Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.

BACKGROUND: Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were applied to detect CMBs in MRI. PURPOSE: To automatically detect CMB on QSM, we proposed a two-stage deep learning pipeline. STUDY TYPE: Retrospective. SUBJECTS: A total number of 1843 CMBs from 393 patients (69 ± 12) with cerebral small vessel disease were included in this study. Seventy-eight subjects (70 ± 13) were used as external testing. FIELD STRENGTH/SEQUENCE: 3 T/QSM. ASSESSMENT: The proposed pipeline consisted of two stages. In stage I, 2.5D fast radial symmetry transform (FRST) algorithm along with a one-layer convolutional network was used to identify CMB candidate regions in QSM images. In stage II, the V-Net was utilized to reduce false positives. The V-Net was trained using CMB and non CMB labels, which allowed for high-level feature extraction and differentiation between CMBs and CMB mimics like vessels. The location of CMB was assessed according to the microbleeds anatomical rating scale (MARS) system. STATISTICAL TESTS: The sensitivity and positive predicative value (PPV) were reported to evaluate the performance of the model. The number of false positive per subject was presented. RESULTS: Our pipeline demonstrated high sensitivities of up to 94.9% at stage I and 93.5% at stage II. The overall sensitivity was 88.9%, and the false positive rate per subject was 2.87. With respect to MARS, sensitivities of above 85% were observed for nine different brain regions. DATA CONCLUSION: We have presented a deep learning pipeline for detecting CMB in the CSVD cohort, along with a semi-automated MARS scoring system using the proposed method. Our results demonstrated the successful application of deep learning for CMB detection on QSM and outperformed previous handcrafted methods. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Advanced Diffusion MRI for prediction of Stroke Recovery.

There is an urgent need for ways to improve our understanding of poststroke recovery to inform the development of novel rehabilitative interventions, and improve the clinical management of stroke patients. Supported by the notion that predictive information on poststroke recovery is embedded not only in the individual brain regions, but also the connections throughout the brain, majority of previous investigations have focused on the relationship between brain functional connections and post-stroke deficit and recovery. However, considering the fact that it is the static anatomical brain connections that constrain and facilitate the dynamic functional brain connections, the microstructures and structural connections of the brain may potentially be better alternatives to the functional MRI-based biomarkers of stroke recovery. This review, therefore, seeks to provide an overview of the basic concept and applications of two recently proposed advanced diffusion MRI techniques, namely lesion network mapping and fixel-based morphometry, that may be useful for the investigation of stroke recovery at the local and global levels of the brain. This review will also highlight the application of some of other emerging advanced diffusion MRI techniques that warrant further investigation in the context of stroke recovery research.

MRI reconstruction using deep Bayesian estimation.

PURPOSE: To develop a deep learning-based Bayesian estimation for MRI reconstruction. METHODS: We modeled the MRI reconstruction problem with Bayes's theorem, following the recently proposed PixelCNN++ method. The image reconstruction from incomplete k-space measurement was obtained by maximizing the posterior possibility. A generative network was utilized as the image prior, which was computationally tractable, and the k-space data fidelity was enforced by using an equality constraint. The stochastic backpropagation was utilized to calculate the descent gradient in the process of maximum a posterior, and a projected subgradient method was used to impose the equality constraint. In contrast to the other deep learning reconstruction methods, the proposed one used the likelihood of prior as the training loss and the objective function in reconstruction to improve the image quality. RESULTS: The proposed method showed an improved performance in preserving image details and reducing aliasing artifacts, compared with GRAPPA, ℓ1 -ESPRiT, model-based deep learning architecture for inverse problems (MODL), and variational network (VN), last two were state-of-the-art deep learning reconstruction methods. The proposed method generally achieved more than 3 dB peak signal-to-noise ratio improvement for compressed sensing and parallel imaging reconstructions compared with the other methods. CONCLUSIONS: The Bayesian estimation significantly improved the reconstruction performance, compared with the conventional ℓ1 -sparsity prior in compressed sensing reconstruction tasks. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios.

Elimination of residual aliasing artifact that resembles brain lesion on multi-oblique diffusion-weighted echo-planar imaging with parallel imaging using virtual coil acquisition.

BACKGROUND: Single-shot diffusion-weighted echo-planar imaging (ssDW-EPI) acquired with parallel imaging and a multi-oblique scan plane may suffer from residual aliasing artifacts, resembling lesions on the calculated apparent diffusion coefficient (ADC) map. PURPOSE: To combine ssDW-EPI and virtual coil acquisition and develop a self-reference reconstruction method to eliminate the residual aliasing artifact on multi-oblique ssDW-EPI sequence with parallel imaging and multiple signal averaging. STUDY TYPE: Prospective. SUBJECTS: Three healthy subjects and 50 stroke patients. FIELD STRENGTH/SEQUENCE: Conventional ssDW-EPI with parallel imaging, and proposed ssDW-EPI with virtual coil acquisition at 1.5T. ASSESSMENT: The efficacy of the proposed method was evaluated in 50 stroke patients by comparing the ssDW-EPI with conventional parallel imaging reconstructions. The extent of residual aliasing artifacts were rated on a 5-point Likert scale by three independent raters. Only the data without residual aliasing artifacts on conventional ssDW-EPI were included for the assessment of signal-to-noise ratio (SNR), ghost-to-signal ratio (GSR), and ADC. STATISTICAL TESTS: The interobserver agreements for examining residual aliasing artifacts were measured by the intraclass correlation coefficient (ICC). A two-sample t-test was performed for comparing SNR, GSR, and ADC. RESULTS: There was a perfect agreement (ICC = 1.00) in the examination of residual aliasing artifacts on images obtained using the proposed method. The incidence rates of the residual aliasing artifact on the ADC maps obtained from the scanner console and proposed method were 60% (ie, 30 out of 50) and 0%, respectively. The proposed method offers significantly lower GSR than conventional parallel imaging reconstruction (P < 0.001). There was no significant difference in SNR (P = 0.20-0.51) and ADC values (P = 0.20-0.94) between conventional parallel imaging reconstructions and the proposed method. DATA CONCLUSION: It appears that our method could effectively eliminate artifacts and significantly improve the GSR of b = 0 T2 WI and b > 0 DWI, as well as permit ADC measurement consistent with conventional techniques. Our method may be beneficial to clinical assessment of the brain that utilizes multi-oblique ssDW-EPI. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1442-1453.

Functional tumour burden of peritoneal carcinomatosis derived from DWI could predict incomplete tumour debulking in advanced ovarian carcinoma.

OBJECTIVES: To investigate the predictive value of peritoneal carcinomatosis (PC) quantification by DWI in determining incomplete tumour debulking in ovarian carcinoma (OC). METHODS: Prospective patients with suspected stage III-IV or recurrent OC were recruited for DWI before surgery. PC on DWI was segmented semi-automatically by k-means clustering, retaining voxels with intermediate apparent diffusion coefficient (ADC) to quantify PC burden. A scoring system, functional peritoneal cancer index (fPCI), was proposed based on the segmentation of tumour volume in 13 abdominopelvic regions with additional point given to involvement of critical sites. ADC of the largest PC was recorded. The surgical complexity and outcomes (complete vs. incomplete tumour debulking) were documented. fPCI was correlated with surgical PCI (sPCI), surgical complexity, and its ability to predict incomplete tumour debulking. RESULTS: Fifty-three patients with stage III-IV or recurrent OC were included with a mean age of 56.1 ± 11.8 years old. Complete tumour debulking was achieved in 38/53 patients (71.7%). Significant correlation was found between fPCI and sPCI (r > 0.757, p < 0.001). Patients with high-fPCI (fPCI ≥ 6) had a high surgical complexity score (p = 0.043) with 84.2% received radical or supra-radical surgery. The mean fPCI was significantly higher in patients with incomplete tumour debulking than in those with complete debulking (10.27 vs. 4.71, p < 0.001). fPCI/ADC combined with The International Federation of Gynecology and Obstetrics stage achieved 92.5% accuracy in predicting incomplete tumour debulking (AUC 0.947). CONCLUSIONS: DWI-derived fPCI offered a semi-automated estimation of PC burden. fPCI/ADC could predict the likelihood of incomplete tumour debulking with high accuracy. KEY POINTS: • Functional peritoneal cancer index (fPCI) derived from DWI offered a semi-automated estimation of tumour burden in ovarian carcinoma. • fPCI was highly correlated with surgical PCI (sPCI). • fPCI/ADC could predict the likelihood of incomplete tumour debulking with high accuracy.

Age-Specific Associations of Renal Impairment and Cerebral Small Vessel Disease Burden in Chinese with Ischaemic Stroke.

BACKGROUND: Recent studies in Caucasians with transient ischaemic attack or ischaemic stroke have demonstrated significant age-specific associations between cerebral small vessel disease (SVD) burden on magnetic resonance imaging and renal impairment. We aimed to validate these findings in a large cohort of Chinese with ischaemic stroke. METHODS: In 959 Chinese with ischaemic stroke who received a brain magnetic resonance imaging at the University of Hong Kong, we determined the age-specific associations of renal impairment (glomerular filtration rate < 60 mL/min/1.73 m2) with neuroimaging markers of SVD as well as with the SVD score. RESULTS: Although renal impairment was associated with the SVD score in univariate analysis in all patients (odds ratio 1.61, 95% confidence interval 1.24-2.09, P < .0001), these associations were attenuated after adjusting for age and sex (P = .38). Similar findings were noted in patients with ischaemic stroke due to SVD and non-SVD subtypes. However, in 222 of 959 patients aged <60, renal impairment was independently associated with an increasing microbleed (adjusted odds ratio 6.82, 2.26-20.59), subcortical (4.97, 1.62-15.24) periventricular white matter hyperintensity (3.96, 1.08-14.51) and global SVD burden (3.41, 1.16-10.04; all P < .05) even after adjusting for age, sex, and vascular risk factors. Nevertheless, there were no associations between renal impairment and individual neuroimaging markers of SVD nor with the SVD score in patients aged ≥60 after adjusting for age and sex (all P > .05). CONCLUSIONS: In Chinese with ischaemic stroke, renal impairment was independently associated with microbleed, white matter hyperintensity and global SVD burden in individuals aged <60, but not in those aged ≥60, suggesting that there may be shared susceptibilities to premature systemic disease.

Phase correction for three-dimensional (3D) diffusion-weighted interleaved EPI using 3D multiplexed sensitivity encoding and reconstruction (3D-MUSER).

PURPOSE: Three-dimensional (3D) multiplexed sensitivity encoding and reconstruction (3D-MUSER) algorithm is proposed to reduce aliasing artifacts and signal corruption caused by inter-shot 3D phase variations in 3D diffusion-weighted echo planar imaging (DW-EPI). THEORY AND METHODS: 3D-MUSER extends the original framework of multiplexed sensitivity encoding (MUSE) to a hybrid k-space-based reconstruction, thereby enabling the correction of inter-shot 3D phase variations. A 3D single-shot EPI navigator echo was used to measure inter-shot 3D phase variations. The performance of 3D-MUSER was evaluated by analyses of point-spread function (PSF), signal-to-noise ratio (SNR), and artifact levels. The efficacy of phase correction using 3D-MUSER for different slab thicknesses and b-values were investigated. RESULTS: Simulations showed that 3D-MUSER could eliminate artifacts because of through-slab phase variation and reduce noise amplification because of SENSE reconstruction. All aliasing artifacts and signal corruption in 3D interleaved DW-EPI acquired with different slab thicknesses and b-values were reduced by our new algorithm. A near-whole brain single-slab 3D DTI with 1.3-mm isotropic voxel acquired at 1.5T was successfully demonstrated. CONCLUSION: 3D phase correction for 3D interleaved DW-EPI data is made possible by 3D-MUSER, thereby improving feasible slab thickness and maximum feasible b-value. Magn Reson Med 79:2702-2712, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Tensor estimation for double-pulsed diffusional kurtosis imaging.

Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.

Sensitivity of diffusion MRI to perilesional reactive astrogliosis in focal ischemia.

Reactive astrogliosis is a response to injury in the central nervous system that plays an essential role in inflammation and tissue repair. It is characterized by hypertrophy of astrocytes, alterations in astrocyte gene expression and astrocyte proliferation. Reactive astrogliosis occurs in multiple neuropathologies, including stroke, traumatic brain injury and Alzheimer's disease, and it has been proposed as a possible source of the changes in diffusion magnetic resonance imaging (dMRI) metrics observed with these diseases. In this study, the sensitivity of dMRI to reactive astrogliosis was tested in an animal model of focal acute and subacute ischemia induced by the vasoconstricting peptide, endothelin-1. Reactive astrogliosis in perilesional cortex was quantified by calculating the astrocyte surface density as determined with a glial fibrillary acidic protein (GFAP) antibody, whereas perilesional diffusion changes were measured in vivo with diffusional kurtosis imaging. We found substantial changes in the surface density of GFAP-positive astrocyte processes and modest changes in dMRI metrics in the perilesional motor cortex following stroke. Although there are time point-specific correlations between dMRI and histological measures, there is no definitive evidence for a causal relationship.

Concordance of FDG PET/CT metabolic tumour volume versus DW-MRI functional tumour volume with T2-weighted anatomical tumour volume in cervical cancer.

BACKGROUND: 18F-fluoro-deoxyglucose positron emission tomography with computed tomography (FDG PET/CT) has been employed to define radiotherapy targets using a threshold based on the standardised uptake value (SUV), and has been described for use in cervical cancer. The aim of this study was to evaluate the concordance between the metabolic tumour volume (MTV) measured on FDG PET/CT and the anatomical tumour volume (ATV) measured on T2-weighted magnetic resonance imaging (T2W-MRI); and compared with the functional tumour volume (FTV) measured on diffusion-weighted MRI (DW-MRI) in cervical cancer, taking the T2W-ATV as gold standard. METHODS: Consecutive newly diagnosed cervical cancer patients who underwent FDG PET/CT and DW-MRI were retrospectively reviewed from June 2013 to July 2017. Volumes of interest was inserted to the focal hypermetabolic activity corresponding to the cervical tumour on FDG PET/CT with automated tumour contouring and manual adjustment, based on SUV 20%-80% thresholds of the maximum SUV (SUVmax) to define the MTV20-80, with intervals of 5%. Tumour areas were manually delineated on T2W-MRI and multiplied by slice thickness to calculate the ATV. FTV were derived by manually delineating tumour area on ADC map, multiplied by the slice thickness to determine the FTV(manual). Diffusion restricted areas was extracted from b0 and ADC map using K-means clustering to determine the FTV(semi-automated). The ATVs, FTVs and the MTVs at different thresholds were compared using the mean and correlated using Pearson's product-moment correlation. RESULTS: Twenty-nine patients were evaluated (median age 52 years). Paired difference of mean between ATV and MTV was the closest and not statistically significant at MTV30 (-2.9cm3, -5.2%, p = 0.301). This was less than the differences between ATV and FTV(semi-automated) (25.0cm3, 45.1%, p < 0.001) and FTV(manual) (11.2cm3, 20.1%, p = 0.001). The correlation of MTV30 with ATV was excellent (r = 0.968, p < 0.001) and better than that of the FTVs. CONCLUSIONS: Our study demonstrated that MTV30 was the only parameter investigated with no statistically significant difference with ATV, had the least absolute difference from ATV, and showed excellent positive correlation with ATV, suggesting its superiority as a functional imaging modality when compared with DW-MRI and supporting its use as a surrogate for ATV for radiotherapy tumour contouring.

Application of diffusional kurtosis imaging to detect occult brain damage in multiple sclerosis and neuromyelitis optica.

Multiple sclerosis (MS) and neuromyelitis optica (NMO) are two common types of inflammatory demyelinating disease of the central nervous system. Early distinction of NMO from MS is crucial but quite challenging. In this study, 13 NMO spectrum disorder patients (Expanded Disability Status Scale (EDSS) of 3.0 ± 1.7, ranging from 2 to 6.5; disease duration of 5.3 ± 4.7 years), 17 relapsing-remitting MS patients (EDSS of 2.6 ± 1.4, ranging from 1 to 5.5; disease duration of 7.9 ± 7.8 years) and 18 healthy volunteers were recruited. Diffusional kurtosis imaging was employed to discriminate NMO and MS patients at the early or stable stage from each other, and from healthy volunteers. The presence of alterations in diffusion and diffusional kurtosis metrics in normal-appearing white matter (NAWM) and diffusely increased mean diffusivity (MD) in the cortical normal-appearing gray matter (NAGM) favors the diagnosis of MS rather than NMO. Meanwhile, normal diffusivities and kurtosis metrics in all NAWM as well as increases in MD in the frontal and temporal NAGM suggest NMO. Our results suggest that diffusion and diffusional kurtosis metrics may well aid in discriminating the two diseases.

Diffusional kurtosis and diffusion tensor imaging reveal different time-sensitive stroke-induced microstructural changes.

BACKGROUND AND PURPOSE: Diffusion MRI is a promising, clinically feasible imaging technique commonly used to describe white matter changes after stroke. We investigated the sensitivity of diffusion MRI to detect microstructural alterations in gray matter after sensorimotor cortex stroke in adult male rats. METHODS: The mean diffusivity (MD) and mean kurtosis of perilesional motor cortex were compared with measures in the contralesional forelimb area of sensorimotor cortex at 2 hours, 24 hours, 72 hours, or 25 days after surgery. MD and mean kurtosis were correlated to the surface densities of glia, dendrites, and axons. RESULTS: Perilesional mean kurtosis was increased at 72 hours and 25 days after stroke, whereas MD was no longer different from contralesional sensorimotor cortex at 24 hours after stroke. There was a significant increase in the density of glial processes at 72 hours after stroke in perilesional motor cortex, which correlated with perilesional MD. CONCLUSIONS: These data support that mean kurtosis and MD provide different but complimentary information on acute and chronic changes in perilesional cortex. Glia infiltration is associated with pseudonormalization of MD in the perilesional motor cortex at 72 hours after lesion; however, this association is absent 25 days after lesion. These data suggest that there are likely several different, time-specific microstructural changes underlying these 2 complimentary diffusion measures.

Double-pulsed diffusional kurtosis imaging for the in vivo assessment of human brain microstructure.

We have recently extended conventional single-pulsed-field-gradient (s-PFG) diffusional kurtosis imaging (DKI) to double-pulsed-field-gradient (d-PFG) diffusion MRI sequences, with a method known as double-pulsed DKI (DP-DKI). By virtue of a six-dimensional (6D) formulation for q-space, many of the results and insights of s-PFG DKI are generalized to those of DP-DKI. Owing to the fact that DP-DKI isolates the second order contributions to the d-PFG signal (i.e. second order in b-value), the 6D diffusional kurtosis encodes information beyond what is available from s-PFG sequences. Previously, we have demonstrated DP-DKI for in vivo mouse brain at 7 T, and it is the objective of this study to demonstrate the feasibility of DP-DKI at 3 T for the in vivo assessment of human brain microstructure. In addition, an example is given of how to utilize the additional information obtained from DP-DKI for the purpose of biophysical modeling. The relationship between a specific microscopic anisotropy metric estimated from DP-DKI and other recently proposed measures is also discussed.

Kurtosis analysis of neural diffusion organization.

A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.

Hemisphere, gender and age-related effects on iron deposition in deep gray matter revealed by quantitative susceptibility mapping.

The purpose of this work was to investigate the effects of hemispheric location, gender and age on susceptibility value, as well as the association between susceptibility value and diffusional metrics, in deep gray matter. Iron content was estimated in vivo using quantitative susceptibility mapping. Microstructure was probed using diffusional kurtosis imaging. Regional susceptibility and diffusional metrics were measured for the putamen, caudate nucleus, globus pallidus, thalamus, substantia nigra and red nucleus in 42 healthy adults (age range 25-78 years). Susceptibility value was significantly higher in the left than the right side of the caudate nucleus (P = 0.043) and substantia nigra (P < 0.001). Women exhibited lower susceptibility values than men in the thalamus (P < 0.001) and red nucleus (P = 0.032). Significant age-related increases of susceptibility were observed in the putamen (P < 0.001), red nucleus (P < 0.001), substantia nigra (P = 0.004), caudate nucleus (P < 0.001) and globus pallidus (P = 0.017). The putamen exhibited the highest rate of iron accumulation with aging (slope of linear regression = 0.73 × 10(-3) ppm/year), which was nearly twice those in substantia nigra (slope = 0.40 × 10(-3) ppm/year) and caudate nucleus (slope = 0.39 × 10(-3) ppm/year). Significant positive correlations between the susceptibility value and diffusion measurements were observed for fractional anisotropy (P = 0.045) and mean kurtosis (P = 0.048) in the putamen without controlling for age. Neither correlation was significant after controlling for age. Hemisphere, gender and age-related differences in iron measurements were observed in deep gray matter. Notably, the putamen exhibited the highest rate of increase in susceptibility with aging. Correlations between susceptibility value and microstructural measurements were inconclusive. These findings could provide new clues for unveiling mechanisms underlying iron-related neurodegenerative diseases.

Double-pulsed diffusional kurtosis imaging.

Diffusional kurtosis imaging (DKI) is extended to double-pulsed-field-gradient (d-PFG) diffusion MRI sequences. This gives a practical approach for acquiring and analyzing d-PFG data. In particular, the leading d-PFG effects, beyond what conventional single-pulsed field gradient (s-PFG) provides, are interpreted in terms of the kurtosis for a diffusion displacement probability density function (dPDF) in a six-dimensional (6D) space. The 6D diffusional kurtosis encodes the unique information provided by d-PFG sequences up to second order in the b-value. This observation leads to a compact expression for the signalmagnitude, and it suggests novel data acquisition and analysis methods. Double-pulsed DKI (DP-DKI) is demonstrated for in vivo mouse brain with d-PFG data obtained at 7 T.

Double-pulsed diffusional kurtosis imaging.

Diffusional kurtosis imaging (DKI) is extended to double-pulsed-field-gradient (d-PFG) diffusion MRI sequences. This gives a practical approach for acquiring and analyzing d-PFG data. In particular, the leading d-PFG effects, beyond what conventional single-pulsed field gradient (s-PFG) provides, are interpreted in terms of the kurtosis for a diffusion displacement probability density function (dPDF) in a six-dimensional (6D) space. The 6D diffusional kurtosis encodes the unique information provided by d-PFG sequences up to second order in the b-value. This observation leads to a compact expression for the signal magnitude, and it suggests novel data acquisition and analysis methods. Double-pulsed DKI (DP-DKI) is demonstrated for in vivo mouse brain with d-PFG data obtained at 7 T. Copyright © 2014 John Wiley & Sons, Ltd.

Histological correlation of diffusional kurtosis and white matter modeling metrics in cuprizone-induced corpus callosum demyelination.

The cuprizone mouse model is well established for studying the processes of both demyelination and remyelination in the corpus callosum, and it has been utilized together with diffusion tensor imaging (DTI) to investigate myelin and axonal pathology. Although some underlying morphological mechanisms contributing to the changes in diffusion tensor (DT) metrics have been identified, the understanding of specific associations between histology and diffusion measures remains limited. Diffusional kurtosis imaging (DKI) is an extension of DTI that provides metrics of diffusional non-Gaussianity, for which an associated white matter modeling (WMM) method has been developed. The main goal of the present study was to quantitatively assess the relationships between diffusion measures and histological measures in the mouse model of cuprizone-induced corpus callosum demyelination. The diffusional kurtosis (DK) and WMM metrics were found to provide additional information that enhances the sensitivity to detect the morphological heterogeneity in the chronic phase of the disease process in the rostral segment of the corpus callosum. Specifically, in the rostral segment, axonal water fraction (d = 2.6; p < 0.0001), radial kurtosis (d = 2.0; p = 0.001) and mean kurtosis (d = 1.5; p = 0.005) showed the most sensitivity between groups with respect to yielding statistically significant p values and high Cohen's d values. These results demonstrate the ability of DK and WMM metrics to detect white mater changes and inflammatory processes associated with cuprizone-induced demyelination. They also validate, in part, the application of these new WMM metrics for studying neurological diseases, as well as helping to elucidate their biophysical meaning.

The structural-functional-connectivity coupling of the aging brain.

Aging primarily affects memory and executive functions, a relationship that may be underpinned by the fact that almost all adults over 60 years old develop small vessel disease (SVD). The fact that a wide range of neuropathologies could only explain up to 43% of the variation in age-related cognitive impairment suggests that other factors, such as cognitive reserve, may play a role in the brain's resilience against aging-related cognitive decline. This study aims to examine the relationship between structural-functional-connectivity coupling (SFC), and aging, cognitive abilities and reserve, and SVD-related neuropathologies using a cohort of n = 176 healthy elders from the Harvard Aging Brain Study. The SFC is a recently proposed biomarker that reflects the extent to which anatomical brain connections can predict coordinated neural activity. After controlling for the effect of age, sex, and years of education, global SFC, as well as the intra-network SFC of the dorsolateral somatomotor and dorsal attention networks, and the inter-network SFC between dorsolateral somatomotor and frontoparietal networks decreased with age. The global SFC decreased with total cognitive score. There were significant interaction effects between years of education versus white matter hyperintensities and between years of education versus cerebral microbleeds on inter-network SFC. Enlarged perivascular space in basal ganglia was associated with higher inter-network SFC. Our results suggest that cognitive ability is associated with brain coupling at the global level and cognitive reserve with brain coupling at the inter-functional-brain-cluster level with interaction effect from white matter hyperintensities and cerebral microbleed in a cohort of healthy elderlies.

Respiratory-Correlated 4-Dimensional Magnetic Resonance Fingerprinting for Liver Cancer Radiation Therapy Motion Management.

PURPOSE: The objective of this study was to develop a respiratory-correlated (RC) 4-dimensional (4D) imaging technique based on magnetic resonance fingerprinting (MRF) (RC-4DMRF) for liver tumor motion management in radiation therapy. METHODS AND MATERIALS: Thirteen patients with liver cancer were prospectively enrolled in this study. k-space MRF signals of the liver were acquired during free-breathing using the fast acquisition with steady-state precession sequence on a 3T scanner. The signals were binned into 8 respiratory phases based on respiratory surrogates, and interphase displacement vector fields were estimated using a phase-specific low-rank optimization method. Hereafter, the tissue property maps, including T1 and T2 relaxation times, and proton density, were reconstructed using a pyramid motion-compensated method that alternatively optimized interphase displacement vector fields and subspace images. To evaluate the efficacy of RC-4DMRF, amplitude motion differences and Pearson correlation coefficients were determined to assess measurement agreement in tumor motion between RC-4DMRF and cine magnetic resonance imaging (MRI); mean absolute percentage errors of the RC-4DMRF-derived tissue maps were calculated to reveal tissue quantification accuracy using digital human phantom; and tumor-to-liver contrast-to-noise ratio of RC-4DMRF images was compared with that of planning CT and contrast-enhanced MRI (CE-MRI) images. A paired Student t test was used for statistical significance analysis with a P value threshold of .05. RESULTS: RC-4DMRF achieved excellent agreement in motion measurement with cine MRI, yielding the mean (± standard deviation) Pearson correlation coefficients of 0.95 ± 0.05 and 0.93 ± 0.09 and amplitude motion differences of 1.48 ± 1.06 mm and 0.81 ± 0.64 mm in the superior-inferior and anterior-posterior directions, respectively. Moreover, RC-4DMRF achieved high accuracy in tissue property quantification, with mean absolute percentage errors of 8.8%, 9.6%, and 5.0% for T1, T2, and proton density, respectively. Notably, the tumor contrast-to-noise ratio in RC-4DMRI-derived T1 maps (6.41 ± 3.37) was found to be the highest among all tissue property maps, approximately equal to that of CE-MRI (6.96 ± 1.01, P = .862), and substantially higher than that of planning CT (2.91 ± 1.97, P = .048). CONCLUSIONS: RC-4DMRF demonstrated high accuracy in respiratory motion measurement and tissue properties quantification, potentially facilitating tumor motion management in liver radiation therapy.