obtained from a model-free advanced diffusion technique called Mean Apparent Propagator MRI (MAP-MRI) was investigated. Ten healthy volunteers were scanned with SMS accelerated multi-shell diffusion MRI acquisitions. Group analyses were performed to study brain regions affected by SMS acceleration. In addition, diffusion metrics from atlas-based fiber tracts of interest were analyzed to investigate how propagator metrics in major fiber tracts were biased by 2- and 3-band SMS acceleration.Both zero-displacement metrics and non-Gaussianity metrics were significantly altered when SMS acceleration was used. MAP-MRI metrics calculated from SMS-3 showed significant differences with respect to SMS-2. Furthermore, with the shorter TR afforded by SMS acceleration, the characteristics of this bias have changed.This has implications for studies using diffusion MRI with SMS acceleration to investigate the effects of a disease or injury on the brain tissues.

Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: A UK biobank study

A comprehensive characterization of the brain’s white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45–80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.

Computing the orientational-average of diffusion-weighted MRI signals: a comparison of different techniques

Numerous applications in diffusion MRI involve computing the orientationally-averaged diffusion-weighted signal. Most approaches implicitly assume, for a given b-value, that the gradient sampling vectors are uniformly distributed on a sphere (or ‘shell’), computing the orientationally-averaged signal through simple arithmetic averaging. One challenge with this approach is that not all acquisition schemes have gradient sampling vectors distributed over perfect spheres. To ameliorate this challenge, alternative averaging methods include: weighted signal averaging; spherical harmonic representation of the signal in each shell; and using Mean Apparent Propagator MRI (MAP-MRI) to derive a three-dimensional signal representation and estimate its ‘isotropic part’. Here, these different methods are simulated and compared under different signal-to-noise (SNR) realizations. With sufficiently dense sampling points (61 orientations per shell), and isotropically-distributed sampling vectors, all averaging methods give comparable results, (MAP-MRI-based estimates give slightly higher accuracy, albeit with slightly elevated bias as b-value increases). As the SNR and number of data points per shell are reduced, MAP-MRI-based approaches give significantly higher accuracy compared with the other methods. We also apply these approaches to in vivo data where the results are broadly consistent with our simulations. A statistical analysis of the simulated data shows that the orientationally-averaged signals at each b-value are largely Gaussian distributed.

White matter microstructural impairments in amyotrophic lateral sclerosis: A mean apparent propagator MRI study

BackgroundWhite matter (WM) impairment is a hallmark of amyotrophic lateral sclerosis (ALS). This study evaluated the capacity of mean apparent propagator magnetic resonance imaging (MAP-MRI) for detecting ALS-related WM alterations. MethodsDiffusion images were obtained from 52 ALS patients and 51 controls. MAP-derived indices [return-to-origin/-axis/-plane probability (RTOP/RTAP/RTPP) and non-Gaussianity (NG)/perpendicular/parallel NG (NG⊥/NG||)] were computed. Measures from diffusion tensor/kurtosis imaging (DTI/DKI) and neurite orientation dispersion and density imaging (NODDI) were also obtained. Voxel-wise analysis (VBA) was performed to determine differences in these parameters. Relationship between MAP parameters and disease severity (assessed by the revised ALS Functional Rating Scale (ALSFRS-R)) was evaluated by Pearson’s correlation analysis in a voxel-wise way. ALS patients were further divided into two subgroups: 29 with limb-only involvement and 23 with both bulbar and limb involvement. Subgroup analysis was then conducted to investigate diffusion parameter differences related to bulbar impairment. ResultsThe VBA (with threshold of P < 0.05 after family-wise error correction (FWE)) showed that ALS patients had significantly decreased RTOP/RTAP/RTPP and NG/ NG⊥/NG|| in a set of WM areas, including the bilateral precentral gyrus, corona radiata, posterior limb of internal capsule, midbrain, middle corpus callosum, anterior corpus callosum, parahippocampal gyrus, and medulla. MAP-MRI had the capacity to capture WM damage in ALS, which was higher than DTI and similar to DKI/NODDI. RTOP/RTAP/NG/NG⊥/NG|| parameters, especially in the bilateral posterior limb of internal capsule and middle corpus callosum, were significantly correlated with ALSFRS-R (with threshold of FWE-corrected P < 0.05). The VBA (with FWE-corrected P < 0.05) revealed the significant RTAP reduction in subgroup with both bulbar and limb involvement, compared with those with limb-only involvement. ConclusionsMicrostructural impairments in corticospinal tract and corpus callosum represent the consistent characteristic of ALS. MAP-MRI could provide alternative measures depicting ALS-related WM alterations, complementary to the common diffusion imaging methods.

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different sources of uncertainty in such problems. Here we introduce methods to characterise different components of uncertainty, and demonstrate the ideas using diffusion MRI super-resolution. Specifically, we propose to account for intrinsic uncertainty through a heteroscedastic noise model and for parameter uncertainty through approximate Bayesian inference, and integrate the two to quantify predictive uncertainty over the output image. Moreover, we introduce a method to propagate the predictive uncertainty on a multi-channelled image to derived scalar parameters, and separately quantify the effects of intrinsic and parameter uncertainty therein. The methods are evaluated for super-resolution of two different signal representations of diffusion MR images—Diffusion Tensor images and Mean Apparent Propagator MRI—and their derived quantities such as mean diffusivity and fractional anisotropy, on multiple datasets of both healthy and pathological human brains. Results highlight three key potential benefits of modelling uncertainty for improving the safety of DL-based image enhancement systems. Firstly, modelling uncertainty improves the predictive performance even when test data departs from training data (“out-of-distribution” datasets). Secondly, the predictive uncertainty highly correlates with reconstruction errors, and is therefore capable of detecting predictive “failures”. Results on both healthy subjects and patients with brain glioma or multiple sclerosis demonstrate that such an uncertainty measure enables subject-specific and voxel-wise risk assessment of the super-resolved images that can be accounted for in subsequent analysis. Thirdly, we show that the method for decomposing predictive uncertainty into its independent sources provides high-level “explanations” for the model performance by separately quantifying how much uncertainty arises from the inherent difficulty of the task or the limited training examples. The introduced concepts of uncertainty modelling extend naturally to many other imaging modalities and data enhancement applications.

Mean Apparent Propagator MRI Is Better Than Conventional Diffusion Tensor Imaging for the Evaluation of Parkinson's Disease: A Prospective Pilot Study.

Background and PurposeMean apparent propagator (MAP) MRI is a novel diffusion imaging method to map tissue microstructure. The purpose of this study was to evaluate the diagnostic value of the MAP MRI in Parkinson’s disease (PD) in comparison with conventional diffusion tensor imaging (DTI).Methods23 PD patients and 22 age- and gender-matched healthy controls were included. MAP MRI and DTI were performed on a 3T MR scanner with a 20-channel head coil. The MAP metrics including mean square displacement (MSD), return to the origin probability (RTOP), return to the axis probability (RTAP), and return to the plane probability (RTPP), and DTI metrics including fractional anisotropy (FA), and mean diffusivity (MD), were measured in subcortical gray matter and compared between the two groups. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic performance of all the metrics. The association between the diffusion metrics and disease severity was assessed by Pearson correlation analysis.ResultsFor MAP MRI, the mean values of MSD in the bilateral caudate, pallidum, putamen, thalamus and substantia nigra (SN) were higher in PD patients than in healthy controls (pFDR ≤ 0.001); the mean values of the zero displacement probabilities (RTOP, RTAP, and RTPP) in the bilateral caudate, pallidum, putamen and thalamus were lower in PD patients (pFDR < 0.001). For DTI, only FA in the bilateral SN was significantly higher in PD patients than those in the controls (pFDR < 0.001). ROC analysis showed that the areas under the curves of MAP MRI metrics (MSD, RTOP, RTAP, and RTPP) in the bilateral caudate, pallidum, putamen and thalamus (range, 0.85–0.94) were greater than those of FA and MD of DTI (range, 0.55–0.69) in discriminating between PD patients and healthy controls. RTAP in the ipsilateral pallidum (r = −0.56, pFDR = 0.027), RTOP in the bilateral and contralateral putamen (r = −0.58, pFDR = 0.019; r = −0.57, pFDR = 0.024) were negatively correlated with UPDRS III motor scores.ConclusionMAP MRI outperformed the conventional DTI in the diagnosis of PD and evaluation of the disease severity.

Leveraging multi-shell diffusion for studies of brain development in youth and young adulthood

Diffusion weighted imaging (DWI) has advanced our understanding of brain microstructure evolution over development. Recently, the use of multi-shell diffusion imaging sequences has coincided with advances in modeling the diffusion signal, such as Neurite Orientation Dispersion and Density Imaging (NODDI) and Laplacian-regularized Mean Apparent Propagator MRI (MAPL). However, the relative utility of recently-developed diffusion models for understanding brain maturation remains sparsely investigated. Additionally, despite evidence that motion artifact is a major confound for studies of development, the vulnerability of metrics derived from contemporary models to in-scanner motion has not been described. Accordingly, in a sample of 120 youth and young adults (ages 12–30) we evaluated metrics derived from diffusion tensor imaging (DTI), NODDI, and MAPL for associations with age and in-scanner head motion at multiple scales. Specifically, we examined mean white matter values, white matter tracts, white matter voxels, and connections in structural brain networks. Our results revealed that multi-shell diffusion imaging data can be leveraged to robustly characterize neurodevelopment, and demonstrate stronger age effects than equivalent single-shell data. Additionally, MAPL-derived metrics were less sensitive to the confounding effects of head motion. Our findings suggest that multi-shell imaging data and contemporary modeling techniques confer important advantages for studies of neurodevelopment.

Optimization of q-space sampling for mean apparent propagator MRI metrics using a genetic algorithm

Mean Apparent Propagator (MAP) MRI is a recently introduced technique to estimate the diffusion probability density function (PDF) robustly. Using the estimated PDF, MAP MRI then calculates zero-displacement and non-Gaussianity metrics, which might better characterize tissue microstructure compared to diffusion tensor imaging or diffusion kurtosis imaging. However, intensive q-space sampling required for MAP MRI limits its widespread adoption. A reduced q-space sampling scheme that maintains the accuracy of the derived metrics would make it more practical. A heuristic approach for acquiring MAP MRI with fewer q-space samples has been introduced earlier with scan duration of less than 10 minutes. However, the sampling scheme was not optimized systematically to preserve the accuracy of the model metrics. In this work, a genetic algorithm is implemented to determine optimal q-space subsampling schemes for MAP MRI that will keep total scan time under 10 min. Results show that the metrics derived from the optimized schemes more closely match those computed from the full set, especially in dense fiber tracts such as the corpus callosum.

Generalized Mean Apparent Propagator MRI to Measure and Image Advective and Dispersive Flows in Medicine and Biology.

Water transport in biological systems spans different regimes with distinct physical behaviors: diffusion, advection, and dispersion. Identifying these regimes is of paramount importance in many in vivo applications, among them, measuring microcirculation of blood in capillary networks and cerebrospinal fluid transport in the glymphatic system. Diffusion magnetic resonance imaging (dMRI) can be used to encode water displacements, and a Fourier transform of the acquired signal furnishes a displacement probability density function known as the propagator. This transformation normally requires the use of a fast Fourier transform (FFT), which presents major feasibility challenges when scanning in vivo, mainly because of dense signal sampling, resulting in long acquisition times. A second approach to reconstruct the propagator is by using analytical representation of the signal, overcoming many of the FFT's limitations. In all analytical implementations of dMRI to date, the translational motion of water has been assumed to be exclusively diffusive, which is the case only in the absence of flow. However, retaining the phase information from the diffusion signal provides the ability to measure both mean coherent velocity and random diffusion from a single experiment. We implement and extend an analytical framework, mean apparent propagator (MAP), which can account for non-zero flow conditions. We call this method generalized MAP or GMAP. We describe a numerical optimization scheme and implement it on data from an MRI flow phantom constructed from a pack of 10- [Formula: see text] beads. The advantages of GMAP over the FFT-based method in the context of sampling density and low-flow detection were demonstrated, and analytically derived propagator moments were shown to agree with theoretical values even after data subsampling. GMAP would enable the detection of microflow in vivo that could help elucidate many important biological processes.

Bayesian uncertainty quantification in linear models for diffusion MRI

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.