Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping.

Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced. However, it remains challenging to isolate the myelin-proton signals from tissues with longer T2. In this article, we extended our previous two-dimensional ultrashort echo time magnetic resonance fingerprinting (UTE-MRF) with dual-echo acquisition to three dimensional (3D). Given a relatively low proton density (PD) of myelin-proton, we utilized Cramér-Rao Lower Bound to encode myelin-proton with the maximal SNR efficiency for optimizing the MR fingerprinting design, in order to improve the sensitivity of the sequence to myelin-proton. In addition, with a second echo of approximately 3 ms, myelin-water component can be also captured. A myelin-tissue (myelin-proton and myelin-water) fraction mapping can be thus calculated. The optimized 3D UTE-MRF with dual-echo acquisition is tested in simulations, physical phantom and in vivo studies of both healthy subjects and multiple sclerosis patients. The results suggest that the rapidly decayed myelin-proton and myelin-water signal can be depicted with UTE signals of our method at clinically relevant resolution (1.8 mm isotropic) in 15 min. With its good sensitivity to myelin loss in multiple sclerosis patients demonstrated, our method for the whole brain myelin-tissue fraction mapping in clinical friendly scan time has the potential for routine clinical imaging.

Increased Free Water in the Substantia Nigra in Asymptomatic LRRK2 G2019S Mutation Carriers.

BACKGROUND: The alteration of substantia nigra (SN) degeneration in populations at risk of Parkinson's disease (PD) is unclear. OBJECTIVE: We investigated free water (FW) values in the posterior SN (pSN) in asymptomatic LRRK2 G2019S mutation carriers. METHODS: We analyzed diffusion imaging data from 28 asymptomatic LRRK2 G2019S mutation carriers and 30 healthy controls (HCs), whereas 11 asymptomatic LRRK2 G2019S carriers and 11 HCs were followed up. FW values in the pSN were measured and compared between the groups. The relationship between longitudinal changes in FW in the pSN and dopamine transporter striatal binding ratio (SBR) was analyzed. RESULTS: FW values in the pSN were significantly elevated and kept increasing during follow-up in asymptomatic LRRK2 G2019S carriers. There was a negative correlation between FW changes in the left pSN and SBR changes in the left putamen. CONCLUSION: FW in the pSN has the potential to be a progression imaging marker of early dopaminergic degeneration in the population at risk of PD. © 2022 International Parkinson and Movement Disorder Society.

Free-Water Imaging of the Substantia Nigra in GBA Pathogenic Variant Carriers.

BACKGROUND: Pathogenic variants in the glucocerebrosidase gene (GBA) have been identified as the most common genetic risk factor for Parkinson's disease (PD). However, the features of substantia nigra damage in GBA pathogenic variant carriers remain unclear. OBJECTIVE: We aimed to evaluate the microstructural changes in the substantia nigra in non-manifesting GBA pathogenic variant carriers (GBA-NMC) and PD patients with GBA pathogenic variant (GBA-PD) with free-water imaging. METHODS: First, we compared free water values in the posterior substantia nigra between non-manifesting non-carriers (NMNC, n = 29), GBA-NMC (n = 26), and GBA-PD (n = 16). Then, free water values in the posterior substantia nigra were compared between GBA-PD and early- (n = 19) and late-onset (n = 40) idiopathic PD (iPD) patients. Furthermore, we examined whether the baseline free water values could predict the progressions of clinical symptoms. RESULTS: The free water values in the posterior substantia nigra were significantly higher in the GBA-NMC and GBA-PD groups compared to NMNC, and were significantly increased in the GBA-PD group than both early- and late-onset iPD. Free water values in the posterior substantia nigra could predict the progression of anxiety and cognitive decline in GBA-NMC and GBA-PD groups. CONCLUSIONS: We demonstrate that free water values are elevated in the substantia nigra and predict the development of non-motor symptoms in GBA-NMC and GBA-PD. Our findings demonstrate that a significant nigral impairment already exists in GBA-NMC, and nigral injury may be more severe in GBA-PD than in iPD. These results support that free-water imaging can as a potential early marker of substantia nigra damage. © 2023 International Parkinson and Movement Disorder Society.

Increased Free Water in the Putamen in Idiopathic REM Sleep Behavior Disorder.

BACKGROUND: It has been suggested that the loss of nigrostriatal dopaminergic axon terminals occurs before the loss of dopaminergic neurons in the substantia nigra (SN) in Parkinson's disease (PD). This study aimed to use free-water imaging to evaluate microstructural changes in the dorsoposterior putamen (DPP) of idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) patients, which is considered a prodromal stage of synucleinopathies. METHODS: Free water values in the DPP, dorsoanterior putamen (DAP), and posterior SN were compared between the healthy controls (n = 48), iRBD (n = 43) and PD (n = 47) patients. In iRBD patients, the relationships between baseline and longitudinal free water values and clinical manifestations or dopamine transporter (DAT) striatal binding ratio (SBR) were analyzed. RESULTS: Free water values were significantly higher in the DPP and posterior substantia nigra (pSN), but not in the DAP, in the iRBD and PD groups than in controls. In iRBD patients, free water values in the DPP were progressively increased and correlated with the progression of clinical manifestations and the striatal DAT SBR. Baseline free water in the DPP was negatively correlated with striatal DAT SBR and hyposmia and positively correlated with motor deficits. CONCLUSIONS: This study demonstrates that free water values in the DPP are increased cross-sectionally and longitudinally and associated with clinical manifestations and the function of the dopaminergic system in the prodromal stage of synucleinopathies. Our findings indicate that free-water imaging of the DPP has the potential to be a valid marker of early diagnosis and progression of synucleinopathies. © 2023 International Parkinson and Movement Disorder Society.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Deep learning-based method for reducing residual motion effects in diffusion parameter estimation.

PURPOSE: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects. METHODS: The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting. A deep learning-based parameter estimation algorithm, using a hierarchical convolutional neural network (H-CNN), was combined with motion assessment and corrupted volume rejection. The method was designed to overcome the limitations of existing methods of this kind that produce parameter estimations whose quality depends strongly on a proportion of the data discarded. Evaluation experiments were conducted for the estimation of diffusion kurtosis and diffusion-tensor-derived measures at both the individual and group levels. The performance was compared with the robust approach of iteratively reweighted linear least squares (IRLLS) after motion correction with and without outlier replacement. RESULTS: Compared with IRLLS, the H-CNN-based technique is minimally sensitive to motion effects. It was tested at severe motion levels when 70% to 90% of the data are rejected and when random motion is present. The technique had a stable performance independent of the numbers and schemes of data rejection. A further test on a data set from children with attention-deficit hyperactivity disorder shows the technique can potentially ameliorate spurious group-level difference caused by head motion. CONCLUSION: This method shows great potential for reducing residual motion effects in motion-corrupted diffusion-weighted-imaging data, bringing benefits that include reduced bias in derived metrics in individual scans and reduced motion-level-dependent bias in population studies employing diffusion MRI.

Reproducibility of volume and asymmetry measurements of hippocampus, amygdala, and entorhinal cortex on traveling volunteers: a multisite MP2RAGE prospective study.

BACKGROUND: Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures-including the hippocampus, amygdala, and entorhinal cortex-in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases. PURPOSE: To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset. MATERIAL AND METHODS: Three traveling individuals were scanned at 10 sites, which were equipped with the same equipment (3T Prisma Siemens). They used the same scan protocol (two inversion-contrast magnetization-prepared rapid gradient echo sequences) and operators. Validity coefficients (intraclass correlations coefficient [ICC]) and spatial overlap measures (Dice Similarity Coefficient [DSC]) were used to estimate the reproducibility of multisite data. RESULTS: ICC and DSC values varied substantially among structures and segmentation methods, and values of manual tracing were relatively higher than the automated method. ICC and DSC values of structural parameters were greater than 0.80 and 0.60 across sites, as determined by manual tracing. Low reproducibility was observed in the amygdala parameters by automatic segmentation method (ICC = 0.349-0.529, DSC = 0.380-0.873). However, ICC and DSC scores of the hippocampus were higher than 0.60 and 0.65 by two segmentation methods. CONCLUSION: This study suggests that a well-controlled multisite study could provide a reliable MRI dataset. Manual tracing of volume assessments is recommended for low reproducibility structures that require high levels of precision in multisite studies.

MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times.

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent. This may confound the interpretation of studies as one cannot disentangle differences in diffusion from those in T2 relaxation. To address this challenge, we propose a multi-TE NODDI (MTE-NODDI) technique, inspired by recent studies exploiting the synergy between diffusion and T2 relaxation. MTE-NODDI could give robust estimates of the non-T2-weighted signal fractions and compartment-specific T2 values, as demonstrated by both simulation and in vivo data experiments. Results showed that the estimated non-T2 weighted intra-neurite fraction and compartment-specific T2 values in white matter were consistent with previous studies. The T2-weighted intra-neurite fractions from the original NODDI were found to be overestimated compared to their non-T2-weighted estimates; the overestimation increases with TE, consistent with the reported intra-neurite T2 being larger than extra-neurite T2. Finally, the inclusion of the free water compartment reduces the estimation error in intra-neurite T2 in the presence of cerebrospinal fluid contamination. With the ability to disentangle non-T2-weighted signal fractions from compartment-specific T2 relaxation, MTE-NODDI could help improve the interpretability of future neuroimaging studies, especially those in brain development, maturation and aging.

Evaluation of the diffusion MRI white matter tract integrity model using myelin histology and Monte-Carlo simulations.

Quantitative evaluation of brain myelination has drawn considerable attention. Conventional diffusion-based magnetic resonance imaging models, including diffusion tensor imaging and diffusion kurtosis imaging (DKI),1 have been used to infer the microstructure and its changes in neurological diseases. White matter tract integrity (WMTI) was proposed as a biophysical model to relate the DKI-derived metrics to the underlying microstructure. Although the model has been validated on ex vivo animal brains, it was not well evaluated with ex vivo human brains. In this study, histological samples (namely corpus callosum) from postmortem human brains have been investigated based on WMTI analyses on a clinical 3T scanner and comparisons with gold standard myelin staining in proteolipid protein and Luxol fast blue. In addition, Monte Carlo simulations were conducted to link changes from ex vivo to in vivo conditions based on the microscale parameters of water diffusivity and permeability. The results show that WMTI metrics, including axonal water fraction AWF, radial extra-axonal diffusivity De⊥, and intra-axonal diffusivity Dawere needed to characterize myelin content alterations. Thus, WMTI model metrics are shown to be promising candidates as sensitive biomarkers of demyelination.

Effect of myelin water exchange on DTI-derived parameters in diffusion MRI: Elucidation of TE dependence.

PURPOSE: Water exchange exists between different neuronal compartments of brain tissue but is often ignored in most diffusion models. The goal of the current study was to demonstrate the dependence of diffusion measurements on echo time (TE) in the human brain and to investigate the underlying effects of myelin water exchange. METHODS: Five healthy subjects were examined with single-shot pulsed-gradient spin-echo echo-planar imaging with fixed duration (δ) and separation (Δ) of diffusion gradient pulses and a set of varying TEs. The effects of water exchange and intrinsic T2 difference in cellular environments were investigated with Monte Carlo simulations. RESULTS: Both in vivo measurements and simulations showed that fractional anisotropy (FA) and axial diffusivity (AD) had positive correlations with TE, while radial diffusivity (RD) showed a negative correlation, which is consistent with a previous study. The simulation results further indicated the sensitivity of TE dependence to the change of g-ratio. CONCLUSION: The exchange between myelin and intra/extra-axonal water pools often plays a non-negligible role in the observed TE dependence of diffusion parameters, which may accompany or alter the effect of intrinsic T2 in causing such dependence. The TE dependence may potentially serve as a biomarker for demyelination processes (e.g., in multiple sclerosis and Alzheimer's disease). Magn Reson Med 79:1650-1660, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Whole brain atlas-based diffusion kurtosis imaging parameters for the evaluation of multiple cognitive-related brain microstructure injuries after radiotherapy in lung cancer patients with brain metastasis.

Background: Whole brain radiation therapy (WBRT) can cause cognitive dysfunctions in lung cancer patients with brain metastasis (BM). Diffusion kurtosis imaging (DKI) can detect brain microstructural alterations sensitivly. We aimed to identify the potential of DKI parameters for early radiation-induced brain injury and investigate the association between microstructure changes and neurocognitive function (NCF) decline. Methods: Lung cancer patients with BM (n=35) who underwent WBRT in a single center in Zhejiang, China, were consecutively and prospectively enrolled between June 24th, 2020 and December 22nd, 2021, and the median follow-up time was 6.0 months (3.6-6.6 months). DKI and T1-weighted (T1W) MRI scans were acquired prior to and following WBRT. Diffusivity-based (mean diffusivity, MD; fractional anisotropy, FA) and kurtosis-based (mean kurtosis, MK; axial kurtosis, AK) parameters were calculated within the automated anatomical labeling (AAL) atlas-based regions. Reliable change indices practice effects (RCI-PE) scores of the Mini-Mental State Examination (MMSE) were calculated to determine significant neurocognitive decline by a one-sample t-test from baseline to 2-6 months post-WBRT. To assess the subacute induced effects within the whole brain, percentage changes of DKI parameters were evaluated at 170 atlas-based regions by a one-sample t-test. Linear regression analyses were used to evaluate the association between DKI parameter changes and RCI-PE scores. Results: Finally, the study included 19 patients in the longitudinal follow-up. RCI-PE scores declined at 2-6 months post-WBRT (mean RCI-PE =-0.842, 95% CI, -0.376 to -1.310; P=0.002). With the atlas-based analysis of subacute effects after post-WBRT, a total of 28 regions changed in at least one diffusion parameter, revealing region-wise microstructural alterations in the brain. Significant correlations of at least one diffusion parameters with RCI-PEs were observed in 9 regions, such as the right orbital part of the inferior frontal gyrus [right IFGorb, r(AK) =0.47, P=0.03] and left middle temporal gyrus [left MTG, r(MK) =-0.49, P=0.03]. Conclusions: DKI parameters can be used to detect early microstructure changes and represent important imaging predictors for cognitive decline. The reported 9 regions are more particularly vulnerable to neurocognitive radiation-induced impairment for lung cancer patients with BM, representing potential dose-avoidance targets for cognitive function preservation.

Quantitative Susceptibility Mapping and Free Water Imaging of Substantia Nigra in Parkinson's Disease.

BACKGROUND: The utility of imaging methods to detect iron content in the substantia nigra pars compacta (SNc) and free water imaging in the posterior substantia nigra (pSN) has the potential to be imaging markers for the detection of Parkinson's disease (PD). OBJECTIVE: This study aimed to compare the discriminative power of above methods, and whether the combination can improve the diagnostic potential of PD. METHODS: Quantitative susceptibility mapping (QSM) and diffusion-weighted data were obtained from 41 healthy controls (HC), 37 patients with idiopathic REM sleep behavior disorder (RBD), and 65 patients with PD. Mean QSM values of bilateral SNc and mean isotropic volume fraction (Viso) values of bilateral pSN (mean QSM|Viso values of bilateral SNc|pSN) were separately calculated and compared among the groups. RESULTS: Mean QSM|Viso values of bilateral SNc|pSN were significantly higher for RBD and PD patients compared to HC and were significantly higher in PD patients than in RBD patients. The power of the mean QSM|Viso values of bilateral SNc|pSN and combined mean QSM and Viso values was 0.873, 0.870, and 0.961 in discriminating PD and HC, 0.779, 0.719, and 0.864 in discriminating RBD from HC, 0.634, 0.636, and 0.689 in discriminating PD and RBD patients. CONCLUSION: QSM and free water imaging have similar discriminative power in the detection of prodromal and clinical PD, while combination of these two methods increases discriminative power. Our findings suggest that the combination of QSM and free water imaging has the potential to become an imaging marker for the diagnosis of PD.

Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease.

OBJECTIVES: The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson's disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD. METHODS: A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1). Meanwhile, an additional five healthy volunteers were scanned with both scanner-A and an additional scanner-B (3 T Prisma) (DATASET-2). Diffusion kurtosis imaging (DKI) of DATASET-2 had an extra b shell compared to DATASET-1. In addition, a 3D-convolutional neural network (CNN) was trained from DATASET-2 to harmonize the quality of scalar measures of scanner-A to a similar level as scanner-B. Whole-brain unpaired t test and Tract-Based Spatial Statistics (TBSS) were performed to validate the differences between the PD and control groups using the model-fitting method and CNN-based method, respectively. We further clarified the correlation between clinical assessments and DKI results. RESULTS: An increase in mean diffusivity (MD) was found in the left substantia nigra (SN) in the PD group. In the right SN, fractional anisotropy (FA) and mean kurtosis (MK) values were negatively correlated with Hoehn and Yahr (H&Y) scales. In the putamen (Put), FA values were positively correlated with the H&Y scales. It is worth noting that these findings were only observed with the deep learning method. There was neither a group difference nor a correlation with clinical assessments in the SN or striatum exceeding the significance level using the conventional model-fitting method. CONCLUSIONS: The CNN-based method improves the robustness of DKI and can help to explore PD-associated imaging features.

Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings.

Multicenter diffusion magnetic resonance imaging (MRI) has drawn great attention recently due to the expanding need for large-scale brain imaging studies, whereas the variability in MRI scanners and data acquisition tends to confound reliable individual-based analysis of diffusion measures. In addition, a growing number of multi-shell diffusion models have been shown with the potential to generate various estimates of physio-pathological information, yet their reliability and reproducibility in multicenter studies remain to be assessed. In this article, we describe a multi-shell diffusion dataset collected from three traveling subjects with identical acquisition settings in ten imaging centers. Both the scanner type and imaging protocol for anatomical and diffusion imaging were well controlled. This dataset is expected to replenish individual reproducible studies via multicenter collaboration by providing an open resource for advanced and novel microstructural and tractography modelling and quantification.

A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols.

Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outcomes has also drawn much attention recently. One fundamental issue arises in how to derive model parameters reliably from image data of varying quality. This issue is even more challenging for advanced diffusion methods such as diffusion kurtosis imaging (DKI). Recently, deep learning-based methods have been demonstrated with their potential for robust and efficient computation of diffusion-derived measures. Inspired by these approaches, the current study specifically designed a framework based on a three-dimensional hierarchical convolutional neural network, to jointly reconstruct and harmonize DKI measures from multicenter acquisition to reformulate these to a state-of-the-art hardware using data from traveling subjects. The results from the harmonized data acquired with different protocols show that: 1) the inter-scanner variation of DKI measures within white matter was reduced by 51.5% in mean kurtosis, 65.9% in axial kurtosis, 53.7% in radial kurtosis, and 61.5% in kurtosis fractional anisotropy, respectively; 2) data reliability of each single scanner was enhanced and brought to the level of the reference scanner; and 3) the harmonization network was able to reconstruct reliable DKI values from high data variability. Overall the results demonstrate the feasibility of the proposed deep learning-based method for DKI harmonization and help to simplify the protocol setup procedure for multicenter scanners with different hardware and software configurations.

Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network.

PURPOSE: In diffusion-weighted magnetic resonance imaging (DW-MRI), the fiber orientation distribution function (fODF) is of great importance for solving complex fiber configurations to achieve reliable tractography throughout the brain, which ultimately facilitates the understanding of brain connectivity and exploration of neurological dysfunction. Recently, multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) method has been explored for reconstructing full fODFs. To achieve a reliable fitting, similar to other model-based approaches, a large number of diffusion measurements is typically required for MSMT-CSD method. The prolonged acquisition is, however, not feasible in practical clinical routine and is prone to motion artifacts. To accelerate the acquisition, we proposed a method to reconstruct the fODF from downsampled diffusion-weighted images (DWIs) by leveraging the strong inference ability of the deep convolutional neural network (CNN). METHODS: The method treats spherical harmonics (SH)-represented DWI signals and fODF coefficients as inputs and outputs, respectively. To compensate for the reduced gradient directions with reduced number of DWIs in acquisition in each voxel, its surrounding voxels are incorporated by the network for exploiting their spatial continuity. The resulting fODF coefficients are fitted with applying the CNN in a multi-target regression model. The network is composed of two convolutional layers and three fully connected layers. To obtain an initial evaluation of the method, we quantitatively measured its performance on a simulated dataset. Then, for in vivo tests, we employed data from 24 subjects from the Human Connectome Project (HCP) as training set and six subjects as test set. The performance of the proposed method was primarily compared to the super-resolved MSMT-CSD with the decreasing number of DWIs. The fODFs reconstructed by MSMT-CSD from all available 288 DWIs were used as training labels and the reference standard. The performance was quantitatively measured by the angular correlation coefficient (ACC) and the mean angular error (MAE). RESULTS: For the simulated dataset, the proposed method exhibited the potential advantage over the model reconstruction. For the in vivo dataset, it achieved superior results over the MSMT-CSD in all the investigated cases, with its advantage more obvious when a limited number of DWIs were used. As the number of DWIs was reduced from 95 to 25, the median ACC ranged from 0.96 to 0.91 for the CNN, but 0.93 to 0.77 for the MSMT-CSD (with perfect score of 1). The angular error in the typical regions of interest (ROIs) was also much lower, especially in multi-fiber regions. The average MAE for the CNN method in regions containing one, two, three fibers was, respectively, 1.09°, 2.75°, and 8.35° smaller than the MSMT-CSD method. The visual inception of the fODF further confirmed this superiority. Moreover, the tractography results validated the effectiveness of the learned fODF, in preserving known major branching fibers with only 25 DWIs. CONCLUSION: Experiments on HCP datasets demonstrated the feasibility of the proposed method in recovering fODFs from up to 11-fold reduced number of DWIs. The proposed method offers a new streamlined reconstruction procedure and exhibits promising potential in acquisition acceleration for the reconstruction of fODFs with good accuracy.

Reproducibility of multi-shell diffusion tractography on traveling subjects: A multicenter study prospective.

Reproducibility of multicenter diffusion magnetic resonance imaging has drawn more attention recently due to rapidly increasing need for large-size brain imaging studies. Advanced multi-shell diffusion models are recommended for their potentials to provide variety of physio-pathological information. While previous studies have investigated the consistency of single-shell diffusion acquisition from various hardware and protocols, a well-controlled study with multi-shell acquisition would be necessary to understand the inherent factors of reproducibility from new complexity of such acquisition protocol. In this study, three traveling subjects were scanned at eight imaging centers equipped with the same type of scanners using the same multi-shell diffusion imaging protocol. Track density imaging and structure connectomes were investigated in local-scale distribution and in distal-scale connectivity, respectively. With evaluations of the coefficient of variation and the intra-class correlation coefficient, our results indicated: 1) similar to single-shell schemes, the intra-center reproducibility of multi-shell is higher than inter-center; 2) multi-shell schemes produce higher reproducibility and precision among centers compared to the single-shell schemes; and 3) in addition to the diffusion schemes, image quality and the presence of complex fiber structure could also associated with multicenter reproducibility.