MR diffusion kurtosis imaging for neural tissue characterization

Assessment the Impact of IDH Mutation Status on MRI Assessments of White Matter Integrity in Glioma Patients: Insights From Peak Width of Skeletonized Mean Diffusivity and Free Water Metrics

BACKGROUND

Gliomas are highly invasive brain tumors that evade accurate geographic assessment by conventional MRI due to microscopic invasion along white matter (WM) tracts. Advanced diffusion MRI techniques are needed to assess occult WM involvement.

PURPOSE

To evaluate peak width of skeletonized mean diffusivity (PSMD) and peak width of skeletonized free water (PSFW), and axonal water fraction (AWF) for assessing glioma-induced alterations in normal-appearing WM and their relationship with isocitrate dehydrogenase 1 (IDH1) mutation.

STUDY TYPE

Retrospective.

POPULATION

One hundred five glioma patients (46 ± 13 years), 53 healthy controls (HCs) (46 ± 9 years).

FIELD STRENGTH/SEQUENCE

3.0 T, T1WI, T1-CE, T2WI, T2FLAIR, and DKI.

ASSESSMENT

PSMD and PSFW were compared between lesion and contralateral sides in glioma patients and between patients and HCs. The associations between these metrics and clinical variables, including IDH1 mutation, was assessed. Corpus callosum (CC) injury, quantified by the AWF, was evaluated for its mediated effect of IDH1 mutation on contralesional PSMD and PSFW.

STATISTICAL TESTS

Paired-t tests, ANCOVA, univariate and multivariate linear regression, and mediation analysis with significance set at P < 0.05.

RESULTS

Contralateral PSMD and PSFW were significantly higher in left-sided gliomas (PSMD: 0.206 ± 0.027 vs. 0.193 ± 0.023; PSFW: 0.119 ± 0.019 vs. 0.106 ± 0.020) than in HCs, with similar increases in right-sided gliomas (PSMD: 0.219 ± 0.036 vs. 0.195 ± 0.023; PSFW: 0.129 ± 0.031 vs. 0.109 ± 0.020). IDH1 wild-type gliomas were associated with higher contralateral PSMD and PSFW (β = -0.302 and -0.412). AWF of CC mediated the impact of IDH1 mutations on contralesional PSMD and PSFW (mediated proportion: 42.7% and 53.7%).

DATA CONCLUSION

PSMD and PSFW are effective biomarkers for assessing WM integrity in gliomas, significantly associated with IDH1 mutation status. AWF of CC mediates the relationship between IDH1 mutation and contralesional PSMD and PSFW.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Application of Diffusional Kurtosis Imaging on Normal-Appearing White Matter in Cerebral Small Vessel Disease

BACKGROUND

This study aimed to investigate the diagnostic potential of diffusional kurtosis imaging (DKI) parameters in detecting pathological alterations in the normal-appearing white matter (NAWM) associated with cerebral small vessel disease (CSVD).

METHODS

A total of 56 patients diagnosed with CSVD were enrolled, all exhibiting confirmed lacunar infarction in the corticospinal tract (CST) as verified by conventional magnetic resonance imaging. A control group of 24 healthy individuals who exhibited no discernible abnormalities on conventional magnetic resonance imaging (MRI) scans was also included. The following DKI parameters were recorded, including mean kurtosis (MK), axial kurtosis (Ka), and radial kurtosis (Kr). Regions of interest were placed at representative levels of the CST on the affected side, encompassing the pons, anterior part of the posterior limb of the internal capsule (PLIC), corona radiata, and subcortex.

RESULTS

Variations in MK, Ka, and Kr values in the pons, anterior part of the PLIC, corona radiata, and subcortex of the control group were observed. Notably, the MK and Kr values of the normal-appearing pons in CSVD patients were significantly elevated compared with the control group. The MK, Ka value of the normal-appearing anterior part of the PLIC was significantly higher in the CSVD group than in the control group. The Kr value of the normal-appearing corona radiata exhibited a significant elevation in CSVD patients compared with the control group. Lastly, patients with CSVD displayed lower Ka values and higher Kr values in the normal-appearing subcortex compared with the control group.

CONCLUSIONS

DKI is an effective tool for assessing NAWM in patients with CSVD. These findings potentially offer novel insights into the prognosis of CSVD and serve as a foundational platform for future DKI studies on NAWM in other diffuse brain lesions.

Attention-Based Q-Space Deep Learning Generalized for Accelerated Diffusion Magnetic Resonance Imaging

Diffusion magnetic resonance imaging (dMRI) is a non-invasive method for capturing the microanatomical information of tissues by measuring the diffusion weighted signals along multiple directions, which is widely used in the quantification of microstructures. Obtaining microscopic parameters requires dense sampling in the q space, leading to significant time consumption. The most popular approach to accelerating dMRI acquisition is to undersample the q-space data, along with applying deep learning methods to reconstruct quantitative diffusion parameters. However, the reliance on a predetermined q-space sampling strategy often constrains traditional deep learning-based reconstructions. The present study proposed a novel deep learning model, named attention-based q-space deep learning (aqDL), to implement the reconstruction with variable q-space sampling strategies. The aqDL maps dMRI data from different scanning strategies onto a common feature space by using a series of Transformer encoders. The latent features are employed to reconstruct dMRI parameters via a multilayer perceptron. The performance of the aqDL model was assessed utilizing the Human Connectome Project datasets at varying undersampling numbers. To validate its generalizability, the model was further tested on two additional independent datasets. Our results showed that aqDL consistently achieves the highest reconstruction accuracy at various undersampling numbers, regardless of whether variable or predetermined q-space scanning strategies are employed. These findings suggest that aqDL has the potential to be used on general clinical dMRI datasets.

A diffusion kurtosis imaging study of the relationship between whole brain microstructure and cognitive function in older adults with mild cognitive impairment

BACKGROUND

The association of Mini-Mental State Examination (MMSE) with microstructure of individual regions across the entire brain remains unexplored.

PURPOSE

To investigate the relationship between cognitive function and the microstructure of each brain region in the gray matter using diffusion kurtosis imaging (DKI) in older adults with mild cognitive impairment (MCI), which is the transitional stage before the onset of dementia.

MATERIAL AND METHODS

DKI and MMSE were obtained for 34 older adults with MCI and 16 cognitively normal (CN) individuals aged 65-85 years. The DKI parameters were measured from 31 distinct regions of interest in the gray matter. A multiple regression analysis was used to examine the association between DKI parameters and MMSE scores; subsequently, interactions between the DKI parameters and the groups (MCI and CN) were examined.

RESULTS

The mean (±SD) MMSE score for the MCI group was 27.67 ± 1.90. Significant positive correlations were observed between MMSE score and mean kurtosis (MK) in the superior frontal, middle frontal, inferior frontal, precentral, postcentral, angular, middle temporal, and inferior occipital gyri, and superior parietal lobe for the MCI group. In addition, the interaction term of the MK in the middle frontal, precentral, postcentral, and angular gyri, and the groups was statistically significant.

CONCLUSION

Older adults with MCI may exhibit histological damage in certain regions of the brain, such as the middle frontal and angular gyri, as observed in this study. The findings could provide insights into understanding the pathophysiology of cognitive decline in this population group.

Prediction of isocitrate dehydrogenase mutation status in WHO grade II glioma by diffusion kurtosis imaging

Purpose

Isocitrate dehydrogenase (IDH) mutation status serves as a crucial prognostic indicator for glioma, typically assessed via immunohistochemical analysis post-surgery. Given the invasiveness of this approach, perhaps we can utilise convenient and noninvasive magnetic resonance imaging (MRI) methods to predict IDH mutation status. However, the current landscape lacks a standardised MRI technique for accurately predicting IDH mutations. In this study, we explore the potential of MRI diffusion kurtosis imaging (DKI) in forecasting the IDH mutation status of WHO grade II brain gliomas.

Material and methods

Twenty-five patients with WHO grade II gliomas were retrospectively included. Patients underwent routine MRI and DKI scanning before surgery, measuring tumoural solid portion, peritumoral oedema, and normal-appearing white matter (NAWM) DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (Ka), and axial radial kurtosis (Kr). The DKI parameter corrections were made (tumour or oedema parameters values divided by the NAWM value) to obtain the rFA (ratio of FA), rMD (ratio of MD), rMK (ratio of MK), rKA (ratio of KA), and rKr (ratio of Kr) values. Postoperative specimens were made of wax blocks and analysed by Sanger gene sequencing. DKI parameters between the 2 groups were compared by independent sample t-tests. The ROC curve was used to analyse the diagnostic value of each parameter.

Results

Twenty-five patients were diagnosed with IDH-mutant (16 cases) and IDH-wild type (9 cases). The rFA and rMK values in the parenchymal region of IDH wild-type tumour were higher than those of IDH mutant, while the rMD values were lower than those of IDH mutant, and the difference between them was statistically significant (p < 0.05). The values of DKI parameters of peritumoral oedema in the 2 groups were not statistically significant.

Conclusions

DKI can provide microstructural changes of diseased tissues and provide more imaging information for preoperative non-invasive judgment of IDH mutation status of WHO grade II gliomas. The values of rMK, rFA, and rMD are helpful in the assessment IDH mutation status, benefiting accurate diagnoses and treatment decisions.

Elucidating Microstructural Alterations in Neurodevelopmental Disorders: Application of Advanced Diffusion-Weighted Imaging in Children With Rasopathies

Neurodevelopmental disorders (NDDs) can severely impact functioning yet effective treatments are limited. Greater insight into the neurobiology underlying NDDs is critical to the development of successful treatments. Using a genetics-first approach, we investigated the potential of advanced diffusion-weighted imaging (DWI) techniques to characterize the neural microstructure unique to neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). In this prospective study, children with NF1, NS, and typical developing (TD) were scanned using a multi-shell DWI sequence optimized for neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI). Region-of-interest and tract-based analysis were conducted on subcortical regions and white matter tracts. Analysis of covariance, principal components, and linear discriminant analysis compared between three groups. 88 participants (Mage = 9.36, SDage = 2.61; 44 male) were included: 31 NS, 25 NF1, and 32 TD. Subcortical regions differed between NF1 and NS, particularly in the thalamus where the neurite density index (NDI; estimated difference 0.044 [95% CI: -0.034, 0.053], d = 2.36), orientation dispersion index (ODI; estimate 0.018 [95% CI: 0.010, 0.026], d = 1.39), and mean kurtosis (MK; estimate 0.049 [95% CI: 0.025, 0.072], d = 1.39) were lower in NF1 compared with NS (all p < 0.0001). Reduced NDI was found in NF1 and NS compared with TD in all 39 white matter tracts investigated (p < 0.0001). Reduced MK was found in a majority of the tracts in NF1 and NS relative to TD, while fewer differences in ODI were observed. The middle cerebellar peduncle showed lower NDI (estimate 0.038 [95% CI: 0.021, 0.056], p < 0.0001) and MK (estimate 0.057 [95% CI: 0.026, 0.089], p < 0.0001) in NF1 compared to NS. Multivariate analyses distinguished between groups using NDI, ODI, and MK measures. Principal components analysis confirmed that the clinical groups differ most from TD in white matter tract-based NDI and MK, whereas ODI values appear similar across the groups. The subcortical regions showed several differences between NF1 and NS, to the extent that a linear discriminant analysis could classify participants with NF1 with an accuracy rate of 97%. Differences in neural microstructure were detected between NF1 and NS, particularly in subcortical regions and the middle cerebellar peduncle, in line with pre-clinical evidence. Advanced DWI techniques detected subtle alterations not found in prior work using conventional diffusion tensor imaging.

Normative Neuroimaging Library: Designing a Comprehensive and Demographically Diverse Dataset of Healthy Controls to Support Traumatic Brain Injury Diagnostic and Therapeutic Development

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response

By measuring the molecular diffusion of water molecules in brain tissue, diffusion MRI (dMRI) provides unique insight into the microstructure and structural connections of the brain in living subjects. Since its inception, the application of dMRI in clinical research has expanded our understanding of the possible biological bases of psychiatric disorders and successful responses to different therapeutic interventions. Here, we review the past decade of diffusion imaging-based investigations with a specific focus on studies examining the mechanisms and predictors of therapeutic response in people with mood disorders. We present a brief overview of the general application of dMRI and key methodological developments in the field that afford increasingly detailed information concerning the macro- and micro-structural properties and connectivity patterns of white matter (WM) pathways and their perturbation over time in patients followed prospectively while undergoing treatment. This is followed by a more in-depth summary of particular studies using dMRI approaches to examine mechanisms and predictors of clinical outcomes in patients with unipolar or bipolar depression receiving pharmacological, neurostimulation, or behavioral treatments. Limitations associated with dMRI research in general and with treatment studies in mood disorders specifically are discussed, as are directions for future research. Despite limitations and the associated discrepancies in findings across individual studies, evolving research strongly indicates that the field is on the precipice of identifying and validating dMRI biomarkers that could lead to more successful personalized treatment approaches and could serve as targets for evaluating the neural effects of novel treatments.

Diffusion and functional MRI reveal microstructural and network connectivity impairment in adult-onset neuronal intranuclear inclusion disease

Objectives

Neuronal intranuclear inclusion disease (NIID) is a rare neurodegenerative disorder lacking reliable neuroimaging biomarkers. This study aimed to evaluate microstructural and functional connectivity alterations using diffusion kurtosis imaging (DKI) and resting-state fMRI (rs-fMRI), and to investigate their diagnostic potential as biomarkers.

Methods

Twenty-three patients with NIID and 40 matched healthy controls (HCs) were recruited. Firstly, gray matter (GM) and white matter (WM) changes were assessed by voxel-based analysis (VBA) and tract-based spatial statistics (TBSS). Then we explored modifications in brain functional networks connectivity by independent component analysis. And the relationship between the altered DKI parameters and neuropsychological evaluation was analyzed. Finally, a receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different gray matter and white matter parameters.

Results

Compared with the HCs, NIID patients showed reduced mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), and kurtosis fractional anisotropy (KFA) values in deep gray matter regions. Significantly decreased MK, RK, AK, KFA and fractional anisotropy (FA), and increased mean diffusivity (MD) values were observed in extensive white matter fiber tracts. Notable alterations in functional connectivity were also detected. Among all DKI parameters, the diagnostic efficiency of AK in GM and FA in WM regions was the highest.

Conclusion

Adult-onset NIID patients exhibited altered microstructure and functional network connectivity. Our findings suggest that DKI parameters may serve as potential imaging biomarkers for diagnosing adult-onset NIID.

Robust, fast and accurate mapping of diffusional mean kurtosis

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Abnormally glymphatic system functional in patients with migraine: a diffusion kurtosis imaging study

BACKGROUND

The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) method has been used to evaluate glymphatic system function in patients with migraine. However, since the diffusion tensor model cannot accurately describe the diffusion coefficient of the nerve fibre crossing region, we proposed a diffusion kurtosis imaging ALPS (DKI-ALPS) method to evaluate glymphatic system function in patients with migraine.

METHODS

The study included 29 healthy controls and 37 patients with migraine. We used diffusion imaging data from a 3T MRI scanner to calculate DTI-ALPS and DKI-ALPS indices of the two groups. We compared the DTI-ALPS and DKI-ALPS indices between the two groups using a two-sample t-test and performed correlation analyses with clinical variables.

RESULTS

There was no significant difference in DTI-ALPS index between the two groups. Patients with migraine showed a significantly increased right DKI-ALPS index compared to healthy controls (1.6858 vs. 1.5729; p = 0.0301). There was no significant correlation between ALPS indices and clinical variables.

CONCLUSIONS

DKI-ALPS is a potential method to assess glymphatic system function and patients with migraine do not have impaired glymphatic system function.

Associations between diffusion kurtosis imaging metrics and neurodevelopmental outcomes in neonates with low-grade germinal matrix and intraventricular hemorrhage

Diffusion Kurtosis Imaging (DKI)-derived metrics are recognized as indicators of maturation in neonates with low-grade germinal matrix and intraventricular hemorrhage (GMH-IVH). However, it is not yet known if these factors are associated with neurodevelopmental outcomes. The objective of this study was to acquire DKI-derived metrics in neonates with low-grade GMH-IVH, and to demonstrate their association with later neurodevelopmental outcomes. In this prospective study, neonates with low-grade GMH-IVH and control neonates were recruited, and DKI were performed between January 2020 and March 2021. These neonates underwent the Bayley Scales of Infant Development test at 18 months of age. Mean kurtosis (MK), radial kurtosis (RK) and gray matter values were measured. Spearman correlation analyses were conducted for the measured values and neurodevelopmental outcome scores. Forty controls (18 males, average gestational age (GA) 30 weeks ± 1.3, corrected GA at MRI scan 38 weeks ± 1) and thirty neonates with low-grade GMH-IVH (13 males, average GA 30 weeks ± 1.5, corrected GA at MRI scan 38 weeks ± 1). Neonates with low-grade GMH-IVH exhibited lower MK and RK values in the PLIC and the thalamus (P < 0.05). The MK value in the thalamus was associated with Mental Development Index (MDI) (r = 0.810, 95% CI 0.695-0.13; P < 0.001) and Psychomotor Development Index (PDI) (r = 0.852, 95% CI 0.722-0.912; P < 0.001) scores. RK value in the caudate nucleus significantly and positively correlated with MDI (r = 0.496, 95% CI 0.657-0.933; P < 0.001) and PDI (r = 0.545, 95% CI 0.712-0.942; P < 0.001) scores. The area under the curve (AUC) were used to assess diagnostic performance of MK and RK in thalamus (AUC = 0.866, 0.787) and caudate nucleus (AUC = 0.833, 0.671) for predicting neurodevelopmental outcomes. As quantitative neuroimaging markers, MK in thalamus and RK in caudate nucleus may help predict neurodevelopmental outcomes in neonates with low-grade GMH-IVH.

Anti-motion Ultrafast T2 Mapping Technique for Quantitative Detection of the Normal-Appearing Corticospinal Tract Changes in Subacute-Chronic Stroke Patients with Distal Lesions

RATIONALE AND OBJECTIVES

Stroke patients commonly face challenges during magnetic resonance imaging (MRI) examinations due to involuntary movements. This study aims to overcome these challenges by utilizing multiple overlapping-echo detachment (MOLED) quantitative technology. Through this technology, we also seek to detect microstructural changes of the normal-appearing corticospinal tract (NA-CST) in subacute-chronic stroke patients.

MATERIALS AND METHODS

79 patients underwent 3.0 T MRI scans, including routine scans and MOLED technique. A deep learning network was utilized for image reconstruction, and the accuracy, reliability, and resistance to motion of the MOLED technique were validated on phantoms and volunteers. Subsequently, we assessed motor dysfunction severity, ischemic lesion volume, T2 values of the bilateral NA-CST, and the T2 ratio (rT2) between the ipsilesional and contralesional NA-CST in patients.

RESULTS

The MOLED technique showed high accuracy (P < 0.001) and excellent repeatability, with a mean coefficient of variation (CoV) of 1.11%. It provided reliable quantitative results even under head movement, with a mean difference (Meandiff)= 0.28% and a standard deviation difference (SDdiff)= 1.34%. Additionally, the T2 value of the ipsilesional NA-CST was significantly higher than contralesional side (P < 0.001), and a positive correlation was observed between rT2 and the severity of motor dysfunction (rs =0.575, P < 0.001). Furthermore, rT2 successfully predicted post-stroke motor impairment, with an area under the curve (AUC) was 0.883.

CONCLUSION

The MOLED technique offers significant advantages for quantitatively imaging stroke patients with involuntary movements. Additionally, T2 mapping from MOLED can detect microstructural changes in the NA-CST, potentially aiding in monitoring stroke-induced motor impairment.

Longitudinal Biochemical and Behavioral Alterations in a Gyrencephalic Model of Blast-Related Mild Traumatic Brain Injury

Blast-related traumatic brain injury (bTBI) is a major cause of neurological disorders in the U.S. military that can adversely impact some civilian populations as well and can lead to lifelong deficits and diminished quality of life. Among these types of injuries, the long-term sequelae are poorly understood because of variability in intensity and number of the blast exposure, as well as the range of subsequent symptoms that can overlap with those resulting from other traumatic events (e.g., post-traumatic stress disorder). Despite the valuable insights that rodent models have provided, there is a growing interest in using injury models using species with neuroanatomical features that more closely resemble the human brain. With this purpose, we established a gyrencephalic model of blast injury in ferrets, which underwent blast exposure applying conditions that closely mimic those associated with primary blast injuries to warfighters. In this study, we evaluated brain biochemical, microstructural, and behavioral profiles after blast exposure using in vivo longitudinal magnetic resonance imaging, histology, and behavioral assessments. In ferrets subjected to blast, the following alterations were found: 1) heightened impulsivity in decision making associated with pre-frontal cortex/amygdalar axis dysfunction; 2) transiently increased glutamate levels that are consistent with earlier findings during subacute stages post-TBI and may be involved in concomitant behavioral deficits; 3) abnormally high brain N-acetylaspartate levels that potentially reveal disrupted lipid synthesis and/or energy metabolism; and 4) dysfunction of pre-frontal cortex/auditory cortex signaling cascades that may reflect similar perturbations underlying secondary psychiatric disorders observed in warfighters after blast exposure.

Diffusion kurtosis MRI tracks gray matter myelin content in the primate cerebral cortex

Diffusion magnetic resonance imaging (dMRI) has been widely employed to model the trajectory of myelinated fiber bundles in white matter. Increasingly, dMRI is also used to assess local tissue properties throughout the brain. In the cerebral cortex, myelin content is a critical indicator of the maturation, regional variation, and disease related degeneration of gray matter tissue. Gray matter myelination can be measured and mapped using several non-diffusion MRI strategies; however, first order diffusion statistics such as fractional anisotropy (FA) show only weak spatial correlation with cortical myelin content. Here we show that a simple higher order diffusion parameter, the mean diffusion kurtosis (MK), is strongly correlated with the laminar and regional variation of myelin in the primate cerebral cortex. We carried out ultra-high resolution, multi-shelled dMRI in ex vivo marmoset monkey brains and compared dMRI parameters from a number of higher order models (diffusion kurtosis, NODDI and MAP MRI) to the distribution of myelin obtained using histological staining, and via Magnetization Transfer Ratio MRI (MTR), a non-diffusion MRI method. In contrast to FA, MK closely matched the myelin content assessed by histology and by MTR in the same sample. The parameter maps from MAP-MRI and NODDI also showed good correspondence with cortical myelin content. The results demonstrate that dMRI can be used to assess the variation of local myelin content in the primate cortical cortex, which may be of great value for assessing tissue integrity and tracking disease in living human patients.

Diffusion Imaging of the Spinal Cord: Clinical Applications

Spinal cord pathologic condition often presents as a neurologic emergency where timely and accurate diagnosis is critical to expedite appropriate treatment and minimize severe morbidity and even mortality. MR imaging is the gold standard imaging technique for diagnosing patients with suspected spinal cord pathologic condition. This review will focus on the basic principles of diffusion imaging and how spinal anatomy presents technical challenges to its application. Both the promises and shortcomings of spinal diffusion imaging will then be explored in the context of several clinical spinal cord pathologies for which diffusion has been evaluated.

Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer

OBJECTIVE

To investigate the diagnostic value of diffusion kurtosis magnetic resonance imaging (DKI) and conventional diffusion-weighted imaging (DWI) for evaluating the response to first-line chemotherapy in unresectable pancreatic cancer.

MATERIALS AND METHODS

We retrospectively analyzed 21 patients with clinically and pathologically confirmed unresected pancreatic cancer who received palliative chemotherapy. Three-tesla MRI examinations containing DWI sequences with b values of 0, 100, 700, 1400, and 2100 s/mm2 were performed before and after chemotherapy. Parameters included the apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), and mean diffusional kurtosis (MK). The performances of the DWI and DKI parameters in distinguishing the response to chemotherapy were evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Overall survival (OS) was calculated from the date of first treatment to the date of death or the latest follow-up date.

RESULTS

The ADCchange and MDchange were significantly higher in the responding group (PR group) than in the nonresponding group (non-PR group) (ADCchange: 0.21 ± 0.05 vs. 0.11 ± 0.09, P = 0.02; MDchange: 0.37 ± 0.24 vs. 0.10 ± 0.12, P = 0.002). No statistical significance was shown when comparing ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost between the PR and non-PR groups. The ROC curve analysis indicated that MDchange (AUC = 0.898, cutoff value = 0.7143) performed better than ADCchange (AUC = 0.806, cutoff value = 0.1369) in predicting the response to chemotherapy.

CONCLUSION

The ADCchange and MDchange demonstrated strong potential for evaluating the response to chemotherapy in unresectable pancreatic cancer. The MDchange showed higher specificity in the classification of PR and non-PR than the ADCchange. Other parameters, including ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost, are not suitable for response evaluation. The combined model SUMchange demonstrated superior performance compared to the individual DWI and DKI models. Further experiments are needed to evaluate the potential of DWI and DKI parameters in predicting the prognosis of patients with unresectable pancreatic cancer.

White Matter Microstructure Changes Revealed by Diffusion Kurtosis and Diffusion Tensor Imaging in Mutant Huntingtin Gene Carriers

Background

Diffusion magnetic resonance imaging (dMRI) has revealed microstructural changes in white matter (WM) in Huntington's disease (HD).

Objective

To compare the validities of different dMRI, i.e., diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) in HD.

Methods

22 mutant huntingtin (mHTT) carriers and 14 controls were enrolled. Clinical assessments and dMRI were conducted. Based on CAG-Age Product (CAP) score, mHTT carriers were categorized into high CAP (hCAP) and medium and low CAP (m& lCAP) groups. Spearman analyses were used to explore correlations between imaging parameters in brain regions and clinical assessments. Receiver operating characteristic (ROC) was used to distinguish mHTT carriers from control, and define the HD patients at advanced stage.

Results

Compared to controls, mHTT carriers exhibited WM changes in DKI and DTI. There were 22 more regions showing significant differences in HD detected by MK than FA. Only MK in five brain regions showed significantly difference between any two group, and negatively correlated with the disease burden (r = -0.80 to -0.71). ROC analysis revealed that MK was more sensitive and FA was more specific, while Youden index showed that the integration of FA and MK gave rise to higher authenticities, in distinguishing m& lCAP from controls (Youden Index = 0.786), and discerning different phase of HD (Youden Index = 0.804).

Conclusions

Microstructural changes in WM occur at early stage of HD and deteriorate over the disease progression. Integrating DKI and DTI would provide the best accuracies for differentiating early HD from control and identifying advanced HD.

Multimodal MRI for Estimating Her-2 Gene Expression in Endometrial Cancer

PURPOSE

To assess the value of multimodal MRI, including amide proton transfer-weighted imaging (APT), diffusion kurtosis imaging (DKI), and T2 mapping sequences for estimating human epidermal growth factor receptor-2 (Her-2) expression in patients with endometrial cancer (EC).

METHODS

A total of 54 patients with EC who underwent multimodal pelvic MRI followed by biopsy were retrospectively selected and divided into the Her-2 positive (n = 24) and Her-2 negative (n = 30) groups. Her-2 expression was confirmed by immunohistochemistry (IHC). Two observers measured APT, mean kurtosis (MK), mean diffusivity (MD), and T2 values for EC lesions.

RESULTS

The Her-2 (+) group showed higher APT values and lower MD and T2 values than the Her-2 (-) group (all p < 0.05); there was no significant difference in MK values (p > 0.05). The area under the receiver operating characteristic curve (AUC) of APT, MD, T2, APT + T2, APT + MD, T2 + MD, and APT + MD + T2 models to identify the two groups of cases were 0.824, 0.695, 0.721, 0.824, 0.858, 0.782, and 0.860, respectively, and the diagnostic efficacy after combined APT + MD + T2 value was significantly higher than those of MD and T2 values individually (p = 0.018, 0.028); the diagnostic efficacy of the combination of APT + T2 values was significantly higher than that of T2 values separately (p = 0.028). Weak negative correlations were observed between APT and T2 values (r = -0.365, p = 0.007), moderate negative correlations between APT and MD values (r = -0.560, p < 0.001), and weak positive correlations between MD and T2 values (r = 0.336, p = 0.013). The APT values were independent predictors for assessing Her-2 expression in EC patients.

CONCLUSION

The APT, DKI, and T2 mapping sequences can be used to preoperatively assess the Her-2 expression in EC, which can contribute to more precise treatment for clinical preoperative.

Modern preoperative imaging and functional mapping in patients with intracranial glioma

Magnetic resonance imaging (MRI) in therapy-naïve intracranial glioma is paramount for neuro-oncological diagnostics, and it provides images that are helpful for surgery planning and intraoperative guidance during tumor resection, including assessment of the involvement of functionally eloquent brain structures. This study reviews emerging MRI techniques to depict structural information, diffusion characteristics, perfusion alterations, and metabolism changes for advanced neuro-oncological imaging. In addition, it reflects current methods to map brain function close to a tumor, including functional MRI and navigated transcranial magnetic stimulation with derived function-based tractography of subcortical white matter pathways. We conclude that modern preoperative MRI in neuro-oncology offers a multitude of possibilities tailored to clinical needs, and advancements in scanner technology (e. g., parallel imaging for acceleration of acquisitions) make multi-sequence protocols increasingly feasible. Specifically, advanced MRI using a multi-sequence protocol enables noninvasive, image-based tumor grading and phenotyping in patients with glioma. Furthermore, the add-on use of preoperatively acquired MRI data in combination with functional mapping and tractography facilitates risk stratification and helps to avoid perioperative functional decline by providing individual information about the spatial location of functionally eloquent tissue in relation to the tumor mass. KEY POINTS::   · Advanced preoperative MRI allows for image-based tumor grading and phenotyping in glioma.. · Multi-sequence MRI protocols nowadays make it possible to assess various tumor characteristics (incl. perfusion, diffusion, and metabolism).. · Presurgical MRI in glioma is increasingly combined with functional mapping to identify and enclose individual functional areas.. · Advancements in scanner technology (e. g., parallel imaging) facilitate increasing application of dedicated multi-sequence imaging protocols.. CITATION FORMAT: · Sollmann N, Zhang H, Kloth C et al. Modern preoperative imaging and functional mapping in patients with intracranial glioma. Fortschr Röntgenstr 2023; 195: 989 - 1000.

Role of Imaging Modalities and N-Acetylcysteine Treatment in Sepsis-Associated Encephalopathy

Sepsis-associated encephalopathy is a severe systemic infection complication. Although early stages involve pathophysiological changes, detection using conventional imaging is challenging. Glutamate chemical exchange saturation transfer and diffusion kurtosis imaging can noninvasively investigate cellular and molecular events in early disease stages using magnetic resonance imaging (MRI). N-Acetylcysteine, an antioxidant and precursor of glutathione, regulates neurotransmitter glutamate metabolism and participates in neuroinflammation. We investigated the protective role of n-acetylcysteine in sepsis-associated encephalopathy using a rat model and monitored changes in brain using magnetic resonance (MR) molecular imaging. Bacterial lipopolysaccharide was injected intraperitoneally to induce a sepsis-associated encephalopathy model. Behavioral performance was assessed using the open-field test. Tumor necrosis factor α and glutathione levels were detected biochemically. Imaging was performed using a 7.0-T MRI scanner. Protein expression, cellular damage, and changes in blood-brain barrier permeability were assessed using western blotting, pathological staining, and Evans blue staining, respectively. Lipopolysaccharide-induced rats showed reduced anxiety and depression after treatment with n-acetylcysteine. MR molecular imaging can identify pathological processes at different disease stages. Furthermore, rats treated with n-acetylcysteine showed increased glutathione levels and decreased tumor necrosis factor α, suggesting enhanced antioxidant capacity and inhibition of inflammatory processes, respectively. Western blot analysis showed reduced expression of nuclear factor kappa B (p50) protein after treatment, suggesting that n-acetylcysteine inhibits inflammation via this signaling pathway. Finally, n-acetylcysteine-treated rats showed reduced cellular damage by pathology and reduced extravasation of their blood-brain barrier by Evans Blue staining. Thus, n-acetylcysteine might be a therapeutic option for sepsis-associated encephalopathy and other neuroinflammatory diseases. Furthermore, noninvasive "dynamic visual monitoring" of physiological and pathological changes related to sepsis-associated encephalopathy was achieved using MR molecular imaging for the first time, providing a more sensitive imaging basis for early diagnosis, identification, and prognosis.

Intravoxel incoherent motion and diffusion kurtosis imaging at 3T MRI: Application to ischemic stroke

BACKGROUND AND PURPOSE

The DKI-IVIM model that incorporates DKI (diffusional kurtosis imaging) into the IVIM (Intravoxel Incoherent Motion) concept was investigated to assess its utility for both enhanced diffusion characterization and perfusion measurements in ischemic stroke at 3 T.

METHODS

Fifteen stroke patients (71 ± 11 years old) were enrolled and DKI-IVIM analysis was performed using 9 b-values from 0 to 1500 s/mm² chosen with the Cramer-Rao-Lower-Bound optimization approach. Pseudo-diffusion coefficient D*, perfusion fraction f, blood flow-related parameter fD*, the diffusion coefficient D and an additional parameter, the kurtosis, K were determined in the ischemic lesion and controlateral normal tissue based on a region of interest approach. The apparent diffusion coefficient (ADC) and arterial spin labelling (ASL) cerebral blood flow (CBF) parameters were also assessed and parametric maps were obtained for all parameters.

RESULTS

Significant differences were observed for all diffusion parameters with a significant decrease for D (p < 0.0001), ADC (p < 0.0001), and a significant increase for K (p < 0.0001) in the ischemic lesions of all patients. f decreased significantly in these regions (p = 0.0002). The fD* increase was not significant (p = 0.56). The same significant differences were found with a motion correction except for fD* (p = 0.47). CBF significantly decreased in the lesions. ADC was significantly positively correlated with D (p < 0.0001) and negatively with K (p = 0.0002); K was also negatively significantly correlated with D (p = 0.01).

CONCLUSIONS

DKI-IVIM model enables for simultaneous cerebral perfusion and enhanced diffusion characterization in an acceptable clinically acquisition time for the ischemic stroke diagnosis with the additional kurtosis factor estimation, that may better reflect the microstructure heterogeneity.

Negative emotion differentiation and white matter microstructure

BACKGROUND

Deficits in the differentiation of negative emotions - the ability to specifically identify one's negative emotions - are associated with poorer mental health outcomes. However, the processes that lead to individual differences in negative emotion differentiation are not well understood, hampering our understanding of why this process is related to poor mental health outcomes. Given that disruptions in some affective processes are associated with white matter microstructure, identifying the circuitry associated with different affective processes can inform our understanding of how disturbances in these networks may lead to psychopathology. Thus, examination of how white matter microstructure relates to individual differences in negative emotion differentiation (NED) may provide insights into (i) its component processes and (ii) its relationship to brain structure.

METHOD

The relationship between white matter microstructure and NED was examined.

RESULTS

NED was related to white matter microstructure in right anterior thalamic radiation and inferior fronto-occipital fasciculus and left peri-genual cingulum.

LIMITATIONS

Although participants self-reported psychiatric diagnoses and previous psychological treatment, psychopathology was not directly targeted, and thus, the extent to which microstructure related to NED could be examined in relation to maladaptive outcomes is limited.

CONCLUSIONS

Results indicate that NED is related to white matter microstructure and suggest that pathways subserving processes that facilitate memory, semantics, and affective experience are important for NED. Our findings provide insights into the mechanisms by which individual differences in NED arise, suggesting intervention targets that may disrupt the relationship between poor differentiation and psychopathology.

Synthesis and bioactivity evaluation of a myelin-specific contrast agent for magnetic resonance imaging of myelination in central nervous system

Demyelination exists in many neurological diseases of nervous system, such as stroke. Currently, magnetic resonance imaging (MRI) has been the main tool for diagnosing and monitoring the myelin related diseases. However, the conventional MRI unable to distinguish demyelinating lesions from other inflammatory lesions. To address this problem, we have designed and prepared a myelin specific magnetic resonance contrast agent, Gd-DTDAS, which was based myelin specific moiety MeDAS^g and Gd-DTPA^h. In this work, we verified the specificity and sensitivity of Gd-DTDAS to myelin. Moreover, we investigated the specific binding ability of Gd-DTDAS to myelin sheath in the MCAO miceⁱ models. The in vivo imaging results showed that Gd-DTDAS can bind to the undamaged myelin sheath in the BBB disruption areas, and in turn reduce the relaxation time. The fluorescence images also showed significant fluorescence in the brain right infarct area of the MCAO model mice with administration of Gd-DTDAS. The above results confirmed that Gd-DTDAS could be preferentially distributed in areas with high myelination and can detect focally induced demyelination.

The Use of Diffusion Kurtosis Imaging for the Differential Diagnosis of Alzheimer's Disease Spectrum

Structural and diffusion kurtosis imaging (DKI) can be used to assess hippocampal macrostructural and microstructural alterations respectively, in Alzheimer's disease (AD) spectrum, spanning from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) and AD. In this study, we explored the diagnostic performance of structural imaging and DKI of the hippocampus in the AD spectrum. Eleven SCD, thirty-seven MCI, sixteen AD, and nineteen age- and sex-matched normal controls (NCs) were included. Bilateral hippocampal volume, mean diffusivity (MD), and mean kurtosis (MK) were obtained. We detected that in AD vs. NCs, the right hippocampal volume showed the most prominent AUC value (AUC = 0.977); in MCI vs. NCs, the right hippocampal MD was the most sensitive discriminator (AUC = 0.819); in SCD vs. NCs, the left hippocampal MK was the most sensitive biomarker (AUC = 0.775). These findings suggest that, in the predementia stage (SCD and MCI), hippocampal microstructural changes are predominant, and the best discriminators are microstructural measurements (left hippocampal MK for SCD and right hippocampal MD for MCI); while in the dementia stage (AD), hippocampal macrostructural alterations are superior, and the best indicator is the macrostructural index (right hippocampal volume).

Image sharpening algorithms improve clarity of surgical field during 3D heads-up surgery

BACKGROUND

Image-sharpening algorithms with color adjustments enable real-time processing of the surgical field with a delay of 4 msec for heads-up surgery using digital three-dimensional displays. The aim of this study was to investigate the usefulness of the algorithms with the Artevo 800^® digital microscope.

METHODS

Seven vitreoretinal surgeons evaluated the effects of image-sharpening processing on the clarity of the surgical field with the Artevo 800^® system that is used for cataract and vitreous surgeries. The scorings were made on a 10-point scale for anterior capsulotomy, phacoemulsification, cortex aspiration, core vitrectomy, and peeling of an epiretinal membrane or an internal limiting membrane. In addition, the images during the internal limiting membrane peeling were processed with or without color adjustments. We also evaluated the skewness (asymmetry in the distribution of the pixels) and kurtosis (sharpness in the distribution of the pixel) of the images to evaluate the contrast with each intensity of image-sharpening.

RESULTS

Our results showed that the mean visibility score increased significantly from 4.9 ± 0.5 at 0% (original image) to 6.6 ± 0.5 at 25% intensity of the image-sharpening algorithm (P < 0.01). The visibility scores of the internal limiting membrane increased significantly from 0% (6.8 ± 0.3, no color adjustments) to 50% after the color adjustments (7.4 ± 0.4, P = 0.012). The mean skewness decreased significantly from 0.83 ± 2.02 at 0% (original source) to 0.55 ± 1.36 at 25% intensity of the image-sharpening algorithm (P = 0.01). The mean kurtosis decreased significantly from 0.93 ± 2.14 at 0% (original image) to 0.60 ± 1.44 at 25% intensity of the image-sharpening algorithm (P = 0.02).

CONCLUSIONS

We conclude that the image-sharpening algorithms can improve the clarity of the surgical field during 3D heads-up surgery by decreasing the skewness and kurtosis.

TRIAL REGISTRATION

This was a prospective clinical study performed at a single academic institution, and the procedures used were approved by the Institutional Review Committee of the Kyorin University School of Medicine (reference number, 1904). The procedures also conformed to the tenets of the Declaration of Helsinki.

Diffusion-Weighted Imaging in Mild Traumatic Brain Injury: A Systematic Review of the Literature

There is evidence that diffusion-weighted imaging (DWI) is able to detect tissue alterations following mild traumatic brain injury (mTBI) that may not be observed on conventional neuroimaging; however, findings are often inconsistent between studies. This systematic review assesses patterns of differences in DWI metrics between those with and without a history of mTBI. A PubMed literature search was performed using relevant indexing terms for articles published prior to May 14, 2020. Findings were limited to human studies using DWI in mTBI. Articles were excluded if they were not full-length, did not contain original data, if they were case studies, pertained to military populations, had inadequate injury severity classification, or did not report post-injury interval. Findings were reported independently for four subgroups: acute/subacute pediatric mTBI, acute/subacute adult mTBI, chronic adult mTBI, and sport-related concussion, and all DWI acquisition and analysis methods used were included. Patterns of findings between studies were reported, along with strengths and weaknesses of the current state of the literature. Although heterogeneity of sample characteristics and study methods limited the consistency of findings, alterations in DWI metrics were most commonly reported in the corpus callosum, corona radiata, internal capsule, and long association pathways. Many acute/subacute pediatric studies reported higher FA and lower ADC or MD in various regions. In contrast, acute/subacute adult studies most commonly indicate lower FA within the context of higher MD and RD. In the chronic phase of recovery, FA may remain low, possibly indicating overall demyelination or Wallerian degeneration over time. Longitudinal studies, though limited, generally indicate at least a partial normalization of DWI metrics over time, which is often associated with functional improvement. We conclude that DWI is able to detect structural mTBI-related abnormalities that may persist over time, although future DWI research will benefit from larger samples, improved data analysis methods, standardized reporting, and increasing transparency.

Seeking the Amygdala: Novel Use of Diffusion Tensor Imaging to Delineate the Basolateral Amygdala

The amygdaloid complex, including the basolateral nucleus (BLA), contributes crucially to emotional and cognitive brain functions, and is a major target of research in both humans and rodents. However, delineating structural amygdala plasticity in both normal and disease-related contexts using neuroimaging has been hampered by the difficulty of unequivocally identifying the boundaries of the BLA. This challenge is a result of the poor contrast between BLA and the surrounding gray matter, including other amygdala nuclei. Here, we describe a novel diffusion tensor imaging (DTI) approach to enhance contrast, enabling the optimal identification of BLA in the rodent brain from magnetic resonance (MR) images. We employed this methodology together with a slice-shifting approach to accurately measure BLA volumes. We then validated the results by direct comparison to both histological and cellular-identity (parvalbumin)-based conventional techniques for defining BLA in the same brains used for MRI. We also confirmed BLA connectivity targets using DTI-based tractography. The novel approach enables the accurate and reliable delineation of BLA. Because this nucleus is involved in and changed by developmental, degenerative and adaptive processes, the instruments provided here should be highly useful to a broad range of neuroimaging studies. Finally, the principles used here are readily applicable to numerous brain regions and across species.

Role of imaging modalities and N-acetylcysteine treatment in sepsis-associated encephalopathy.. [DOI: 10.21203/rs.3.rs-2459747/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background Sepsis-associated encephalopathy is a severe complication due to systemic infection. Although early stages involve pathophysiological changes, detection using conventional imaging is challenging. Glutamate chemical exchange saturation transfer and diffusion kurtosis imaging can noninvasively investigate cellular and molecular events in the early stage of the disease by MRI. N-acetylcysteine, an antioxidant and precursor of glutathione, regulates the metabolism of the neurotransmitter glutamate and participates in neuroinflammation. We aimed to investigate the protective role of n-acetylcysteine in sepsis-associated encephalopathy using a rat model and monitor changes in the brain using magnetic resonance molecular imaging. Methods Bacterial lipopolysaccharide was injected intraperitoneally into the rats to induce a sepsis-associated encephalopathy model. The behavioural performance was assessed using the open field test. Tumour necrosis factor alpha and glutathione levels were detected biochemically. Imaging was performed using a 7.0-T MRI scanner. Protein expressions and cellular damage were assessed by western blotting and pathological staining, respectively. We also evaluated changes in the blood-brain barrier permeability by the Evans blue staining. Results The lipopolysaccharide-induced rats showed reduced anxiety and depression after treatment with n-acetylcysteine. Magnetic resonance molecular imaging can identify pathological processes at different stages of the disease. Furthermore, rats treated with n-acetylcysteine showed increased glutathione levels and decreased tumour necrosis factor alpha, suggesting enhanced antioxidant capacity and inhibition of inflammatory processes, respectively. Western blot analysis showed a reduced expression of nuclear factor kappa B (p50) protein after treatment, suggesting that n-acetylcysteine inhibits inflammation via this signalling pathway. Finally, n-acetylcysteine treated rats also showed reduced cellular damage by pathology and reduced extravasation of their blood-brain barrier by Evan Blue staining. Conclusion This study showed that n-acetylcysteine might be a therapeutic option for sepsis-associated encephalopathy and other neuroinflammatory diseases. Furthermore, non-invasive ‘dynamic visual monitoring’ of the physiological and pathological changes related to sepsis-associated encephalopathy was achieved for the first time using magnetic resonance molecular imaging, which provides a more sensitive imaging basis for early clinical diagnosis, identification, and prognosis.

Application of diffusion kurtosis imaging in neonatal brain development

Background

Deviations from the regular pattern of growth and development could lead to early childhood diseases, suggesting the importance of evaluating early brain development. Through this study, we aimed to explore the changing patterns of white matter and gray matter during neonatal brain development using diffusion kurtosis imaging (DKI).

Materials and methods

In total, 42 full-term neonates (within 28 days of birth) underwent conventional brain magnetic resonance imaging (MRI) and DKI. The DKI metrics (including kurtosis parameters and diffusion parameters) of white matter and deep gray matter were measured. DKI metrics from the different regions of interest (ROIs) were evaluated using the Kruskal-Wallis test and Bonferroni method. Spearman rank correlation analysis of the DKI metrics was conducted, and the age at the time of brain MRI acquisition was calculated. The subjects were divided into three groups according to their age at the time of brain MRI acquisition: the first group, neonates aged ≤7 days; the second group, neonates aged 8-14 days; and the third group, neonates aged 15-28 days. The rate of change in DKI metrics relative to the first group was computed.

Results

The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), and fractional anisotropy (FA) values showed positive correlations, whereas mean diffusion (MD), axial diffusion (Da), and radial diffusion (Dr) values showed negative correlations with the age at the time of brain MRI acquisition. The absolute correlation coefficients between MK values of almost all ROIs (except genu of the corpus callosum and frontal white matter) and the age at the time of brain MRI acquisition were greater than other metrics. The kurtosis parameters and FA values of central white matter were significantly higher than that of peripheral white matter, whereas the MD and Dr values were significantly lower than that of peripheral white matter. The MK value of the posterior limb of the internal capsule was the highest among the white matter areas. The FA value of the splenium of the corpus callosum was significantly higher than that of the other white matter areas. The kurtosis parameters and FA values of globus pallidus and thalamus were significantly higher than those of the caudate nucleus and putamen, whereas the Da and Dr values of globus pallidus and thalamus were significantly lower than those of the caudate nucleus and putamen. The relative change rates of kurtosis parameters and FA values of all ROIs were greater than those of MD, Da, and Dr values. The amplitude of MK values of almost all ROIs (except for the genu of the corpus callosum and central white matter of the centrum semiovale level) was greater than that of other metrics. The relative change rates of the Kr values of most ROIs were greater than those of the Ka value, and the relative change rates of the Dr values of most ROIs were greater than those of the Da value.

Conclusion

DKI parameters showed potential advantages in detecting the changes in brain microstructure during neonatal brain development.

Preliminary research of the classification of the brain acute stroke lesions by the Diffusion Kurtosis Imaging (DKI) and Diffusion Weighted Imaging (DWI) parameters

BACKGROUND

Diffusion-weighted magnetic resonance imaging (DWI) is a mature scanning technique. With high sensitivity in detecting cerebral infractions, it has become an essential part of the clinical evaluation of acute stroke. However, with the update in medical ideals and treatment, clinicians are now focusing on distinguishing between reversible and irreversible brain tissue damage rather than detecting ischaemic lesions alone.

OBJECTIVE

We supposed that Diffusion Kurtosis Imaging (DKI) could classify heterogeneous DWI lesions, deepening the understanding of tissue injury. We systematically studied the different parameters of DKI in acute stroke patients in the literature.

METHODS

We collected 41 patients (26 male, 15 female), including different infarctions with acute cerebral infarction in different brain regions. Of all patients, 20 were single-infarction, while others were multi-infarctions. In this paper, we categorized acute cerebral infarction lesions into two types according to the parametric characteristics of both DKI and DWI. Type I means the DKI and DWI were matched, and Type II means the DKI and DWI were mismatched. Based on each parametric map, the region of interest (ROI) is outlined in each most severe lesion area (as large as possible in the center of the lesion). In the control group, ROIs of the same size are located in the corresponding regions of the contralateral hemisphere.

RESULTS

In both Type I and Type II, all parameters conform to a normal distribution. An independent sample T-test was used to compare the differences between each group. In Type I, we found the FA, MD, Da, Dr, MK and Ka values were statistically different (P< 0.05), while in Type II, only the MK and Ka values were statistically different (P< 0.05).

CONCLUSION

DKI, compared to DWI, can provide more imaging information about intracranial ischemic infarction, which can deepen the understanding of the mechanism of ischemic tissue damage. Our classification of the brain acute stroke lesions by DKI parameters and DWI may help us rediscover the real core of infraction.

Diffusion kurtosis imaging in patients with tissue-negative transient ischemic attack

Approximately 50–60% of patients with a clinical transient ischemic attack (TIA) do not have diffusion-weighted imaging (DWI) evidence of cerebral ischemia. The purpose of this study was to assess the added diagnostic value of diffusion kurtosis imaging (DKI) in the evaluation of patients with TIA who have normal DWI findings. From September 2014 to May 2017, a total of 179 consecutive patients with suspected TIA were eligible for enrollment in our study. The inclusion criteria were a confirmed diagnosis of TIA confirmed by a stroke neurologist, MRI (including DWI and DKI) within 24 h after symptom onset, no stroke history, and no DWI lesion. A follow-up DWI was performed to establish stroke recurrence within a period of 90 days. A total of 98 patients who had no lesions on the baseline DWI were included for data analysis. Of these 98 patients, 31 (31.6%) had positive findings on the initial DKI. In 29 of the 31 (93.5%) patients, the location of the abnormality observed on DKI was consistent with the clinical symptoms. During the 90-day follow-up period, 14 (14.3%) patients developed recurrent stroke. The prevalence of recurrent stroke was higher in the DKI-positive group than in the DKI-negative group (29.0% vs. 7.5%, p = 0.01). A comparison between the patients with and without recurrent stroke showed that an abnormality on the baseline DKI was associated with stroke recurrence. Furthermore, 8 of the 9 stroke patients in the DKI-positive group developed a new ischemic lesion in the artery territory corresponding to the initial DKI abnormality. The new findings suggest the predictive value of DKI on the recurrence of stroke in the patients with TIA who have negative findings on conventional DWI.

Registration and quantification network (RQnet) for IVIM-DKI analysis in MRI

PURPOSE

A deep learning method is proposed for aligning diffusion weighted images (DWIs) and estimating intravoxel incoherent motion-diffusion kurtosis imaging parameters simultaneously.

METHODS

We propose an unsupervised deep learning method that performs 2 tasks: registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. A common registration method in diffusion MRI is based on minimizing dissimilarity between various DWIs, which may result in registration errors due to different contrasts in different DWIs. We designed a novel unsupervised deep learning method for both accurate registration and quantification of various diffusion parameters. In order to generate motion-simulated training data and test data, 17 volunteers were scanned without moving their heads, and 4 volunteers moved their heads during the scan in a 3 Tesla MRI. In order to investigate the applicability of the proposed method to other organs, kidney images were also obtained. We compared the registration accuracy of the proposed method, statistical parametric mapping, and a deep learning method with a normalized cross-correlation loss. In the quantification part of the proposed method, a deep learning method that considered the diffusion gradient direction was used.

RESULTS

Simulations and experimental results showed that the proposed method accurately performed registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. The registration accuracy of the proposed method was high for all b values. Furthermore, quantification performance was analyzed through simulations and in vivo experiments, where the proposed method showed the best performance among the compared methods.

CONCLUSION

The proposed method aligns the DWIs and accurately quantifies the intravoxel incoherent motion-diffusion kurtosis imaging parameters.

The role of the temporal pole in temporal lobe epilepsy: A diffusion kurtosis imaging study

This study aimed to evaluate the use of diffusion kurtosis imaging (DKI) to detect microstructural abnormalities within the temporal pole (TP) and its temporopolar cortex in temporal lobe epilepsy (TLE) patients. DKI quantitative maps were obtained from fourteen lesional TLE and ten non-lesional TLE patients, along with twenty-three healthy controls. Data collected included mean (MK); radial (RK) and axial kurtosis (AK); mean diffusivity (MD) and axonal water fraction (AWF). Automated fiber quantification (AFQ) was used to quantify DKI measurements along the inferior longitudinal (ILF) and uncinate fasciculus (Unc). ILF and Unc tract profiles were compared between groups and tested for correlation with disease duration. To characterize temporopolar cortex microstructure, DKI maps were sampled at varying depths from superficial white matter (WM) towards the pial surface. Patients were separated according to the temporal lobe ipsilateral to seizure onset and their AFQ results were used as input for statistical analyses. Significant differences were observed between lesional TLE and controls, towards the most temporopolar segment of ILF and Unc proximal to the TP within the ipsilateral temporal lobe in left TLE patients for MK, RK, AWF and MD. No significant changes were observed with DKI maps in the non-lesional TLE group. DKI measurements correlated with disease duration, mostly towards the temporopolar segments of the WM bundles. Stronger differences in MK, RK and AWF within the temporopolar cortex were observed in the lesional TLE and noticeable differences (except for MD) in non-lesional TLE groups compared to controls. This study demonstrates that DKI has potential to detect subtle microstructural alterations within the temporopolar segments of the ILF and Unc and the connected temporopolar cortex in TLE patients including non-lesional TLE subjects. This could aid our understanding of the extrahippocampal areas, more specifically the temporal pole role in seizure generation in TLE and might inform surgical planning, leading to better seizure outcomes.

White matter alterations in pediatric brainstem glioma: An national brain tumor registry of China study

Background

Previous studies have identified alterations in structural connectivity of patients with glioma. However, white matter (WM) integrity measured by diffusion kurtosis imaging (DKI) in pediatric patients with brainstem glioma (BSG) was lack of study. Here, the alterations in WM of patients with BSG were assessed through DKI analyses.

Materials and methods

This study involved 100 patients with BSG from the National Brain Tumor Registry of China (NBTRC) and 50 age- and sex-matched healthy controls from social recruitment. WM tracts were segmented and reconstructed using U-Net and probabilistic bundle-specific tracking. Next, automatic fiber quantitative (AFQ) analyses of WM tracts were performed using tractometry module embedded in TractSeg.

Results

WM quantitative analysis identified alterations in DKI-derived values in patients with BSG compared with healthy controls. WM abnormalities were detected in the projection fibers involved in the brainstem, including corticospinal tract (CST), superior cerebellar peduncle (SCP), middle cerebellar peduncle (MCP) and inferior cerebellar peduncle (ICP). Significant WM alterations were also identified in commissural fibers and association fibers, which were away from tumor location. Statistical analyses indicated the severity of WM abnormality was statistically correlated with the preoperative Karnofsky Performance Scale (KPS) and symptom duration of patients respectively.

Conclusion

The results of this study indicated the widely distributed WM alterations in patients with BSG. DKI-derived quantitative assessment may provide additional information and insight into comprehensively understanding the neuropathological mechanisms of brainstem glioma.

Measuring healthy female nulliparous pubovisceral muscle from diffusion kurtosis imaging

This study explores the feasibility of using diffusion kurtosis imaging (DKI) in the pelvic floor region and assesses the water diffusivity of the pubovisceral muscle. Twenty-seven healthy young nulliparous females underwent DKI at 3.0 T that included 15 gradient directions and three b values (0, 750, and 1500 s/mm² ). The diffusion tensor and diffusion kurtosis metrics values of the pubovisceral muscle were measured after image processing. Two independent sample t-tests, a paired-samples t-test, and a nonparametric hypothesis test were performed as appropriate to compare the differences among different metrics. Twenty-six subjects (mean ± standard deviation age, 25 ± 2 years) were successfully analyzed by measuring the diffusion tensor and diffusion kurtosis metrics of the bilateral pubovisceral muscles. The metrics included mean kurtosis, axial kurtosis, radial kurtosis, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. We found no statistically significant differences for these measurement values between the left and right pubovisceral muscles (p = 0.271-0.931). However, radial kurtosis was greater than axial kurtosis in both pubovisceral muscles (p < 0.001) and axial diffusivity was lower than radial diffusivity in both pubovisceral muscles (p < 0.001). We deem the application of DKI technology to the pelvic floor region to be feasible.

The heterogeneity of tissue destruction between iron rim lesions and non-iron rim lesions in multiple sclerosis: A diffusion MRI study

OBJECTIVES

This study aimed to explore the microstructural heterogeneity of different white matter (WM) tissues in relapsing-remitting multiple sclerosis (RRMS) patients by diffusion magnetic resonance imaging (dMRI) and its correlation with disability and cognitive status.

MATERIALS AND METHODS

A total of 337 iron rim lesions (IRLs), 337 perilesional white matters of IRLs (IRLs-PLWMs), 330 non-iron rim lesions (non-IRLs), 330 non-IRLs-PLWMs, 42 normal-appearing white matters (NAWMs) in 42 RRMS patients, and 30 white matters in healthy controls (WMs in HCs) were enrolled in the lesion-wise analysis. Diffusion kurtosis imaging (DKI) parameters including kurtosis fractional anisotropy (KFA) and mean kurtosis (MK), and diffusion tensor imaging (DTI) parameters including fractional anisotropy (FA) and mean diffusivity (MD) were measured in the six types of tissues. Subgroup analysis was performed between non-IRLs with QSM hyperintense (non-IRLs-H) and non-IRLs with QSM isointense or hypointense (non-IRLs-I), as well as between non-IRLs-H-PLWMs and non-IRLs-I-PLWMs. Thirty-four out of forty-two patients were enrolled in patient-wise analysis. The relationships between these diffusion metrics of patients and their Kurtzke Expanded Disability Status Scale (EDSS) score and Symbol Digit Modalities Test (SDMT) score were analyzed separately by partial correlation analysis with age and disease duration (DD) as covariates.

RESULTS

The KFA, FA, MK, and MD values were significantly different among the six types of tissues. The lowest KFA, FA, and MK values and the highest MD values were revealed in IRLs. There were significant differences in all the enrolled diffusion metrics between IRLs and non-IRLs, as well as between IRLs-PLWMs and non-IRLs-PLWMs (p < 0.05). There were no significant differences between NAWMs and WMs in HCs (p = 1.000 for all enrolled diffusion metrics). For all the enrolled diffusion metrics, no significant differences were found in the subgroup analysis. The FA, MK, and MD values of total lesions (including IRLs and non-IRLs) (r = -0.420, p = 0.017; r = -0.472, p = 0.006; r = -0.475, p = 0.006) and the MK values of IRLs (r = -0.438, p = 0.012) were correlated with the EDSS scores. There was no significant correlation between the diffusion parameter values and the SDMT scores.

CONCLUSION

Our findings demonstrate that IRLs are more destructive than non-IRLs. Similarly, IRLs-PLWMs are more destructive than non-IRLs-PLWMs. Additionally, diffusion parameter values of MS lesions can reflect the disability degree. These findings contribute to a better understanding of the different evolution of MS lesions and the relationship between the disability level of patients and focal lesion damage degree.

Altered Cerebral Microstructure in Adults With Atrial Septal Defect and Ventricular Septal Defect Repaired in Childhood

Background

Delayed brain development, brain injury, and neurodevelopmental disabilities are commonly observed in infants operated for complex congenital heart defect. Our previous findings of poorer neurodevelopmental outcomes in individuals operated for simple congenital heart defects calls for further etiological clarification. Hence, we examined the microstructural tissue composition in cerebral cortex and subcortical structures in comparison to healthy controls and whether differences were associated with neurodevelopmental outcomes.

Methods and Results

Adults (n=62) who underwent surgical closure of an atrial septal defect (n=33) or a ventricular septal defect (n=29) in childhood and a group of healthy, matched controls (n=38) were enrolled. Brain diffusional kurtosis imaging and neuropsychological assessment were performed. Cortical and subcortical tissue microstructure were assessed using mean kurtosis tensor and mean diffusivity and compared between groups and tested for associations with neuropsychological outcomes. Alterations in microstructural tissue composition were found in the parietal, temporal, and occipital lobes in the congenital heart defects, with distinct mean kurtosis tensor cluster‐specific changes in the right visual cortex (pericalcarine gyrus, P=0.002; occipital part of fusiform and lingual gyri, P=0.019). Altered microstructural tissue composition in the subcortical structures was uncovered in atrial septal defects but not in ventricular septal defects. Associations were found between altered cerebral microstructure and social recognition and executive function.

Conclusions

Children operated for simple congenital heart defects demonstrated altered microstructural tissue composition in the cerebral cortex and subcortical structures during adulthood when compared with healthy peers. Alterations in cerebral microstructural tissue composition were associated with poorer neuropsychological performance.

Registration

URL: https://www.clinicaltrials.gov; Unique identifier: NCT03871881.

Diffusion Kurtosis Imaging of Neonatal Spinal Cord in Clinical Routine

Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC.

In vivo Correlation Tensor MRI reveals microscopic kurtosis in the human brain on a clinical 3T scanner

Diffusion MRI (dMRI) has become one of the most important imaging modalities for noninvasively probing tissue microstructure. Diffusional Kurtosis MRI (DKI) quantifies the degree of non-gaussian diffusion, which in turn has been shown to increase sensitivity towards, e.g., disease and orientation mapping in neural tissue. However, the specificity of DKI is limited as different sources can contribute to the total intravoxel diffusional kurtosis, including: variance in diffusion tensor magnitudes (K_iso), variance due to diffusion anisotropy (K_aniso), and microscopic kurtosis (μK) related to restricted diffusion, microstructural disorder, and/or exchange. Interestingly, μK is typically ignored in diffusion MRI signal modeling as it is assumed to be negligible in neural tissues. However, recently, Correlation Tensor MRI (CTI) based on Double-Diffusion-Encoding (DDE) was introduced for kurtosis source separation, revealing non negligible μK in preclinical imaging. Here, we implemented CTI for the first time on a clinical 3T scanner and investigated the sources of total kurtosis in healthy subjects. A robust framework for kurtosis source separation in humans is introduced, followed by estimation of μK (and the other kurtosis sources) in the healthy brain. Using this clinical CTI approach, we find that μK significantly contributes to total diffusional kurtosis both in gray and white matter tissue but, as expected, not in the ventricles. The first μK maps of the human brain are presented, revealing that the spatial distribution of μK provides a unique source of contrast, appearing different from isotropic and anisotropic kurtosis counterparts. Moreover, group average templates of these kurtosis sources have been generated for the first time, which corroborated our findings at the underlying individual-level maps. We further show that the common practice of ignoring μK and assuming the multiple gaussian component approximation for kurtosis source estimation introduces significant bias in the estimation of other kurtosis sources and, perhaps even worse, compromises their interpretation. Finally, a twofold acceleration of CTI is discussed in the context of potential future clinical applications. We conclude that CTI has much potential for future in vivo microstructural characterizations in healthy and pathological tissue.

The Application of Diffusion Kurtosis Imaging on the Heterogeneous White Matter in Relapsing-Remitting Multiple Sclerosis

Objectives

To evaluate the microstructural damage in the heterogeneity of different white matter areas in relapsing-remitting multiple sclerosis (RRMS) patients by using diffusion kurtosis imaging (DKI) and its correlation with clinical and cognitive status.

Materials and Methods

Kurtosis fractional anisotropy (KFA), fractional anisotropy (FA), mean kurtosis (MK), and mean diffusivity (MD) in T1-hypointense lesions (T1Ls), pure T2-hyperintense lesions (pure-T2Ls), normal-appearing white matter (NAWM), and white matter in healthy controls (WM in HCs) were measured in 48 RRMS patients and 26 sex- and age-matched HCs. All the participants were assessed with the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and the Symbol Digit Modalities Test (SDMT) scores as the cognitive status. The Kurtzke Expanded Disability Status Scale (EDSS) scores were used to evaluate the clinical status in RRMS patients.

Results

The lowest KFA, FA, and MK values and the highest MD values were found in T1Ls, followed by pure-T2Ls, NAWM, and WM in HCs. The T1Ls and pure-T2Ls were significantly different in FA (p = 0.002) and MK (p = 0.013), while the NAWM and WM in HCs were significantly different in KFA, FA, and MK (p < 0.001; p < 0.001; p = 0.001). The KFA, FA, MK, and MD values in NAWM (r = 0.360, p = 0.014; r = 0.415, p = 0.004; r = 0.369, p = 0.012; r = −0.531, p < 0.001) were correlated with the MMSE scores and the FA, MK, and MD values in NAWM (r = 0.423, p = 0.003; r = 0.427, p = 0.003; r = −0.359, p = 0.014) were correlated with the SDMT scores.

Conclusion

Applying DKI to the imaging-based white matter classification has the potential to reflect the white matter damage and is correlated with cognitive impairment.

White Matter Abnormalities and Cognitive Deficit After Mild Traumatic Brain Injury: Comparing DTI, DKI, and NODDI

White matter (WM) disruption is an important determinant of cognitive impairment after mild traumatic brain injury (mTBI), but traditional diffusion tensor imaging (DTI) shows some limitations in assessing WM damage. Diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) show advantages over DTI in this respect. Therefore, we used these three diffusion models to investigate complex WM changes in the acute stage after mTBI. From 32 mTBI patients and 31 age-, sex-, and education-matched healthy controls, we calculated eight diffusion metrics based on DTI (fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity), DKI (mean kurtosis), and NODDI (orientation dispersion index, volume fraction of intracellular water (Vic), and volume fraction of the isotropic diffusion compartment). We used tract-based spatial statistics to identify group differences at the voxel level, and we then assessed the correlation between diffusion metrics and cognitive function. We also performed subgroup comparisons based on loss of consciousness. Patients showed WM abnormalities and cognitive deficit. And these two changes showed positive correlation. The correlation between Vic of the splenium of the corpus callosum and Digit Symbol Substitution Test scores showed the smallest p-value (p = 0.000, r = 0.481). We concluded that WM changes, especially in the splenium of the corpus callosum, correlate to cognitive deficit in this study. Furthermore, the high voxel count of NODDI results and the consistency of mean kurtosis and the volume fraction of intracellular water in previous studies and our study showed the functional complementarity of DKI and NODDI to DTI.

SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI

Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled "SDnDTI" for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.

Altered Microstructural Changes Detected by Diffusion Kurtosis Imaging in Patients With Cognitive Impairment After Acute Cerebral Infarction

Objective

To detect the microstructural changes in patients with cognitive impairment after acute cerebral infarction using diffusion kurtosis imaging (DKI).

Materials and Methods

A total of 70 patients with acute cerebral infarction were divided into two groups: 35 patients with cognitive impairment (VCI group), and 35 patients without cognitive impairment (N-VCI group), according to mini-mental state examination (MMSE) score. Healthy individuals (n = 36) were selected as the normal control (NORM) group. DKI parameters from 28 different brain regions of interest (ROIs) were selected, measured, and compared.

Results

VCI group patients had significantly higher mean diffusion (MD) and significantly lower mean kurtosis (MK) values in most ROIs than those in the N-VCI and NORM groups. DKI parameters in some ROIs correlated significantly with MMSE score. The splenium of corpus callosum MD was most correlated with MMSE score, the correlation coefficient was −0.652, and this parameter had good ability to distinguish patients with VCI from healthy controls; at the optimal cut-off MD value (0.9915), sensitivity was 91.4%, specificity 100%, and the area under the curve value 0.964.

Conclusions

Pathological changes in some brain regions may underlie cognitive impairment after acute cerebral infarction, especially the splenium of corpus callosum. These preliminary results suggest that, in patients with VCI, DKI may be useful for assessing microstructural tissue damage.

T2*-weighted imaging and diffusion kurtosis imaging (DKI) of rectal cancer: correlation with clinical histopathologic prognostic factors

PURPOSE

Histopathologic prognostic factors of rectal cancer are closely associated with local recurrence and distant metastasis. We aim to investigate the feasibility of T2*WI in assessment of clinical prognostic factors of rectal cancer, and compare with DKI.

METHODS

This retrospective study enrolled 50 out of 205 patients with rectal cancer according to the inclusion criteria. The following parameters were obtained: R2* from T2*WI, mean diffusivity (MD_k), mean kurtosis (MK), and mean diffusivity (MD_t) from DKI using tensor method. Above parameters were compared by Mann-Whitney U-test or students' t test. Spearman correlations between different parameters and histopathological prognostic factors were determined. The diagnostic performances of R2* and DKI-derived parameters were analyzed by receiver operating characteristic curves (ROC), separately and jointly.

RESULTS

There were positive correlations between R2* and multiple prognostic factors of rectal cancer such as T category, N category, tumor grade, CEA level, and LVI (P < 0.004). MD_k and MD_t showed negative correlations with almost all the histopathological prognostic factors except CRM and TIL involvement (P < 0.003). MK correlated positively with the prognostic factors except CA19-9 level and CRM involvement (P < 0.006). The AUC ranges were 0.724-0.950 for R2* and 0.755-0.913 for DKI-derived parameters for differentiation of prognostic factors. However, no significant differences of diagnostic performance were found between T2*WI, DKI, or the combined imaging methods in characterizing rectal cancer.

CONCLUSION

R2* and DKI-derived parameters were associated with different histopathological prognostic factors, and might act as noninvasive biomarkers for histopathological characterization of rectal cancer.

Comprehensive diffusion MRI dataset for in vivo human brain microstructure mapping using 300 mT/m gradients

Strong gradient systems can improve the signal-to-noise ratio of diffusion MRI measurements and enable a wider range of acquisition parameters that are beneficial for microstructural imaging. We present a comprehensive diffusion MRI dataset of 26 healthy participants acquired on the MGH-USC 3 T Connectome scanner equipped with 300 mT/m maximum gradient strength and a custom-built 64-channel head coil. For each participant, the one-hour long acquisition systematically sampled the accessible diffusion measurement space, including two diffusion times (19 and 49 ms), eight gradient strengths linearly spaced between 30 mT/m and 290 mT/m for each diffusion time, and 32 or 64 uniformly distributed directions. The diffusion MRI data were preprocessed to correct for gradient nonlinearity, eddy currents, and susceptibility induced distortions. In addition, scan/rescan data from a subset of seven individuals were also acquired and provided. The MGH Connectome Diffusion Microstructure Dataset (CDMD) may serve as a test bed for the development of new data analysis methods, such as fiber orientation estimation, tractography and microstructural modelling.

Altered Microstructure of Cerebral Gray Matter in Neuromyelitis Optica Spectrum Disorder-Optic Neuritis: A DKI Study

The purpose of this study was to analyze microstructural alterations in cerebral gray matter using non-Gaussian diffusion kurtosis imaging (DKI) in neuromyelitis optica spectrum disorder (NMOSD) patients with optic neuritis (NMOSD-ON). DKI was performed in 14 NMOSD-ON patients and 22 normal controls (NCs). DKI-derived metrics, including mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), fractional anisotropy (FA), and mean diffusivity (MD), were voxel-wisely compared by two-sample t-tests with gaussian random field (GRF) correction between the two groups. The correlations between altered DKI metrics and clinical features were analyzed. Compared with NCs, NMOSD-ON patients showed significantly decreased MK and RK both in the left inferior temporal gyrus (ITG), and decreased AK in the bilateral calcarine (CAL). While increased MD in the left fusiform gyrus (FFG), right CAL, and right hippocampus (HIP)/parahippocampal gyrus (PHG) were found. Furthermore, correlation analysis showed that mean deviation was negatively correlated with AK values of bilateral CAL and positively correlated with MD values of right CAL (q < 0.05, false discovery rate (FDR) corrected). For NMOSD-ON patients, microstructural abnormalities in the occipital visual cortex are correlated with clinical disability. These findings may provide complementary information to understand the neuropathological mechanisms underlying the impairments of cerebral gray matter in NMOSD-ON.

Widespread White Matter Microstructure Alterations Based on Diffusion Tensor Imaging and Diffusion Kurtosis Imaging in Patients With Pontine Infarction

Neurological deficits after stroke are closely related to white matter microstructure damage. However, secondary changes in white matter microstructure after pontine infarction (PI) in the whole brain remain unclear. This study aimed to investigate the correlation of diffusion kurtosis imaging (DKI)-derived diffusion and kurtosis parameters of abnormal white matter tracts with behavioral function in patients with chronic PI. Overall, 60 patients with unilateral chronic PI (33 patients with left PI and 27 patients with right PI) and 30 normal subjects were recruited and underwent DKI scans. Diffusion parameters derived from diffusion tensor imaging (DTI) and DKI and kurtosis parameters derived from DKI were obtained. Between-group differences in multiple parameters were analyzed to assess the changes in abnormal white matter microstructure. Moreover, we also calculated the sensitivities of different diffusion and kurtosis parameters of DTI and DKI for identifying abnormal white matter tracts. Correlations between the DKI-derived parameters in secondary microstructure changes and behavioral scores in the PI were analyzed. Compared with the NC group, both left PI and right PI groups showed more extensive perilesional and remote white matter microstructure changes. The DKI-derived diffusion parameters showed higher sensitivities than did the DTI-derived parameters. Further, DKI-derived diffusion and kurtosis parameters in abnormal white matter regions were correlated with impaired motor and cognitive function in patients with PI. In conclusion, PI could lead to extensive white matter tracts impairment in perilesional and remote regions. Further, the diffusion and kurtosis parameters could be complementary for identifying comprehensive tissue microstructural damage after PI.

Diffusional kurtosis imaging as a possible prognostic marker of cervical incomplete spinal cord injury outcome: a prospective pilot study

BACKGROUND

Spinal cord injury (SCI) is associated with substantial chronic morbidity and mortality. Routine imaging techniques such as T1- and T2-weighted magnetic resonance imaging (MRI) are not effective in predicting neurological deficiency grade or outcome. Diffusional kurtosis imaging (DKI) is an MR imaging technique that provides microstructural information about biological tissue. There are no longitudinal prospective studies assessing DKI metrics in acute traumatic SCI. Therefore, the purpose of this study was to establish a DKI protocol for acute SCI and correlate the DKI metrics to the functional neurological outcome of the patients.

METHODS

Eight consecutive SCI patients referred to our institution with cervical SCI were included in the study. An acute diagnostic MRI scan was supplemented with a novel fast, mean kurtosis DKI protocol, which describes the average deviation from Gaussian diffusional along nine different directions. Mean kurtosis values were measured at the injury site and normalized to the mean kurtosis values of a non-injured site. At discharge form specialized rehabilitation, patients were evaluated using the Spinal Cord Independence Measure-III (SCIM-III). The DKI metrics and SCIM-III were analysed using Spearman's rank correlation.

RESULTS

This pilot study found a significant correlation between decreasing mean kurtosis values at the injury site of the spinal cord and higher grade of disability measured by the SCIM-III (p = 0.002).

CONCLUSION

This pilot study found that DKI may be a valuable tool as a prognostic marker in the acute phase of SCI.

Assessment of Isocitrate Dehydrogenase 1 Genotype and Cell Proliferation in Gliomas Using Multiple Diffusion Magnetic Resonance Imaging

Objectives: To compare the efficacy of parameters from multiple diffusion magnetic resonance imaging (dMRI) for prediction of isocitrate dehydrogenase 1 (IDH1) genotype and assessment of cell proliferation in gliomas. Methods: Ninety-one patients with glioma underwent diffusion weighted imaging (DWI), multi-b-value DWI, and diffusion kurtosis imaging (DKI)/neurite orientation dispersion and density imaging (NODDI) on 3.0T MRI. Each parameter was compared between IDH1-mutant and IDH1 wild-type groups by Mann-Whitney U test in lower-grade gliomas (LrGGs) and glioblastomas (GBMs), respectively. Further, performance of each parameter was compared for glioma grading under the same IDH1 genotype. Spearman correlation coefficient between Ki-67 labeling index (LI) and each parameter was calculated. Results: The diagnostic performance was better achieved with apparent diffusion coefficient (ADC), slow ADC (D), fast ADC (D^∗), perfusion fraction (f), distributed diffusion coefficient (DDC), heterogeneity index (α), mean diffusivity (MD), mean kurtosis (MK), and intracellular volume fraction (ICVF) for distinguishing IDH1 genotypes in LrGGs, with statistically insignificant AUC values from 0.750 to 0.817. In GBMs, no difference between the two groups was found. For IDH1-mutant group, all parameters, except for fractional anisotropy (FA) and D^∗, significantly discriminated LrGGs from GBMs (P < 0.05). However, for IDH1 wild-type group, only ADC statistically discriminated the two (P = 0.048). In addition, MK has maximal correlation coefficient (r = 0.567, P < 0.001) with Ki-67 LI. Conclusion: dMRI-derived parameters are promising biomarkers for predicting IDH1 genotype in LrGGs, and MK has shown great potential in assessing glioma cell proliferation.