Irimia A, Ngo V, Chaudhari NN, Zhang F, Joshi SH, Penkova AN, Donnell LJO, Sheikh-Bahaei N, Zheng X, Chui HC. White Matter Degradation Near Cerebral Microbleeds Is Associated With Cognitive Change After Mild Traumatic Brain Injury. Neurobiol Aging. 2022;120:68–80.
To explore how cerebral microbleeds (CMBs) accompanying mild traumatic brain injury (mTBI) reflect white matter (WM) degradation and cognitive decline, magnetic resonance images were acquired from 62 mTBI adults (imaged \~7 days and \~6 months post-injury) and 203 matched healthy controls. On average, mTBI participants had a count of 2.7 ± 2.6 traumatic CMBs in WM, located 6.1 ± 4.4 mm from cortex. At \~6-month follow-up, 97% of CMBs were associated with significant reductions (34% ± 11%, q < 0.05) in the fractional anisotropy of WM streamlines within \~1 cm of CMB locations. Male sex and older age were significant risk factors for larger reductions (q < 0.05). For CMBs in the corpus callosum, cingulum bundle, inferior and middle longitudinal fasciculi, fractional anisotropy changes were significantly and positively associated with changes in cognitive functions mediated by these structures (q < 0.05). Our findings distinguish traumatic from non-traumatic CMBs by virtue of surrounding WM alterations and challenge the assumption that traumatic CMBs are neurocognitively silent. Thus, mTBI with CMB findings can be described as a clinical endophenotype warranting longitudinal cognitive assessment.
Turk EA, Stout JN, Feldman HA, Gagoski B, Zhou C, Tamen R, Manhard MK, Adalsteinsson E, Roberts DJ, Golland P, Grant E, Barth WH. Change in T2* Measurements of Placenta and Fetal Organs During Braxton Hicks Contractions. Placenta. 2022;128:69–71.
Maternal-placental perfusion can be temporarily compromised by Braxton Hicks (BH) uterine contractions. Although prior studies have employed T2* changes to investigate the effect of BH contractions on placental oxygen, the effect of these contractions on the fetus has not been fully characterized. We investigated the effect of BH contractions on quantitative fetal organ T2* across gestation together with the birth information. We observed a slight but significant decrease in fetal brain and liver T2* during contractions.
Sack J, Nitsch J, Meine H, Kikinis R, Halle M, Rutherford A. Quantitative Analysis of Liver Disease Using MRI-Based Radiomic Features of the Liver and Spleen. J Imaging. 2022;8(10):277.
BACKGROUND: Radiomics extracts quantitative image features to identify biomarkers for characterizing disease. Our aim was to characterize the ability of radiomic features extracted from magnetic resonance (MR) imaging of the liver and spleen to detect cirrhosis by comparing features from patients with cirrhosis to those without cirrhosis. METHODS: This retrospective study compared MR-derived radiomic features between patients with cirrhosis undergoing hepatocellular carcinoma screening and patients without cirrhosis undergoing intraductal papillary mucinous neoplasm surveillance between 2015 and 2018 using the same imaging protocol. Secondary analyses stratified the cirrhosis cohort by liver disease severity using clinical compensation/decompensation and Model for End-Stage Liver Disease (MELD). RESULTS: Of 167 patients, 90 had cirrhosis with 68.9% compensated and median MELD 8. Combined liver and spleen radiomic features generated an AUC 0.94 for detecting cirrhosis, with shape and texture components contributing more than size. Discrimination of cirrhosis remained high after stratification by liver disease severity. CONCLUSIONS: MR-based liver and spleen radiomic features had high accuracy in identifying cirrhosis, after stratification by clinical compensation/decompensation and MELD. Shape and texture features performed better than size features. These findings will inform radiomic-based applications for cirrhosis diagnosis and severity.
Mayer C, Nägele FL, Petersen M, Frey BM, Hanning U, Pasternak O, Petersen E, Gerloff C, Thomalla G, Cheng B. Free-Water Diffusion MRI Detects Structural Alterations Surrounding White Matter Hyperintensities in the Early Stage of Cerebral Small Vessel Disease. J Cereb Blood Flow Metab. 2022;42(9):1707–18.
In cerebral small vessel disease (CSVD), both white matter hyperintensities (WMH) of presumed vascular origin and the normal-appearing white matter (NAWM) contain microstructural brain alterations on diffusion-weighted MRI (DWI). Contamination of DWI-derived metrics by extracellular free-water can be corrected with free-water (FW) imaging. We investigated the alterations in FW and FW-corrected fractional anisotropy (FA-t) in WMH and surrounding tissue and their association with cerebrovascular risk factors. We analysed 1,000 MRI datasets from the Hamburg City Health Study. DWI was used to generate FW and FA-t maps. WMH masks were segmented on FLAIR and T1-weighted MRI and dilated repeatedly to create 8 NAWM masks representing increasing distance from WMH. Linear models were applied to compare FW and FA-t across WMH and NAWM masks and in association with cerebrovascular risk. Median age was 64 ± 14 years. FW and FA-t were altered 8 mm and 12 mm beyond WMH, respectively. Smoking was significantly associated with FW in NAWM (p = 0.008) and FA-t in WMH (p = 0.008) and in NAWM (p = 0.003) while diabetes and hypertension were not. Further research is necessary to examine whether FW and FA-t alterations in NAWM are predictors for developing WMH.
Bonkhoff AK, Hong S, Bretzner M, Schirmer MD, Regenhardt RW, Arsava M, Donahue K, Nardin M, Dalca A, Giese AK, Etherton MR, Hancock BL, Mocking SJT, McIntosh E, Attia J, Benavente O, Cole JW, Donatti A, Griessenauer C, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner S, Lemmens R, Levi C, McDonough CW, Meschia J, Phuah CL, Rolfs A, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Soederholm M, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle P, Worrall BB, Jern C, Lindgren AG, Maguire J, Golland P, Bzdok D, Wu O, Rost NS. Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome. Neurology. 2022;99(13):e1364-e1379.
BACKGROUND AND OBJECTIVES: To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern-specific ways. METHODS: MR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3-6 months after stroke were obtained from the MRI-Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning-based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score &gt;2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age<sup>2</sup>, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. RESULTS: A total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location-specific WMH effects on stroke severity, higher WMH burden increased the odds of unfavorable outcomes independent of lesion location. DISCUSSION: Higher WMH burden may be associated with an increased stroke severity in case of stroke lesions involving left-hemispheric insular, opercular, and inferior frontal regions (potentially linked to language functions) and right-hemispheric temporoparietal regions (potentially linked to attention). Our findings suggest that patients with specific constellations of WMH burden and lesion locations may have greater benefits from acute recanalization treatments. Future clinical studies are warranted to systematically assess this assumption and guide more tailored treatment decisions.
Vasung L, Xu J, Abaci-Turk E, Zhou C, Holland E, Barth WH, Barnewolt C, Connolly S, Estroff J, Golland P, Feldman HA, Adalsteinsson E, Grant E. Cross-sectional Observational Study of Typical in-utero Fetal Movements using Machine Learning. Dev Neurosci. 2022;.
Early variations of fetal movements are the hallmark of a healthy developing central nervous system. However, there are no automatic methods to quantify the complex 3D motion of the developing fetus in-utero. The aim of this prospective study was to use machine learning (ML) on in-utero MRI to perform quantitative kinematic analysis of fetal limb movement, assessing the impact of maternal, placental, and fetal factors. In this cross-sectional, observational study, we used 76 sets of fetal (24-40 gestational weeks (GW)) blood oxygenation level-dependent (BOLD) MRI scans of 52 women (18-45 years old) during typical pregnancies. Pregnant women were scanned for 5 to 10 minutes while breathing room air (21% O2) and for 5 to 10 minutes while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 minutes in total with an effective temporal resolution of approximately 3 seconds. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network (CNN) previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors manually corrected and the absolute movement time (AMT) for each joint was calculated. To identify variables that had a significant association with AMT, we constructed a mixed-model ANOVA with interaction terms. Fetuses showed significantly longer duration of limb movements during maternal hyperoxia. We also found a significant centrifugal increase of AMT across limbs and significantly longer AMT of upper extremities < 31 GW and longer AMT of lower extremities > 35 GW. In conclusion, using ML we successfully quantified complex 3D fetal limb motion in-utero and across gestation, showing maternal factors (hyperoxia) and fetal factors (gestational age, joint) impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.
Diao B, Bagayogo NA, Carreras NP, Halle M, Ruiz-Alzola J, Ungi T, Fichtinger G, Kikinis R. The Use of 3D Digital Anatomy Model Improves the Communication With Patients Presenting With Prostate Disease: The First Experience in Senegal. PLoS One. 2022;17(12):e0277397.
OBJECTIVES: We hypothesized that the use of an interactive 3D digital anatomy model can improve the quality of communication with patients about prostate disease. METHODS: A 3D digital anatomy model of the prostate was created from an MRI scan, according to McNeal’s zonal anatomy classification. During urological consultation, the physician presented the digital model on a computer and used it to explain the disease and available management options. The experience of patients and physicians was recorded in questionnaires. RESULTS: The main findings were as follows: 308 patients and 47 physicians participated in the study. In the patient group, 96.8% reported an improved level of understanding of prostate disease and 90.6% reported an improved ability to ask questions during consultation. Among the physicians, 91.5% reported improved communication skills and 100% reported an improved ability to obtain patient consent for subsequent treatment. At the same time, 76.6% of physicians noted that using the computer model lengthened the consultation. CONCLUSION: This exploratory study found that the use of a 3D digital anatomy model in urology consultations was received overwhelmingly favorably by both patients and physicians, and it was perceived to improve the quality of communication between patient and physician. A randomized study is needed to confirm the preliminary findings and further quantify the improvements in the quality of patient-physician communication.


Zhang F, Cho KIK, Tang Y, Zhang T, Kelly S, Di Biase M, Xu L, Li H, Matcheri K, Whitfield-Gabrieli S, Niznikiewicz M, Stone WS, Wang J, Shenton ME, Pasternak O. MK-Curve Improves Sensitivity to Identify White Matter Alterations in Clinical High Risk for Psychosis. Neuroimage. 2021;226:117564.

Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.

Ramos-Llordén G, Vegas-Sánchez-Ferrero G, Liao C, Westin CF, Setsompop K, Rathi Y. SNR-Enhanced Diffusion MRI With Structure-Preserving Low-Rank Denoising in Reproducing Kernel Hilbert Spaces. Magn Reson Med. 2021;86(3):1614–32.

PURPOSE: To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages nonlinear redundancy in the data to boost the SNR while preserving signal information. METHODS: We exploit nonlinear redundancy of the dMRI data by means of kernel principal component analysis (KPCA), a nonlinear generalization of PCA to reproducing kernel Hilbert spaces. By mapping the signal to a high-dimensional space, a higher level of redundant information is exploited, thereby enabling better denoising than linear PCA. We implement KPCA with a Gaussian kernel, with parameters automatically selected from knowledge of the noise statistics, and validate it on realistic Monte Carlo simulations as well as with in vivo human brain submillimeter and low-resolution dMRI data. We also demonstrate KPCA denoising on multi-coil dMRI data. RESULTS: SNR improvements up to 2.7 were obtained in real in vivo datasets denoised with KPCA, in comparison to SNR gains of up to 1.8 using a linear PCA denoising technique called Marchenko-Pastur PCA (MPPCA). Compared to gold-standard dataset references created from averaged data, we showed that lower normalized root mean squared error was achieved with KPCA compared to MPPCA. Statistical analysis of residuals shows that anatomical information is preserved and only noise is removed. Improvements in the estimation of diffusion model parameters such as fractional anisotropy, mean diffusivity, and fiber orientation distribution functions were also demonstrated. CONCLUSION: Nonlinear redundancy of the dMRI signal can be exploited with KPCA, which allows superior noise reduction/SNR improvements than the MPPCA method, without loss of signal information.

Szczepankiewicz F, Westin CF, Nilsson M. Gradient Waveform Design for Tensor-valued Encoding in Diffusion MRI. J Neurosci Methods. 2021;348:109007.

Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the ’shape of the b-tensor’ as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.