Accelerated Multi-band Magnetic Resonance Fingerprinting Using Spiral in-out with additional kz Encoding and Modified Sliding Window Reconstruction
Di Cui1, Xiaoxi Liu1, Hing-Chiu Chang1, Queenie Chan2, and Edward S Hui1

1Diagnostic Radiology, The University of Hong Kong, Hong Kong, China, 2Philips Healthcare, Hong Kong, China


Multi-band Magnetic Resonance Fingerprinting can be achieved using UNFOLD-like acquisition and dictionary matching without using parallel methods. However, the MR parametric maps after dictionary matching in one slice suffers from artifacts due to the high frequency components of other simultaneously acquired slices. In this work, a new acquisition strategy was proposed for the multi-band acquisition, where spiral-in-out trajectory was used to provide extra kz encoding. A modified sliding window reconstruction was also proposed to reduce the high frequency oscillations.


Magnetic Resonance Fingerprinting (MRF) [1] is a novel approach to efficiently quantify multiple MR parameters, and multi-band (MB) has been implemented in MRF for further acceleration. UNFOLD [2] is an effective method to decouple the simultaneously acquired slices in multi-band MRF, given the different frequencies of their signal evolution. A major problem in UNFOLD-based MB-MRF is that the desired low frequency component of target slice is usually collapsed with high frequency artifacts from other slices due to undersampling and signal evolution. Here we proposed spiral-in-out MB-MRF to alleviate this problem in two aspects. First, one additional blip gradient was added in spiral in-out sequence, leading to better separation of frequency components using UNFOLD. Second, a modified sliding window [3] reconstruction was proposed to remove the high frequency artifacts before dictionary matching.


A FISP-MRF [4] sequence was used and the readout gradient of our proposed sequence is shown in Fig 1a and the associated spiral-in-out trajectory in Fig 1b. The flip angle (FA) and repetition time (TR) trains are respectively shown in Figs 1c and 1d. The additional Gz blip (the green trapezoid in Fig 1a) is inserted in between the spiral-in and spiral-out readout portion, thereby introducing a $$$\Delta k_{z}$$$ shift between the two readouts. The additional Gz blip is equivalent to phase encoding step along kz. The encoding pattern of our proposed MRF acquisition is better visualized in the kz-t domain in Fig 2. Figs 2a and 2c show the interleaved acquisition pattern, which is similar to the stack of spiral 3D MRF [6]. Figs 2b and 2d show two representative sampling patterns using the additional Gz blip in our proposed acquisition strategy. Note that the density should be compensated along kz before reconstruction or matching for the sampling strategy in Fig 2b.

Given our proposed sampling strategy, the image series of different slices are encoded with unique phase pattern that favors UNFOLD. An example for MB factor of 3 is illustrated in Fig 3. The dictionary matching process is to search the maximum inner product between the acquired MR signal and dictionary $$$\sum_{t=0}^N m_{i,j}(t) = \sum_{t=0}^N s_{i}(t)d_{j}(t)$$$ where $$$\textbf{s}_i$$$ and $$$\textbf{d}_j$$$ respectively indicate the signal evolution vector for the ith voxel and the jth dictionary vector, $$$\textbf{m}_{i,j}=\textbf{s}_i\cdot\textbf{d}_j$$$, and $$$s_{i}$$$, $$$d_{j}$$$ and $$$m_{i,j}$$$ are their tth elements, and N is the number of TR. If $$$\textbf{S}_{i}$$$, $$$\textbf{D}_{j}$$$ and $$$\textbf{M}_{i,j}$$$ are Fourier transform of $$$\textbf{s}_i$$$, $$$\textbf{d}_j$$$ and $$$\textbf{m}_{i,j}$$$, then $$$\textbf{M}_{i,j}=\textbf{S}_{i}\circledast\textbf{D}_{j}$$$, where $$$\circledast$$$ indicates convolution, so $$$\sum_{t=0}^N m_{i,j}(t) = M_{i,j}(0)=\sum_{\omega=0}^N S_{i}(\omega)D_{j}(-\omega)$$$. Then the target low frequency component in signal is chosen by $$$D_{j}(\omega)$$$. Due to the high in-plane undersampling factor, the high frequency components of signal are not neglectable. The low frequency component is then collapsed with the high frequency components of other slices. With the extra encoding of kz, the frequency of different slices became more sparsely distributed, thus rendering less effect on each other.

To further reduce the high frequency artifact, a modified sliding window reconstruction method was proposed. As shown in Fig 2e, sliding window gridding was performed for each kz so that the combined k-space data still followed the desired sampling pattern in kz-t, and that in-plane artifacts and, more importantly, high frequency components of signals were subsequently reduced.

All data were acquired with a 3T MRI scanner (Achieva TX, Philips Healthcare) with 8-channel head coil. An in-house FISP-MRF sequence was used with imaging parameters: TE = 6ms, TI = 20ms, FOV = 300 x 300 mm2, image resolution = 1.17 x1.17 x 5 mm3, acquisition matrix size = 256x256. The acquisition window is 8.4ms, with a rotation increment of gold angle 222.5 degree for different dynamics, and TR varied between 12~14 ms.

Results and Discussion

Figs 4 and 5 show the parameter maps with MB = 3 and MB = 4 respectively. For 2D MRF, spiral-in-out trajectory can reduce the error caused by off-resonance effect, especially with long acquisition window. For MB-MRF, the use of our proposed spiral-in-out trajectory in conjunction with an additional Gz blip allows two different kz encoding within one TR, which helps unfold the slices in subsequent procedures. During the reconstruction of dynamic images, a modified sliding window method was proposed to remove the high frequency artifacts from irrelevant slices. Our results showed the proposed method improved the quality of UNFOLD-like MRF matching, thereby permitting higher MB factor (Figs 4 and 5).


A new acquisition strategy and reconstruction method were proposed for MB-MRF that consist of an additional Gz blip for kz encoding and modified sliding window for improving the UNFOLD performance for MB-MRF.


No acknowledgement found.


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Fig 1. (a) Our proposed spiral-in-out readout for MB-MRF sequence. The additional Gz blip (green) was introduced to provide extra kz encoding. (b) Spiral-in-out trajectory. (c) Flip angle and (d) TR trains.

Fig 2. Multi-band MRF acquisition strategy in kz-t domain for the case of MB factor of 3 (a) without and (b) with extra kz encoding; MB factor of 4 (c) without and (d) with extra kz encoding. (e) Our proposed modified sliding window reconstruction method.

Fig 3. (a) MR signal evolution from MB-MRF with MB factor of 3. (b) Simulated signal of related dictionary entry. (c) The Fourier transform of the signal in b, and (d) the Fourier transform of the signal in a. (e)-(g) MR signal evolutions of the 3 simultaneously acquired slices in a. The shaded area indicated the low frequency components of the signal from one slice in e, which is collapsed with the high frequency components of the signal from the other two slices in f and g.

Fig 4. MR parametric maps from MB-MRF with MB factor of 3 obtained from (a) single slice MRF result[MOU1] , (b) without and (c) with additional kz encoding, (d) with modified sliding window, and (e) with additional kz encoding and modified sliding window. [MOU1]What do you mean by reference?

Fig 5. MR parametric maps from MB-MRF with MB factor of 4 obtained from (a) single slice MRF result[MOU1] , (b) without and (c) with additional kz encoding, (d) with modified sliding window, and (e) with additional kz encoding and modified sliding window. [MOU1]What do you mean by reference?

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)