A new method is described in which slice profile in 2D multi-slice acquisition is corrected for with k-space-based processing, restoring resolution along the slice select direction. When used with multiple multi-slice acquisitions the method may allow isotropic 3D resolution. The method is described, and preliminary results from phantom and prostate MRI exams are presented.
The approach is illustrated in Figure 1. Figure 1A shows a set of slices acquired in a 2D multi-slice scan with slice selection along Z. The inplane sampling is r, the slice thickness T, and the slice-to-slice spacing S. For illustration assume T:r is 4:1 and S>T. The idea of the approach is to transform the acquired slice information along Z while maintaining the signals in $$$k_X-k_Y$$$ space. Assuming frequency encoding is along $$$k_X$$$, only the $$$k_Y-k_Z$$$ plane is shown (Fig. 1B). Full sampling of k-space is assumed to extend from -1/2r to +1/2r in both directions. The rect-like slice thickness T imposes a sinc-like low-pass filter of width 2/T along $$$k_Z$$$, with the shaded passband shown. Sampling slices at spacing S along Z causes the passband signals to be replicated at intervals of 1/S along $$$k_Z$$$, shown as the dashed horizontal lines, creating aliasing.
The reconstruction can be expressed mathematically. Define $$$\vec{k}$$$ as the desired, unknown, high resolution k-space data vector of size N (assumed here to be along Z) for a specific $$$\left(k_X,k_Y\right)$$$. The multi-slice process subjects $$$\vec{k}$$$ to slice-select-based modulation and shifted replications, both expressed as matrices. This is further down-sampled to match the number M of slices along Z (M<N). This can be expressed as:
$$F \cdot \vec{z} = D \cdot \left \{ A_0 + A_{-1} + A_{+1} + \ldots \right \}\cdot B \cdot \vec{k}\tag{1}$$
where $$$F$$$ is the M-point Fourier transform, $$$\vec{z}$$$ the M×1 acquired data vector along Z, $$$D$$$ is the M×N down-sampling matrix, $$$A_i$$$ express the replications at offsets i(1/S) due to the k-space aliasing (with $$$A_0$$$ the identity matrix $$$I_N$$$), and $$$B$$$ is the N×N diagonal matrix describing the $$$k$$$ modulation due to slice selection. Inversion of Eq. 1 yields an estimate of $$$\vec{k}$$$.
The algorithm of Eq. 1 was tested experimentally in T2SE images of a phantom and of subjects having a prostate MRI exam (for which multi-slice images in A, C, and S orientations are typically all acquired). In the experiments the acquired images were contiguous; i.e. S=T. Also, the algorithm was extended by taking multi-slice data from two orientations (A and C), applying Eq. 1 individually to each and combining results for improved $$$k_Y$$$-$$$k_Z$$$ coverage (Fig. 1C).
Figure 2. (A) Reference 2D multi-slice axial image of prostate. Line shows position of synthetic sagittal image. (B) Sagittal image formed from stacked acquired axial images. (C) Synthetic sagittal image resulting from described algorithm showing improved apparent up-down resolution.