Radovan Jiřík^{1}, Marie Daňková^{2}, Pavel Rajmic^{2}, Lucie Krátká^{1}, Lenka Dvořáková^{1}, Eva Dražanová^{1}, and Zenon Starčuk, jr.^{1}

A DCE-MRI method for absolute quantification of cerebral blood flow (CBF) and volume (CBV) and vessel permeability surface area product is presented. It is based on L+S compressed sensing, the two-compartment exchange model (2CXM) and blind deconvolution estimation of the arterial input function. The method is evaluated on data from a healthy rat.

In brain perfusion imaging, cerebral blood flow (CBF) and volume (CBV) are usually estimated using DSC-MRI. However, DCE-MRI does not provide absolute quantification because of tissue dependent r2 and r2* relaxivities and T1 effects in cases of broken blood brain barrier (BBB).

A possible solution might be use of DCE-MRI. It provides absolute perfusion quantification and is typically used for estimation of permeability of leaky capillaries. DCE-MRI measurement of CBV and especially CBF is difficult due to generally lower r1 relaxivity and low blood volume in a normal tissue. However, it has been shown that it is possible to quantify simultaneously CBV, CBF and permeability in normal brain and brain lesions^{1,2}. The main problem of this approach is to acquire DCE-MRI data with a sufficiently high signal to noise ratio (SNR).

In this initial study, a solution based on compressed sensing (CS) is suggested. It is a way to increase the spatial/temporal resolution and the SNR.

Golden angle radial sampling acquisition^{3} is used (so far as a 2D acquisition method) because it provides a fairly uniform coverage of the k-space with high temporal incoherence for any arbitrary number of consecutive projections. Hence, the temporal sampling rate can be chosen after data acquisition by selecting the number of consecutive projections per image sequence frame.

Furthermore, surface array coil is used for acquisition to include the parallel imaging prior information in the compressed sensing reconstruction. The coil element sensitivities are estimated using the ESPIRiT algorithm^{4} from separate calibration scans.

The spatio-temporal matrix of the measured image sequence is assumed to be a sum of two components: L with low-rank and S with a sparse gradient along the temporal dimension^{5}. Image sequence reconstruction is then performed by solving an optimization problem consisting of a data fidelity term and two regularization terms promoting the L+S model. The solution is found using the proximal gradient algorithm^{6}.

The reconstructed image sequence is then converted to a sequence proportional to the contrast agent (CA) concentration^{7}. The arterial input function (AIF) is estimated using blind multichannel deconvolution^{8} from tissue concentration curves extracted from manually drawn brain regions and assuming an intact BBB and the 2CXM pharmacokinetic model^{9}. Finally, voxel-by-voxel perfusion analysis using the 2CXM model results in perfusion parameter maps.

The gradient echo golden angle acquisition method was implemented on a 9.4T BioSpin (Bruker Biospin MRI, Germany) scanner. An experimental dataset was acquired with a normal Sprague-Dawley rat (experiment approved by national authority) using a 4-element surface rat brain array coil and the following parameters: TR/TE 17/1.7ms, flip angle 25deg., axial slice, thickness 2.4mm, acquisition time 14min, contrast agent (Magnevist, Bayer HealthCare Pharmaceuticals, Germany).

First, a “reference” image sequence was reconstructed using 54 projections per frame and the regridding algorithm^{10}. To test the possibility of imaging of several slices (and later extension to a 3D acquisition and image reconstruction), only 21 projections per frame were used further on. This corresponds to the temporal sampling rate Ts=0.357s. Then, every third frame of the reconstructed sequence was used (Ts=1.071s) which leaves time for a theoretical acquisition of two more slices per frame. Furthermore, a TR cycle of 17ms can include excitation and acquisition of approximately 4 slices, hence this experiment simulated acquisition of 12 slices. Two reconstruction algorithms were applied in this scheme: regridding and compressed sensing.

Golden angle compressed sensing DCE-MRI can be used for absolute quantification of CBF, CBV and PS. It might be especially useful for a better definition of penumbra and for providing additional information on the BBB status. Further extensions to 3D acquisition and reconstruction^{14} is meaningful and will most probably further improve the results.

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Figure 1. Anatomy image, T2-weighted RARE sequence, TR/TE=3500/36 ms, FOV 2.3x3.5 mm, image matrix 256x256, axial slice, slices thickness 0.7 mm.

Figure 2. Reconstructed images (frame 100 – after CA arrival): regridding reconstruction for 54 projections per frame (left), regridding reconstruction for 21 projections per frame (middle), compressed sensing reconstruction for 21 projections per frame (right).

Figure 3. Example tissue concentration curves corresponding to the same voxel in somatosensory cortex: regridding reconstruction for 54 projections per frame (left), regridding reconstruction for 21 projections per frame (middle), compressed sensing reconstruction for 21 projections per frame (right).

Figure 4. Perfusion parameter maps of blood flow (CBF), blood volume (CBV), mean transit time (MTT) and permeability surface area product (PS): regridding reconstruction for 54 projections per frame (left), regridding reconstruction for 21 projections per frame (middle), compressed sensing reconstruction for 21 projections per frame (right).

Figure 5. Boxplots of CBF, CBV and MTT (colums) estimated within three brain regions (rows). “Reg.,54p.” – regridding reconstruction for 54 projections per frame, “Reg.,21p.” – regridding reconstruction for 21 projections per frame, “CS,21p.” – compressed sensing reconstruction for 21 projections per frame.