Publications by Year: 2021

PURPOSE: To develop a time-division multiplexing echo-planar imaging (TDM-EPI) sequence for approximately two- to threefold acceleration when acquiring joint relaxation-diffusion MRI data with multiple TEs. METHODS: The proposed TDM-EPI sequence interleaves excitation and data collection for up to 3 separate slices at different TEs and uses echo-shifting gradients to disentangle the overlapping echo signals during the readout period. By properly arranging the sequence event blocks for each slice and adjusting the echo-shifting gradients, diffusion-weighted images from separate slices can be acquired. Therefore, we present 2 variants of the sequence. A single-TE TDM-EPI is presented to demonstrate the concept. Next, a multi-TE TDM-EPI is presented to highlight the advantages of the TDM approach for relaxation-diffusion imaging. These sequences were evaluated on a 3 Tesla scanner with a water phantom and in vivo human brain data. RESULTS: The single-TE TDM-EPI sequence can simultaneously acquire 2 slices with a maximum b value of 3000 s/mm2 and 2.5 mm isotropic resolution using interleaved readout windows with TE ≈ 78 ms. With the same b value and resolution, the multi-TE TDM-EPI sequence can simultaneously acquire 2 or 3 separate slices using interleaved readout sections with shortest TE ≈ 70 ms and ΔTE ≈ 30 ms. Phantom and in vivo experiments have shown that the proposed TDM-EPI sequences can provide similar image quality and diffusion measures as conventional EPI readouts with multiple echoes but can reduce the overall relaxation-diffusion protocol scan time by approximately two- to threefold. CONCLUSION: TDM-EPI is a novel approach to acquire diffusion imaging data at multiple TEs. This enables a significant reduction in acquisition time for relaxation-diffusion MRI experiments but without compromising image quality and diffusion measurements, thus removing a significant barrier to the adoption of relaxation-diffusion MRI in clinical research studies of neurological and mental disorders.

Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting.

BACKGROUND: Pulmonary hypertension is a heterogeneous disease and a significant portion of patients at risk for it have available computed tomography (CT) imaging. Advanced automated processing techniques could be leveraged to for early detection, screening and development of quantitative phenotypes. Pruning and vascular tortuosity have been previously described in pulmonary arterial hypertension (PAH) but the extent of these phenomena in arterial versus venous pulmonary vasculature and in exercise pulmonary hypertension (ePH) have not been described. RESEARCH QUESTION: What are the arterial and venous manifestations of pruning and vascular tortuosity using CT imaging in PAH and do they also occur in ePH? STUDY DESIGN AND METHODS: A cohort of patients with PAH, ePH and controls with available CT angiograms were retrospectively identified to examine the differential arterial and venous presence of pruning and tortuosity in patients with precapillary pulmonary hypertension not confounded by lung or thromboembolic disease The pulmonary vasculature was reconstructed, an AI method was used to separate arteries and veins and used to compute arterial and venous vascular volumes and tortuosity. RESULTS: 42 PAH, 12 ePH, 37 controls were identified. There was relatively lower arterial small vessel volume in subjects with PAH (PAH: 14.7(11.7-16.2) p<0.0001 vs controls 16.9(15.6-19.2)) and venous small vessel volume in subjects with PAH and ePH (PAH: 8.0(6.5-9.6) p<0.0001, ePH:7.8(7.5-11.4) p=0.004 vs control 11.5(10.6-12.2)). Higher large arterial volume, however, was only observed in the pulmonary arteries (PAH: 17.1(13.6-23.4) p<0.0001 vs controls 11.4(8.1-15.4)). Similarly, tortuosity was higher in the pulmonary arteries in PAH (PAH: 3.5(3.3-3.6) p=0.0002, vs control 3.2(3.2-3.3). INTERPRETATION: Lower small distal pulmonary vascular volume, higher proximal arterial volume and higher arterial tortuosity are observed and can be quantified using automated techniques from clinically acquired CT scans of patients with exercise and resting pulmonary arterial hypertension.

People learn new languages with varying degrees of success but what are the neuroanatomical correlates of the difference in language-learning aptitude? In this study, we set out to investigate how differences in cortical morphology and white matter microstructure correlate with aptitudes for vocabulary learning, phonetic memory, and grammatical inferencing as measured by the first-language neutral LLAMA test battery. We used ultra-high field (7T) magnetic resonance imaging to estimate the cortical thickness and surface area from sub-millimeter resolved image volumes. Further, diffusion kurtosis imaging was used to map diffusion properties related to the tissue microstructure from known language-related white matter tracts. We found a correlation between cortical surface area in the left posterior-inferior precuneus and vocabulary learning aptitude, possibly indicating a greater predisposition for storing word-figure associations. Moreover, we report negative correlations between scores for phonetic memory and axial kurtosis in left arcuate fasciculus as well as mean kurtosis, axial kurtosis, and radial kurtosis of the left superior longitudinal fasciculus III, which are tracts connecting cortical areas important for phonological working memory.

Astronauts are exposed to elevated CO2 levels onboard the International Space Station. Here, we investigated structural brain changes in 11 participants following 30-days of head-down tilt bed rest (HDBR) combined with 0.5% ambient CO2 (HDBR + CO2) as a spaceflight analog. We contrasted brain changes observed in the HDBR + CO2 group with those of a previous HDBR sample not exposed to elevated CO2. Both groups exhibited a global upward shift of the brain and concomitant intracranial free water (FW) redistribution. Greater gray matter changes were seen in the HDBR + CO2 group in some regions. The HDBR + CO2 group showed significantly greater FW decrements in the posterior cerebellum and the cerebrum than the HDBR group. In comparison to the HDBR group, the HDBR + CO2 group exhibited greater diffusivity increases. In half of the participants, the HDBR + CO2 intervention resulted in signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), a constellation of ocular structural and functional changes seen in astronauts. We therefore conducted an exploratory comparison compared between subjects that did and did not develop SANS and found asymmetric lateral ventricle enlargement in the SANS group. These results enhance our understanding of the underlying mechanisms of spaceflight-induced brain changes, which is critical for promoting astronaut health and performance.

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.

OBJECTIVES: Overall outcomes for trauma patients have improved over time. However, mortality for postinjury sepsis has been reported to be unchanged. Estimate incidence of and risk factors for sepsis in ICU patients after major trauma and the association between sepsis, mortality, and clinical course. DESIGN SETTING AND PATIENTS: ICU in a large urban trauma center in Sweden with a well-developed trauma system. Retrospective cohort study of trauma patients admitted to the ICU for more than 24 hours were included. MEASUREMENTS AND MAIN RESULTS: Primary outcome measure was 30-day mortality. Secondary outcomes were 1-year mortality and impact on clinical course. In total, 722 patients with a median Injury Severity Score of 26 (interquartile range, 18-38) were included. Incidence of sepsis was 22%. Septic patients had a four-fold increase in length of stay and need for organ supportive therapy. The overall 30-day mortality rate was 9.3%. After exclusion of early trauma-related deaths in the first 48 hours, the 30-day mortality rate was 6.7%. There was an association between sepsis and this adjusted 30-day mortality (day 3 odds ratio, 2.1 [95% CI, 1.1-3.9]; day 4 odds ratio, 3.1 [95% CI, 1.5-6.1]; day 5 odds ratio, 3.0 [95% CI, 1.4-6.2]). Septic patients had a 1-year mortality of 17.7% (nonseptic 11.0%). Development of sepsis was independently associated with age, spine and chest injury, shock, red cell transfusion, and positive blood alcohol concentration at admission. The risk of sepsis increased, in a dose-dependent manner, with the number of transfusions. CONCLUSIONS: Postinjury sepsis was associated with a complicated clinical course and with mortality after exclusion of early, trauma-related deaths.

Background: There have been reports of increasing azole resistance in Candida tropicalis, especially in the Asia-Pacific region. Here we report on the epidemiology and antifungal susceptibility of C. tropicalis causing invasive candidiasis in China, from a 9-year surveillance study.

Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter.