Impairment of Cerebrovascular Hemodynamics in Patients With Severe and Milder Forms of Sickle Cell Disease.

In patients with sickle cell disease (SCD), cerebral blood flow (CBF) is elevated to counteract anemia and maintain oxygen supply to the brain. This may exhaust the vasodilating capacity of the vessels, possibly increasing the risk of silent cerebral infarctions (SCI). To further investigate cerebrovascular hemodynamics in SCD patients, we assessed CBF, arterial transit time (ATT), cerebrovascular reactivity of CBF and ATT (CVR CBF and CVR ATT ) and oxygen delivery in patients with different forms of SCD and matched healthy controls. We analyzed data of 52 patients with severe SCD (HbSS and HbSß0-thal), 20 patients with mild SCD (HbSC and HbSß+-thal) and 10 healthy matched controls (HbAA and HbAS). Time-encoded arterial spin labeling (ASL) scans were performed before and after a vasodilatory challenge using acetazolamide (ACZ). To identify predictors of CBF and ATT after vasodilation, regression analyses were performed. Oxygen delivery was calculated and associated with hemoglobin and fetal hemoglobin (HbF) levels. At baseline, severe SCD patients showed significantly higher CBF and lower ATT compared to both the mild SCD patients and healthy controls. As CBF postACZ was linearly related to CBF preACZ , CVR CBF decreased with disease severity. CVR ATT was also significantly affected in severe SCD patients compared to mild SCD patients and healthy controls. Considering all groups, women showed higher CBF postACZ than men (p < 0.01) independent of baseline CBF. Subsequently, post ACZ oxygen delivery was also higher in women (p < 0.05). Baseline, but not post ACZ, GM oxygen delivery increased with HbF levels. Our data showed that baseline CBF and ATT and CVR CBF and CVR ATT are most affected in severe SCD patients and to a lesser extent in patients with milder forms of SCD compared to healthy controls. Cerebrovascular vasoreactivity was mainly determined by baseline CBF, sex and HbF levels. The higher vascular reactivity observed in women could be related to their lower SCI prevalence, which remains an area of future work. Beneficial effects of HbF on oxygen delivery reflect changes in oxygen dissociation affinity from hemoglobin and were limited to baseline conditions suggesting that high HbF levels do not protect the brain upon a hemodynamic challenge, despite its positive effect on hemolysis.

Non-linear fitting with joint spatial regularization in arterial spin labeling.

Multi-Delay single-shot arterial spin labeling (ASL) imaging provides accurate cerebral blood flow (CBF) and, in addition, arterial transit time (ATT) maps but the inherent low SNR can be challenging. Especially standard fitting using non-linear least squares often fails in regions with poor SNR, resulting in noisy estimates of the quantitative maps. State-of-the-art fitting techniques improve the SNR by incorporating prior knowledge in the estimation process which typically leads to spatial blurring. To this end, we propose a new estimation method with a joint spatial total generalized variation regularization on CBF and ATT. This joint regularization approach utilizes shared spatial features across maps to enhance sharpness and simultaneously improves noise suppression in the final estimates. The proposed method is evaluated at three levels, first on synthetic phantom data including pathologies, followed by in vivo acquisitions of healthy volunteers, and finally on patient data following an ischemic stroke. The quantitative estimates are compared to two reference methods, non-linear least squares fitting and a state-of-the-art ASL quantification algorithm based on Bayesian inference. The proposed joint regularization approach outperforms the reference implementations, substantially increasing the SNR in CBF and ATT while maintaining sharpness and quantitative accuracy in the estimates.

Robust single-shot acquisition of high resolution whole brain ASL images by combining time-dependent 2D CAPIRINHA sampling with spatio-temporal TGV reconstruction.

For ASL perfusion imaging in clinical settings the current guidelines recommends pseudo-continuous arterial spin labeling with segmented 3D readout. This combination achieves the best signal to noise ratio with reasonable resolution but is prone to motion artifacts due to the segmented readout. Motion robust single-shot 3D acquisitions suffer from image blurring due to the T2 decay of the sampled signals during the long readout. To tackle this problem, we propose an accelerated 3D-GRASE sequence with a time-dependent 2D-CAIPIRINHA sampling pattern. This has several advantages: First, the single-shot echo trains are shortened by the acceleration factor; Second, the temporal incoherence between measurements is increased; And third, the coil sensitivity maps can be estimated directly from the averaged k-space data. To obtain improved perfusion images from the undersampled time series, we developed a variational image reconstruction approach employing spatio-temporal total-generalized-variation (TGV) regularization. The proposed ASL-TGV method reduced the total acquisition time, improved the motion robustness of 3D ASL data, and the image quality of the cerebral blood flow (CBF) maps compared to those by a standard segmented approach. An evaluation was performed on 5 healthy subjects including intentional movement for 2 subjects. Single-shot whole brain CBF-maps with high resolution 3.1 × 3.1 × 3 mm and image quality can be acquired in 1min 46sec. Additionally high quality CBF- and arterial transit time (ATT) -maps from single-shot multi-post-labeling delay (PLD) data can be gained with the proposed method. This method may improve the robustness of 3D ASL in clinical settings, and may be applied for perfusion fMRI.

Spatio-temporal TGV denoising for ASL perfusion imaging.

In arterial spin labeling (ASL) a perfusion weighted image is achieved by subtracting a label image from a control image. This perfusion weighted image has an intrinsically low signal to noise ratio and numerous measurements are required to achieve reliable image quality, especially at higher spatial resolutions. To overcome this limitation various denoising approaches have been published using the perfusion weighted image as input for denoising. In this study we propose a new spatio-temporal filtering approach based on total generalized variation (TGV) regularization which exploits the inherent information of control and label pairs simultaneously. In this way, the temporal and spatial similarities of all images are used to jointly denoise the control and label images. To assess the effect of denoising, virtual ground truth data were produced at different SNR levels. Furthermore, high-resolution in-vivo pulsed ASL data sets were acquired and processed. The results show improved image quality, quantitative accuracy and robustness against outliers compared to seven state of the art denoising approaches.