Contrast-free dynamic susceptibility contrast using sinusoidal and bolus oxygenation challenges.

Deoxygenation-based dynamic susceptibility contrast (dDSC) MRI uses respiratory challenges as a source of endogenous contrast as an alternative to gadolinium injection. These gas challenges induce T2*-weighted MRI signal losses, after which tracer kinetics modeling was applied to calculate cerebral perfusion. This work compares three gas challenges, desaturation (transient hypoxia), resaturation (transient normoxia), and SineO2 (sinusoidal modulation of end-tidal oxygen pressures) in a cohort of 10 healthy volunteers (age 37 ± 11 years; 60% female). Perfusion estimates consisted of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). Calculations were computed using a traditional tracer kinetics model in the time domain for desaturation and resaturation and in the frequency domain for SineO2. High correlations and limits of agreement were observed among the three deoxygenation-based paradigms for CBV, although MTT and CBF estimates varied with the hypoxic stimulus. Cross-modality correlation with gadolinium DSC was lower, particularly for MTT, but on a par with agreement between the other perfusion references. Overall, this work demonstrated the feasibility and reliability of oxygen respiratory challenges to measure brain perfusion. Additional work is needed to assess the utility of dDSC in the diagnostic evaluation of various pathologies such as ischemic strokes, brain tumors, and neurodegenerative diseases.

Improved cerebrovascular reactivity mapping using coherence weighted general linear model in the frequency domain.

Cerebrovascular reactivity (CVR) is a prognostic indicator of cerebrovascular health. Estimating CVR from endogenous end-tidal carbon dioxide (CO2) fluctuation and MRI signal recorded under resting state can be difficult due to the poor signal-to-noise ratio (SNR) of signals. Thus, we aimed to improve the method of estimating CVR from end-tidal CO2 and MRI signals. We proposed a coherence weighted general linear model (CW-GLM) to estimate CVR from the Fourier coefficients weighted by the signal coherence in frequency domain, which confers two advantages. First, it requires no signal alignment in time domain, which simplifies experimental methods. Second, it limits the GLM analysis within the frequency band where CO2 and MRI signals are highly correlated, which automatically suppresses noise and nuisance signals. We compared the performance of our method with time-domain GLM (TD-GLM) and frequency-domain GLM (FD-GLM) in both synthetic and in-vivo data; wherein we calculated CVR from signals recorded under both resting state and sinusoidal stimulus. In synthetic data, CW-GLM has a remarkable performance on CVR estimation from narrow band signals with a mean-absolute error of 0.7 % (gray matter) and 1.2 % (white matter), which was lower than all the other methods. Meanwhile, CW-GLM maintains a comparable performance on CVR estimation from resting signals, with a mean-absolute error of 4.1 % (gray matter) and 8 % (white matter). The superior performance was maintained across the 36 in-vivo measurements, with CW-GLM exhibiting limits of agreement of -16.7 % - 9.5 % between CVR calculated from the resting and sinusoidal CO2 paradigms which was 12 % - 209 % better than current time-domain methods. Evaluating of the cross-coherence spectrum revealed highest signal coherence within the frequency band from 0.01 Hz to 0.05 Hz, which overlaps with previously recommended frequency band (0.02 Hz to 0.04 Hz) for CVR analysis. Our data demonstrates that CW-GLM can work as a self-adaptive band-pass filter to improve CVR robustness, while also avoiding the need for signal temporal alignment.

Sinusoidal CO2 respiratory challenge for concurrent perfusion and cerebrovascular reactivity MRI.

Introduction: Deoxygenation-based dynamic susceptibility contrast (dDSC) has previously leveraged respiratory challenges to modulate blood oxygen content as an endogenous source of contrast alternative to gadolinium injection in perfusion-weighted MRI. This work proposed the use of sinusoidal modulation of end-tidal CO2 pressures (SineCO 2 ), which has previously been used to measure cerebrovascular reactivity, to induce susceptibility-weighted gradient-echo signal loss to measure brain perfusion. Methods: SineCO 2 was performed in 10 healthy volunteers (age 37 ± 11, 60% female), and tracer kinetics model was applied in the frequency domain to calculate cerebral blood flow, cerebral blood volume, mean transit time, and temporal delay. These perfusion estimates were compared against reference techniques, including gadolinium-based DSC, arterial spin labeling, and phase contrast. Results: Our results showed regional agreement between SineCO 2 and the clinical comparators. SineCO 2 was able to generate robust CVR maps in conjunction to baseline perfusion estimates. Discussion: Overall, this work demonstrated feasibility of using sinusoidal CO2 respiratory paradigm to simultaneously acquire both cerebral perfusion and cerebrovascular reactivity maps in one imaging sequence.

Chronic anemia: The effects on the connectivity of white matter.

Chronic anemia is commonly observed in patients with hemoglobinopathies, mainly represented by disorders of altered hemoglobin (Hb) structure (sickle cell disease, SCD) and impaired Hb synthesis (e.g. thalassemia syndromes, non-SCD anemia). Both hemoglobinopathies have been associated with white matter (WM) alterations. Novel structural MRI research in our laboratory demonstrated that WM volume was diffusely lower in deep, watershed areas proportional to anemia severity. Furthermore, diffusion tensor imaging analysis has provided evidence that WM microstructure is disrupted proportionally to Hb level and oxygen saturation. SCD patients have been widely studied and demonstrate lower fractional anisotropy (FA) in the corticospinal tract and cerebellum across the internal capsule and corpus callosum. In the present study, we compared 19 SCD and 15 non-SCD anemia patients with a wide range of Hb values allowing the characterization of the effects of chronic anemia in isolation of sickle Hb. We performed a tensor analysis to quantify FA changes in WM connectivity in chronic anemic patients. We calculated the volumetric mean of FA along the pathway of tracks connecting two regions of interest defined by BrainSuite's BCI-DNI atlas. In general, we found lower FA values in anemic patients; indicating the loss of coherence in the main diffusion direction that potentially indicates WM injury. We saw a positive correlation between FA and hemoglobin in these same regions, suggesting that decreased WM microstructural integrity FA is highly driven by chronic hypoxia. The only connection that did not follow this pattern was the connectivity within the left middle-inferior temporal gyrus. Interestingly, more reductions in FA were observed in non-SCD patients (mainly along with intrahemispheric WM bundles and watershed areas) than the SCD patients (mainly interhemispheric).

Anemia Increases Oxygen Extraction Fraction in Deep Brain Structures but Not in the Cerebral Cortex.

Sickle cell disease (SCD) is caused by a single amino acid mutation in hemoglobin, causing chronic anemia and neurovascular complications. However, the effects of chronic anemia on oxygen extraction fraction (OEF), especially in deep brain structures, are less well understood. Conflicting OEF values have been reported in SCD patients, but have largely attributed to different measurement techniques, faulty calibration, and different locations of measurement. Thus, in this study, we investigated the reliability and agreement of two susceptibility-based methods, quantitative susceptibility mapping (QSM) and complex image summation around a spherical or a cylindrical object (CISSCO), for OEF measurements in internal cerebral vein (ICV), reflecting oxygen saturation in deep brain structures. Both methods revealed that SCD patients and non-sickle anemia patients (ACTL) have increased OEF in ICV (42.6% ± 5.6% and 30.5% ± 3.6% in SCD by CISSCO and QSM respectively, 37.0% ± 4.1% and 28.5% ± 2.3% in ACTL) compared with controls (33.0% ± 2.3% and 26.8% ± 1.8%). OEF in ICV varied reciprocally with hematocrit (r 2 = 0.92, 0.53) and oxygen content (r 2 = 0.86, 0.53) respectively. However, an opposite relationship was observed for OEF measurements in sagittal sinus (SS) with the widely used T2-based oximetry, T2-Relaxation-Under-Spin-Tagging (TRUST), in the same cohorts (31.2% ± 6.6% in SCD, 33.3% ± 5.9% in ACTL and 36.8% ± 5.6% in CTL). Importantly, we demonstrated that hemoglobin F and other fast moving hemoglobins decreased OEF by TRUST and explained group differences in sagittal sinus OEF between anemic and control subjects. These data demonstrate that anemia causes deep brain hypoxia in anemia subjects with concomitant preservation of cortical oxygenation, as well as the key interaction of the hemoglobin dissociation curve and cortical oxygen extraction.

MRI restoration using edge-guided adversarial learning.

Magnetic resonance imaging (MRI) images acquired as multislice two-dimensional (2D) images present challenges when reformatted in orthogonal planes due to sparser sampling in the through-plane direction. Restoring the "missing" through-plane slices, or regions of an MRI image damaged by acquisition artifacts can be modeled as an image imputation task. In this work, we consider the damaged image data or missing through-plane slices as image masks and proposed an edge-guided generative adversarial network to restore brain MRI images. Inspired by the procedure of image inpainting, our proposed method decouples image repair into two stages: edge connection and contrast completion, both of which used general adversarial networks (GAN). We trained and tested on a dataset from the Human Connectome Project to test the application of our method for thick slice imputation, while we tested the artifact correction on clinical data and simulated datasets. Our Edge-Guided GAN had superior PSNR, SSIM, conspicuity and signal texture compared to traditional imputation tools, the Context Encoder and the Densely Connected Super Resolution Network with GAN (DCSRN-GAN). The proposed network may improve utilization of clinical 2D scans for 3D atlas generation and big-data comparative studies of brain morphometry.

CEREBRAL BLOOD FLOW AND PREDICTORS OF WHITE MATTER LESIONS IN ADULTS WITH TETRALOGY OF FALLOT.

Long-term outcomes for Tetralogy of Fallot (TOF) have improved dramatically in recent years, but survivors are still afflicted by cerebral damage. In this paper, we characterized the prevalence and predictors of cerebral silent infarction (SCI) and their relationship to cerebral blood flow (CBF) in 46 adult TOF patients. We calculated both whole brain and regional CBF using 2D arterial spin labeling (ASL) images, and investigated the spatial overlap between voxel-wise CBF values and white matter hyperintensities (WMHs) identified from T2-FLAIR images. SCIs were found in 83% of subjects and were predicted by the year of the patient's first cardiac surgery and patient's age at scanning (combined r2 0.44). CBF was not different in brain regions prone to stroke compared with healthy white matter.

ORCHESTRAL FULLY CONVOLUTIONAL NETWORKS FOR SMALL LESION SEGMENTATION IN BRAIN MRI.

White matter (WM) lesion identification and segmentation has proved of clinical importance for diagnosis, treatment and neurological outcomes. Convolutional neural networks (CNN) have demonstrated their success for large lesion load segmentation, but are not sensitive to small deep WM and sub-cortical lesion segmentation. We propose to use multi-scale and supervised fully convolutional networks (FCN) to segment small WM lesions in 22 anemic patients. The multiple scales enable us to identify the small lesions while reducing many false alarms, and the multi-supervised scheme allows a better management of the unbalanced data. Compared to a single FCN (Dice score ~0.31), the performance on the testing dataset of our proposed networks achieved a Dice score of 0.78.