Diffusion-weighted Volume Research Articles

Finding the limits of deep learning clinical sensitivity with fractional anisotropy (FA) microstructure maps.

Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named "one-minute FA," which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to train DL networks and obtain FA maps even with 4 DW input volumes, far less than the minimum number of directions for the mathematical estimation of the DT. Here we investigated the impact of reducing the number of DW input volumes to 4 or 7, and evaluated the performance and clinical sensitivity of the corresponding DL networks trained to calculate FA, while comparing results also with those using our one-minute FA. Each network training was performed on the human connectome project open-access dataset that has a high resolution and many DW volumes, used to fit a ground truth FA. To evaluate the generalizability of each network, they were tested on two external clinical datasets, not seen during training, and acquired on different scanners with different protocols, as previously done. Using 4 or 7 DW volumes, it was possible to train DL networks to obtain FA maps with the same range of values as ground truth - map, only when using HCP test data; pathological sensitivity was lost when tested using the external clinical datasets: indeed in both cases, no consistent differences were found between patient groups. On the contrary, our "one-minute FA" did not suffer from the same problem. When developing DL networks for reduced acquisition times, the ability to generalize and to generate quantitative biomarkers that provide clinical sensitivity must be addressed.

DiMANI: diffusion MRI for anatomical nuclei imaging-Application for the direct visualization of thalamic subnuclei.

The thalamus is a centrally located and heterogeneous brain structure that plays a critical role in various sensory, motor, and cognitive processes. However, visualizing the individual subnuclei of the thalamus using conventional MRI techniques is challenging. This difficulty has posed obstacles in targeting specific subnuclei for clinical interventions such as deep brain stimulation (DBS). In this paper, we present DiMANI, a novel method for directly visualizing the thalamic subnuclei using diffusion MRI (dMRI). The DiMANI contrast is computed by averaging, voxelwise, diffusion-weighted volumes enabling the direct distinction of thalamic subnuclei in individuals. We evaluated the reproducibility of DiMANI through multiple approaches. First, we utilized a unique dataset comprising 8 scans of a single participant collected over a 3-year period. Secondly, we quantitatively assessed manual segmentations of thalamic subnuclei for both intra-rater and inter-rater reliability. Thirdly, we qualitatively correlated DiMANI imaging data from several patients with Essential Tremor with the localization of implanted DBS electrodes and clinical observations. Lastly, we demonstrated that DiMANI can provide similar features at 3T and 7T MRI, using varying numbers of diffusion directions. Our results establish that DiMANI is a reproducible and clinically relevant method to directly visualize thalamic subnuclei. This has significant implications for the development of new DBS targets and the optimization of DBS therapy.

Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding.

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.

A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis.

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10−4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.

Abstract 119: The Effect Of Neuroprotectant NA1 On Early Infarct Growth Following Endovascular Therapy: The REPERFUSE-NA1 Study

Background: Early infarct growth after endovascular therapy (EVT) may lead to unfavorable outcomes despite successful reperfusion. The REPERFUSE-NA1 study replicated the preclinical NA1 experiment by investigating the effect of NA1 on early DWI infarct growth in acute ischemic stroke patients receiving EVT. Methods: REPERFUSE-NA1 was a sub-study of the randomized controlled trial ESCAPE-NA1 (ClinicalTrialGov NCT02930018). Patients received MRI within 5 hours and 24 hours of EVT. The primary outcome was early diffusion weighted (DWI) infarct growth. The secondary outcome was region-specific DWI infarct growth in the white matter, cortical grey matter and basal ganglia. Results: A total of 71 patients was included, of whom 67 had sufficient MR imaging at 5h and 24h post-EVT. For patients who received NA1 compared to placebo, the median age (68.8 v 67.5), baseline NIHSS (15.5 v 16), time from symptom onset to reperfusion (161 v 167 minutes) and mTICI 2b-3 (94.4% v 94.3%) were statistically not different. Median DWI volumes post-EVT (5h) were 13.0 mL (IQR, 5.9-28.1) in NA1 and 13.3 mL (IQR, 3.1-27.0) in placebo. At 24h median DWI volumes increased to 22.6 mL (IQR, 11.2-63.4) in the NA1 group and 22.4 mL (IQR, 7.4-52.3) in the placebo group, equating to a 48.4% DWI volume growth in the NA1 group and a 66.0% growth in the placebo group. Median DWI volume growth was 55.1% for NA1 patients who received alteplase compared to 41.3% for NA1 patients who did not receive alteplase (p=0.65). In terms of region-specific infarct volumes, absolute growth was 2.27mL (IQR, 0.70-6.19) in the white matter, 3.93 mL (IQR, 0.18-12.40) in cortical grey matter, and 1.20 mL (IQR, 0.31-2.62) in basal ganglia in the overall cohort. Between the NA1 and the placebo groups, lesion growth in the white matter (1.96 mL v 2.77mL), cortical gray matter (3.93 mL v 4.07 mL) and basal ganglia (1.62 mL v 0.89 mL) did not differ significantly. Conclusion: There was substantial infarct growth early after EVT, with the largest absolute growth in cortical grey matter. No significant effect of NA1 on early infarct growth was observed.

Abstract WP259: Acute Stroke Neuroprotection With Intravenous Rns60 In The Cynomolgus Macaque

Introduction: RNS60 is an experimental therapy comprised of stable oxygenated nanobubbles in a sodium chloride solution with an oxygen content of 55+/-5 ppm. Phase I/II clinical trials established a safety profile for RNS60 in patients with ALS and MS. In-vitro assays in neurons have demonstrated mitochondrial biogenesis with RNS60. Therefore, we hypothesized that RNS60 would protect the ischemic penumbra and delay stroke evolution. In this study, we assessed RNS60 in a cynomolgus macaque (CM) model of transient middle cerebral artery occlusion (MCAO). Methods: Experimenters were blinded and CMs randomly allocated to treatment. In the first experiment, 22 male CMs (2.6+/-0.55 kg) were anesthetized with physiologic monitoring and MCAO followed by immediate MRI imaging to define perfusion deficit. RNS60 (5cc/kg/h IV over 1h followed by 2.5cc/kg/h for 48h) or drug vehicle were administered starting 1h following MCAO. MCA was reperfused 90min after MCAO. CMs underwent MRI diffusion-weighted (DWI) and T2 imaging at 48h and 30d after MCAO. Serial Non-Human Primate Stroke Scale (NHPSS) assessments were collected over 30 days following MCAO. In the second experiment, 10 male CMs (4.95+/-0.18 kg) underwent permanent MCAO. RNS60 or drug vehicle were infused starting 5 minutes after MCAO (5cc/kg/h IV for 1 hour followed by 2.5cc/kg/h until sacrifice). Perfusion and DWI MRI images were obtained serially over 6 hours to measure the evolution of the penumbra. CMs were sacrificed at the completion of the experiment. Results: DWI stroke volume was significantly reduced 4.3+/-1.1mL vs. 9.1+/-0.92mL (P=0.0036) at 48 hours post MCAO in RNS60 vs. placebo treated CMs. This result was conserved on T2 imaging at 30 days where stroke volume was 3.5+/-0.86mL vs. 7.3+/-0.79mL (P=0.004). RNS60 significantly improved NHPSS scores over 30 days compared to controls after removing three outlier CMs (2 mortalities and one non-reperfused MCA). The results of the second experiment demonstrated a 6h DWI volume of 5.9cc in RNS60 treated CMs compared to 10.6cc in controls. Conclusion: RNS60 administered during acute ischemic stroke significantly reduces stroke volume and improves functional outcomes in male CMs. The evolution of penumbra to stroke is slowed following RNS60 administration.

Participant factors that contribute to magnetic resonance imaging motion artifacts in children with mild traumatic brain injury or orthopedic injury.

Motion can compromise image quality and confound results, especially in pediatric research. This study evaluated qualitative and quantitative approaches to motion artifacts detection and correction, and whether motion artifacts relate to injury history, age, or sex in children with mild traumatic brain injury or orthopedic injury relative to typically developing children. The concordance between qualitative and quantitative motion ratings was also examined. Children aged 8-16years with mild traumatic brain injury (n = 141) or orthopedic injury (n = 73) were recruited from the emergency department and completed an MRI scan roughly 2weeks post-injury. Typically developing children (n = 41) completed a single MRI scan. T1- and diffusion-weighted images were visually inspected and rated for motion artifacts by trained examiners. Quantitative estimates of motion artifacts were derived from FreeSurfer and FSL. Age (younger > older) and sex (boys > girls) were significantly associated with motion artifacts on both T1- and diffusion-weighted images. Children with mild traumatic brain or orthopedic injury had significantly more motion-corrupted diffusion-weighted volumes than typically developing children, but mild traumatic brain injury and orthopedic injury groups did not differ from each other. The exclusion of motion-corrupted volumes did not significantly change diffusion tensor imaging metrics. Results indicate that automated quantitative estimates of motion artifacts, which are less labour-intensive than manual methods, are appropriate. Results have implications for the reliability of structural MRIresearch and highlight the importance of considering motion artifacts in studies of pediatric mild traumatic brain injury.

Sex-specific differences in white matter microvascular integrity after ischaemic stroke

Background and purposeFunctional outcomes after ischaemic stroke are worse in women, despite adjusting for differences in comorbidities and treatment approaches. White matter microvascular integrity represents one risk factor for poor...

Early molecular oxidative stress biomarkers of ischemic penumbra in acute stroke

To assess whether plasma biomarkers of oxidative stress predict diffusion-perfusion mismatch in patients with acute ischemic stroke (AIS). We measured plasma levels of oxidative stress biomarkers such as F2-isoprostanes (F2-isoPs), total and perchloric acid Oxygen Radical Absorbance Capacity (ORACTOT and ORACPCA), urinary levels of 8-oxo-7,8-dihydro-2'-deoxyguoanosine, and inflammatory and tissue-damage biomarkers (high-sensitivity C-reactive protein, matrix metalloproteinase-2 and -9) in a prospective study of patients with AIS presenting within 9 hours of symptom onset. Diffusion-weighted (DWI) and perfusion-weighted (PWI) MRI sequences were analyzed with a semiautomated volumetric method. Mismatch was defined as baseline mean transit time volume minus DWI volume. A percent mismatch cutoff of >20% was considered clinically significant. A stricter definition of mismatch was also used. Mismatch salvage was the region free of overlap by final infarction. Mismatch >20% was present in 153 of 216 (70.8%) patients (mean [±SD] age 69.2 ± 14.3 years, 41.2% women). Patients with mismatch >20% were more likely to have higher baseline plasma levels of ORACPCA (p = 0.020) and F2-isoPs (p = 0.145). Multivariate binary logistic regression demonstrated that lnF2-isoP (odds ratio [OR] 2.44, 95% confidence interval [CI] 1.19-4.98, p = 0.014) and lnORACPCA (OR 4.18, 95% CI 1.41-12.41, p = 0.010) were independent predictors of >20% PWI-DWI mismatch and the stricter mismatch definition, respectively. lnORACTOT significantly predicted mismatch salvage volume (>20% mismatch p = 0.010, stricter mismatch definition p = 0.003). Elevated hyperacute plasma levels of F2-isoP and ORAC are associated with radiographic evidence of mismatch and mismatch salvage in patients with AIS. If validated, these findings may add to our understanding of the role of oxidative stress in cerebral tissue fate during acute ischemia.

Reproducibility of rectal tumor volume delineation using diffusion-weighted MRI: Agreement on volumes between observers

PurposeTo retrospectively evaluate the inter-observer agreement between a radiologist and a radiation oncologist and volume differences, in T2 and diffusion-weighted (DWI) MRI of gross tumor volume (GTV) delineation, in rectal cancer patients. Materials and methodsTwo observers, a radiologist and a radiation oncologist, delineated GTVs of 50 patients on T2-weighted MRI (T2GTV) and echo planar DWI (DWIGTV). Observers agreement was assessed using DICE index, Bland-Altman analysis and intra-class correlation coefficient (ICC). Student's t-test was used for GTV comparison. ResultsMedian T2GTV and DWIGTV were 17.09±14.12 cm3 (1.92–62.03) and 12.79±12.31 cm3 (1.23–62.25) for radiologist, and 16.82±13.66 cm3 (1.78–65.9) and 13.72±12.77 cm3 (1.29–69.75) for radiation oncologist. T2GTV were significantly larger compared to DWIGTV (P<0.001 and P<0.001, for both observers). Mean DICE index for T2GTV and DWIGTV were 0.80±0.07 and 0.77±0.06. The mean difference between the two observers were 0.26cm3 (95% CI: −5.36 to 5.88) and −1.13cm3 (95% CI: −5.70 to 3.44) for T2 and DWI volumes. The ICC for T2 volumes was 0.989 (95% CI: 0.981–0.994) (P<0.001) and 0.992 (95% CI: 0.986–0.996) (P<0.001) for DWI volumes. ConclusionDWI resulted in smaller volumes delineation compared to T2-weighted MRI. Substantial and almost perfect agreements were reported for DWIGTV and T2GTV between radiologist and radiation oncologist. Due to the fact that DWI could be considered a simple technique for volume delineation for radiation oncologist, DWI could be used to improve quality in radiation planning for an accurate boost volume delineation when a dose escalation is investigated.

Abstract WP66: CT and Multiphase CTA Alone Can Identify Thrombectomy Candidates Beyond 6 Hours

Introduction: Current stroke guidelines recommend CT Perfusion (CTP) or MRI to identify thrombectomy candidates beyond 6 hours from stroke onset. The efficacy of more readily available NCCT and CTA in the identification of such patients is unknown. In this study, we compared the accuracy of arterial phase CTA only (sCTA) and multi-phase CTA (mCTA) with MRI in patients beyond 6 hours from onset. Methods: We retrospectively reviewed 45 consecutive patients, presenting 6 to 24 hours from stroke onset, with known ICA or M1 occlusions. All patients had pre-treatment CTA and MRI, with diffusion weighted (DWI) and perfusion weighted imaging (PWI). MRI images were processed using RAPID. Five experienced readers independently scored ASPECTS (0-10), sCTA collateral score (0-5), and whether patients would have met DAWN or DEFUSE-3 MRI imaging criteria. After a one-month delay, grading was repeated by the same readers using NCCT and mCTA. The sensitivity and specificity for DAWN and DEFUSE-3 eligibility was examined for both sCTA and mCTA. We also examined the median DWI volume based on the combination of NCCT ASPECTS and collateral score, for both sCTA and mCTA. Results: Sensitivity and specificity for DAWN eligibility was 91.4% and 74.8% for sCTA; 87.2% and 76.8% for mCTA. For DEFUSE-3, sensitivity and specificity was 92.3% and 46.3% for sCTA; 94.5% and 57.5% for mCTA. The higher specificity for DAWN is likely due to the more restrictive imaging criteria used in that trial. For DWI volume, the results are summarized in the graph, and show that the combination of mCTA collateral score and NCCT ASPECTS is significantly more correlated with DWI volume than sCTA (p=.0018). Conclusions: In conclusion, the combination of NCCT ASPECTS and mCTA collateral score has high sensitivity and moderate specificity for identifying patients who meet DAWN or DEFUSE-3 criteria. NCCT ASPECTS and Multi-phase CTA collateral score are significantly more correlated with DWI volume than single-phase.

Multifold Acceleration of Diffusion MRI via Deep Learning Reconstruction from Slice-Undersampled Data.

Diffusion MRI (dMRI), while powerful for characterization of tissue microstructure, suffers from long acquisition time. In this paper, we present a method for effective diffusion MRI reconstruction from slice-undersampled data. Instead of full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DW volumes corresponding to different diffusion wavevectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. The experimental results indicate a high acceleration factor of up to 5 can be achieved with minimal information loss.

Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks.

Diffusion MRI (dMRI), while powerful for the characterization of tissue microstructure, suffers from long acquisition times. In this paper, we propose a super-resolution (SR) reconstruction method based on orthogonal slice-undersampling for accelerated dMRI acquisition. Instead of scanning full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DW volumes corresponding to different diffusion wave-vectors can be harnessed using graph convolutional neural networks for reconstruction of the full DW volumes. We demonstrate that our SR reconstruction method outperforms typical interpolation methods and mitigates partial volume effects. Experimental results indicate that acceleration up to a factor of 5 can be achieved with minimal information loss.

Diffusion tensor imaging of the anterior cruciate ligament graft

A great need exists for objective biomarkers to assess graft healing following ACL reconstruction to guide the time of return to sports. The purpose of this study was to evaluate the feasibility and reliability of diffusion tensor imaging (DTI) to delineate the anterior cruciate ligament (ACL) graft and to investigate its diffusion properties using a clinical 3T scanner. DTI of the knee (b = 0, 400, and 800 s/mm2 , 10 diffusion directions, repeated 16 times for a total of 336 diffusion-weighted volumes) was performed at 3T in 17 patients between 3 and 7 months (mean, 4 months) following ACL reconstruction. Tractography was performed by two independent observers to delineate the ACL graft. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated within the graft. Interrater reliability was assessed using the intraclass correlation coefficient (ICC) and the scan-rescan reproducibility was evaluated based on the percentage coefficient of variance (%CV) across 20 repetition bootknife samples. In all subjects, tractography of the ACL graft was feasible. Quantitative evaluation of the diffusion properties of the ACL graft yielded the following mean ± SD values: FA = 0.23 ± 0.04; MD = 1.30 ± 0.11 × 10-3 mm2 /s; AD = 1.61 ± 0.12 × 10-3 mm2 /s, and RD = 1.15 ± 0.11 × 10-3 mm2 /s. Interrater reliability for the DTI parameters was excellent (ICC = 0.91-0.98). Mean %CVs for FA, MD, AD, and RD were 4.6%, 3.5%, 3.7%, and 4.4%, respectively. We demonstrated the feasibility and reliability of DTI for the visualization and quantitative evaluation of the ACL graft at 3T. 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1423-1432.

Extrapolation-Based References Improve Motion and Eddy-Current Correction of High B-Value DWI Data: Application in Parkinson's Disease Dementia.

PurposeConventional motion and eddy-current correction, where each diffusion-weighted volume is registered to a non diffusion-weighted reference, suffers from poor accuracy for high b-value data. An alternative approach is to extrapolate reference volumes from low b-value data. We aim to compare the performance of conventional and extrapolation-based correction of diffusional kurtosis imaging (DKI) data, and to demonstrate the impact of the correction approach on group comparison studies.MethodsDKI was performed in patients with Parkinson’s disease dementia (PDD), and healthy age-matched controls, using b-values of up to 2750 s/mm2. The accuracy of conventional and extrapolation-based correction methods was investigated. Parameters from DTI and DKI were compared between patients and controls in the cingulum and the anterior thalamic projection tract.ResultsConventional correction resulted in systematic registration errors for high b-value data. The extrapolation-based methods did not exhibit such errors, yielding more accurate tractography and up to 50% lower standard deviation in DKI metrics. Statistically significant differences were found between patients and controls when using the extrapolation-based motion correction that were not detected when using the conventional method.ConclusionWe recommend that conventional motion and eddy-current correction should be abandoned for high b-value data in favour of more accurate methods using extrapolation-based references.

A framework to analyze cerebral mean diffusivity using surface guided diffusion mapping in diffusion tensor imaging.

The mean diffusivity (MD) value has been used to describe microstructural properties in Diffusion Tensor Imaging (DTI) in cortical gray matter (GM). Recently, researchers have applied a cortical surface generated from the T1-weighted volume. When the DTI data are analyzed using the cortical surface, it is important to assign an accurate MD value from the volume space to the vertex of the cortical surface, considering the anatomical correspondence between the DTI and the T1-weighted image. Previous studies usually sampled the MD value using the nearest-neighbor (NN) method or Linear method, even though there are geometric distortions in diffusion-weighted volumes. Here we introduce a Surface Guided Diffusion Mapping (SGDM) method to compensate for such geometric distortions. We compared our SGDM method with results using NN and Linear methods by investigating differences in the sampled MD value. We also projected the tissue classification results of non-diffusion-weighted volumes to the cortical midsurface. The CSF probability values provided by the SGDM method were lower than those produced by the NN and Linear methods. The MD values provided by the NN and Linear methods were significantly greater than those of the SGDM method in regions suffering from geometric distortion. These results indicate that the NN and Linear methods assigned the MD value in the CSF region to the cortical midsurface (GM region). Our results suggest that the SGDM method is an effective way to correct such mapping errors.

Correction strategy for diffusion-weighted images corrupted with motion: application to the DTI evaluation of infants' white matter

ObjectiveDiffusion imaging techniques such as DTI and HARDI are difficult to implement in infants because of their sensitivity to subject motion. A short acquisition time is generally preferred, at the expense of spatial resolution and signal-to-noise ratio. Before estimating the local diffusion model, most pre-processing techniques only register diffusion-weighted volumes, without correcting for intra-slice artifacts due to motion or technical problems. Here, we propose a fully automated strategy, which takes advantage of a high orientation number and is based on spherical-harmonics decomposition of the diffusion signal. Material and methodsThe correction strategy is based on two successive steps: 1) automated detection and resampling of corrupted slices; 2) correction for eddy current distortions and realignment of misregistered volumes. It was tested on DTI data from adults and non-sedated healthy infants. ResultsThe methodology was validated through simulated motions applied to an uncorrupted dataset and through comparisons with an unmoved reference. Second, we showed that the correction applied to an infant group enabled to improve DTI maps and to increase the reliability of DTI quantification in the immature cortico-spinal tract. ConclusionThis automated strategy performed reliably on DTI datasets and can be applied to spherical single- and multiple-shell diffusion imaging.

Anatomical and functional volume concordance between FDG PET, and T2 and diffusion-weighted MRI for cervical cancer: a hybrid PET/MR study

To evaluate the concordance among (18)F-FDG PET imaging, MR T2-weighted (T2-W) imaging and apparent diffusion coefficient (ADC) maps with diffusion-weighted (DW) imaging in cervical cancer using hybrid whole-body PET/MR. This study prospectively included 35 patients with cervical cancer who underwent pretreatment (18)F-FDG PET/MR imaging. (18)F-FDG PET and MR images were fused using standard software. The percent of the maximum standardized uptake values (SUV max) was used to contour tumours on PET images, and volumes were calculated automatically. Tumour volumes measured on T2-W and DW images were calculated with standard techniques of tumour area multiplied by the slice profile. Parametric statistics were used for data analysis. FDG PET tumour volumes calculated using SUV max (14.30 ± 4.70) and T2-W imaging volume (33.81 ± 27.32 cm(3)) were similar (P > 0.05) at 35 % and 40 % of SUV max (32.91 ± 18.90 cm(3) and 27.56 ± 17.19 cm(3) respectively) and significantly correlated (P < 0.001; r = 0.735 and 0.766). The mean DW volume was 30.48 ± 22.41 cm(3). DW volumes were not significantly different from FDG PET volumes at either 35 % SUV max or 40 % SUV max or from T2-W imaging volumes (P > 0.05). PET subvolumes with increasing SUV max cut-off percentage showed an inverse change in mean ADC values on DW imaging (P < 0.001, ANOVA). Hybrid PET/MR showed strong volume concordance between FDG PET, and T2-W and DW imaging in cervical cancer. Cut-off at 35 % or 40 % of SUV max is recommended for (18)F-FDG PET/MR SUV-based tumour volume estimation. The linear tumour subvolume concordance between FDG PET and DW imaging demonstrates individual regional concordance of metabolic activity and cell density.

Estimation of the CSA‐ODF using Bayesian compressed sensing of multi‐shell HARDI

Diffusion MRI provides important information about the brain white matter structures and has opened new avenues for neuroscience and translational research. However, acquisition time needed for advanced applications can still be a challenge in clinical settings. There is consequently a need to accelerate diffusion MRI acquisitions. A multi-task Bayesian compressive sensing (MT-BCS) framework is proposed to directly estimate the constant solid angle orientation distribution function (CSA-ODF) from under-sampled (i.e., accelerated image acquisition) multi-shell high angular resolution diffusion imaging (HARDI) datasets, and accurately recover HARDI data at higher resolution in q-space. The proposed MT-BCS approach exploits the spatial redundancy of the data by modeling the statistical relationships within groups (clusters) of diffusion signal. This framework also provides uncertainty estimates of the computed CSA-ODF and diffusion signal, directly computed from the compressive measurements. Experiments validating the proposed framework are performed using realistic multi-shell synthetic images and in vivo multi-shell high angular resolution HARDI datasets. Results indicate a practical reduction in the number of required diffusion volumes (q-space samples) by at least a factor of four to estimate the CSA-ODF from multi-shell data. This work presents, for the first time, a multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions. It demonstrates improvement of the quality of acquired datasets by means of CS de-noising, and accurate estimation of the CSA-ODF, as well as enables a reduction in the acquisition time by a factor of two to four, especially when "staggered" q-space sampling schemes are used. The proposed MT-BCS framework can naturally be combined with parallel MR imaging to further accelerate HARDI acquisitions.

A Framework for Linear and Non-Linear Registration of Diffusion-Weighted MRIs Using Angular Interpolation

Registration of diffusion-weighted magnetic resonance images (DW-MRIs) is a key step for population studies, or construction of brain atlases, among other important tasks. Given the high dimensionality of the data, registration is usually performed by relying on scalar representative images, such as the fractional anisotropy (FA) and non-diffusion-weighted (b0) images, thereby ignoring much of the directional information conveyed by DW-MR datasets itself. Alternatively, model-based registration algorithms have been proposed to exploit information on the preferred fiber orientation(s) at each voxel. Models such as the diffusion tensor or orientation distribution function (ODF) have been used for this purpose. Tensor-based registration methods rely on a model that does not completely capture the information contained in DW-MRIs, and largely depends on the accurate estimation of tensors. ODF-based approaches are more recent and computationally challenging, but also better describe complex fiber configurations thereby potentially improving the accuracy of DW-MRI registration. A new algorithm based on angular interpolation of the diffusion-weighted volumes was proposed for affine registration, and does not rely on any specific local diffusion model. In this work, we first extensively compare the performance of registration algorithms based on (i) angular interpolation, (ii) non-diffusion-weighted scalar volume (b0), and (iii) diffusion tensor image (DTI). Moreover, we generalize the concept of angular interpolation (AI) to non-linear image registration, and implement it in the FMRIB Software Library (FSL). We demonstrate that AI registration of DW-MRIs is a powerful alternative to volume and tensor-based approaches. In particular, we show that AI improves the registration accuracy in many cases over existing state-of-the-art algorithms, while providing registered raw DW-MRI data, which can be used for any subsequent analysis.