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

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

Stefan M Spann et al. Neuroimage. 2020.

Abstract

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.

Keywords: ASL; Arterial spin labeling; CAIPIRINHA; CBF; Cerebral blood flow; Spatio-temporal reconstruction; Total generalized variation (TGV).

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Figures

Fig. 1.

(A) Sequence diagram for accelerated 3D-GRASE acquisition with variable 2D-CAIPIRINHA pattern and balanced pCASL labeling (dotted lines are modifications in control condition). The background suppression consists of a pre-saturation and 2 non-selective inversion pulses. The acquisition strategy is shown for a CAIPIRINHA 1 × 6(2) pattern. A center out acquisition is used as illustrated in (B). For one C/L-pair the same 2D-CAIPIRINHA pattern is used. Between subsequent C/L-pairs the pattern is shifted in PE1 or PE2 direction as exemplary shown in (C). This variation increases the temporal incoherence between the C/L-pairs and additionally allows the estimation of the coil sensitivity maps directly from the summed data. Subfigure (D) shows for comparison the acquisition scheme of the fully sampled but segmented approach for the used settings.

Fig. 2.

Simulated ASL dataset in ASL space. (A) Co-registered T1w-image, (B) Proton-density weighted image (M0), (C) synthetic simulated noise-free CBF-map with a hyperperfusion area (120 ml/100 g/min) indicated with a red arrow and the hypoperfusion area (0 ml/100 g/min) indicated with a blue arrow, (D) PWI after blurring using a simulated modulation transfer function (MTF), (E) control image and (F) label image.

Fig. 3.

Transversal and sagittal view of one representative slice of CBF–map from the simulated synthetic dataset. Performance comparison of the fully sampled CBF-maps and the proposed accelerated 2D-time CAIPIRINHA acquisition using different reconstruction approaches and a different number of averages. In the transversal views the hyperperfusion (indicated with a red arrow) and hypoperfusion area (indicated with a blue arrow) are clearly visible in the segmented as well as in the CBF-maps using the proposed ASL-TGV method. The qualitative improvement in image quality and noise suppression is in concordance with the quantitative metrics SSIM and PSNR. Note that the SSIM and PSNR values in the sagittal views are calculated over the whole brain.

Fig. 4.

Mean and standard deviation of CBF-values in four different areas of the synthetic CBF-map for the fully sampled (Full) and accelerated approach in dependence of different numbers of C/L-pairs. Note that the standard deviation in the noise free GT is due to blurring and incorporation of GM and WM values with a probability higher than 90%. Additionally, a N of 30 corresponds to 5 C/L-pairs for the fully sampled approach due to a 6-fold lower temporal resolution.

Fig. 5.

(A) Comparison of the TSNR of the PWIs averaged over all subjects for GM, WM and whole brain between different acquisition and reconstruction methods. The error bars shows the standard deviation. (B) Single perfusion weighted image from subject 5 for the segmented approaches (fully and accelerated sum) and the proposed single-shot method reconstructed with different algorithms.

Fig. 6.

One representative slice of CBF-map in dependence of different number of C/L-pairs of example subject 1. Nfull is the number of C/L-pairs acquired for the fully sampled data and Nacc is the number of C/L-pairs acquired for the proposed accelerated single-shot data (Nfull = 5/4/3/2; tacq = 4 min 30sec/3 min 41sec/2 min 52sec/2 min 3sec, Nacc = 30/24/18/12; tacq = 4 min 14sec/3min 25sec/2min 36sec/1min 46sec). On the left side are the CBF-maps where the subject was asked to lie as still as possible, whereas on the right side are the results from the subject moving his head during the acquisition. As expected the results of the segmented acquisition shows lots of artifacts for the motion case whereas the single-shot methods perform well and delivers CBF-maps with good image quality. The highest improvement in image quality is achieved with the proposed ASL-TGV approach. The red arrow indicates remaining motion artifacts for the accelerated sum approach.

Fig. 7.

Mean and standard deviation of CBF-values in GM and WM of subject 1 for the fully sampled approach (Full) and the proposed accelerated acquisition combined with different reconstruction methods using a different number of C/L-pairs. The error bars denote ±1 standard deviation.

Fig. 8.

Different slices of CBF-maps for the highest and lowest number of averages (Nfull = 5/2; tacq = 4 min 30sec/2 min 3sec, Nacc = 30/12; tacq = 4min 14sec/1min 46sec) of subject 2. The single-shot CBF-maps reconstructed with the ASL-TGV approach shows an improved image quality compared to fully sampled but segmented acquisition for the motionless acquisition. In case of subject movement, the CBF-maps of the segmented approach are not interpretable whereas for the single-shot method the motion can be corrected retrospectively, which results in CBF-maps with a good image quality.

Fig. 9.

Mean CBF-values in GM and WM of subject 2 for the fully sampled but segmented approach and the proposed accelerated single-shot acquisition in combination with different reconstruction approaches. The error bars denote ±1 standard deviation.

Fig. 10.

One representative slice of PWI at different PLDs and the corresponding estimated CBF- and ATT-maps. The red arrow indicates areas where the ASL-TGV method provides more details in the CBF-map.

Fig. 11.

Mean CBF- and ATT-values in GM and WM for the fully sampled but segmented approach and the proposed accelerated single-shot acquisition in combination with different reconstruction approaches. The error bars denote ±1 standard deviation.

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