Sophie Schauman^{1}, Thomas W Okell^{1}, and Mark Chiew^{1}

^{1}Wellcome Centre for Integrative Neuroimaging, NDCN, University of Oxford, Oxford, United Kingdom

Arterial spin labeling methods can be used to produce vessel selective angiograms. However, to do this in 3D or 4D is extremely time consuming as many encodings of high spatial (and temporal) resolution images are needed. We propose an optimized acquisition and reconstruction method to create high quality angiogams is five minutes or less. For the acquisition protocol we explore different sampling patterns across encoded images, and for the reconstruction method different ways of constraining the signal temporally.

VE-ASL angiography is performed like pseudo-continuous ASL with additional transverse gradients to allow for some vessels to be tagged and others to be controlled. This way, multiple ‘encoded’ images can be acquired, allowing for blood originating in different arteries to be decoded. However, fully sampled 3D time-resolved (4D) VE-ASL angiography is extremely time-consuming, requiring hours of scan time.

Methods like parallel imaging (PI

The temporal evolution of the signal follows the smooth dynamic model described by Okell et al.

The reconstruction was performed using FISTA

$$cost = \frac{1}{2} |\mathbf{Ex}-\mathbf{d}|^2_2+\lambda_1 | \mathbf{Sx}|_1 + \frac{1}{2} \lambda_2 |\mathbf{\nabla x}|_2^2$$

Where $$$\mathbf{E}$$$ consists of the vessel encoding, coil sensitivity profiles, and a Fourier sampling operator. Here, $$$\mathbf{x}$$$ is the estimated image (one vector containing all spatial, temporal, and vessel components concatenated), $$$\mathbf{d}$$$ is the acquired data, $$$\mathbf{\nabla}$$$ is the temporal finite difference operator (Fig. 1c), and $$$\lambda_1$$$ and $$$\lambda_2$$$ are weights on the sparsity and temporal smoothness terms respectively. When the temporal model was used, $$$\mathbf{S}$$$ was set to the model-derived temporal basis (Fig. 1b) and $$$\lambda_2$$$ was set to zero. When no temporal model was used, $$$\mathbf{S}$$$ was set to the identity transform.

Simulations were performed on a 128x128x86 numerical phantom with 6 temporal frames based on data acquired for a previous study

Initial feasibility data were also acquired in-vivo from one healthy volunteer. Resolution: 1.1 mm

Regularizing the temporal dimension resulted in higher correlation coefficients when using the smoothness constraint, but the sparse temporal model worsened the result (Fig. 3). The temporal model resulted in residual signal in the later frames that drove down the correlation coefficient.

The in-vivo data was reconstructed with 4 different combinations of acquisition method and temporal regularization (Fig. 4). Here, the paired encodings with varying spokes method with temporal smoothing produced the visibly highest quality images, with reduced background noise and higher vessel contrast.

Figure 5 shows the in-vivo varying spokes + temporal smoothness data in more detail and how the reconstruction behaves as the data gets more and more undersampled, with considerable retention of image quality, even in only 5 minutes of data.

Temporally, we found that the simpler smoothness constraint worked better at regularizing the reconstruction and maintaining the temporal fidelity than using a kinetic model. This could be explained by the low number of frames acquired and therefore low compressibility of the temporal signal.

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Figure 1–The encoding operator, E, consists of a part that models the encoding of the signal coming from different arteries, and a Fourier transform from image to k-sapce. (A) shows E with pairs of opposed vessel encodings with matched trajectories to allow for static tissue signal removal by pairwise subtraction. (B) and (C) show the two types of temporal regularization transforms used in this work; temporal model and temporal smoothness constraints, applied to an example time signal.

Figure 2– In simulation a) same spokes each encoding and c) varying spokes with paired encodings performed best. Varying the spokes every encoding, b), reulted in aliasing of the static tissue into the vessel components and poor image quality. Pre-subtraction, d), produced somewhat noiser results than a) and c).

Figure 3– In simulations, temporal smoothness improved image quality compared to no regularisation, whereas the temporal model actually worsened the result by leaving erroneous residual signal in later frames.

Figure 4– All methods performed well in vivo, however the varying spokes with temporal smoothness constraint produced images with less background noise and sharper delineation of small vessels (e.g. white circle)

Figure 5- In vivo 10, 5, and 1 min 3D images. The five-minute scan is almost indistinguishable from the 10-minute scan. The one-minute scan shows the main vessels but the smaller, distal vessels are lost.