HF-SENSE: an improved partially parallel imaging using a high-pass filter

Abstract

Background One of the major limitations of MRI is its slow acquisition speed. To accelerate data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual aliasing artifacts when the reduction factor goes higher. Undersampling the k-space data and then reconstruct images with artificial sparsity is an efficient way to accelerate data acquisition. By exploiting artificial sparsity with a high-pass filter, an improved SENSE method is proposed in this work, termed high-pass filtered SENSE (HF-SENSE). Methods First, a high-pass filter was applied to the raw k-space data, the result of which was used as the inputs of sensitivity estimation and undersampling process. Second, the adaptive array coil combination method was adopted to calculate sensitivity maps on a block-by-block basis. Third, Tikhonov regularized SENSE was then used to reconstruct magnetic resonance images. Fourth, the reconstructed images were transformed into k-space data, which was filtered with the corresponding inverse filter. Results Both simulation and in vivo experiments demonstrate that HF-SENSE method significantly reduces noise level of the reconstructed images compared with SENSE. Furthermore, it is found that HF-SENSE can achieve lower normalized root-mean-square error value than SENSE. Conclusions The proposed method explores artificial sparsity with a high-pass filter. Experiments demonstrate that the proposed HF-SENSE method can improve the image quality of SENSE reconstruction. The high-pass filter parameters can be predefined. With this image reconstruction method, high acceleration factors can be achieved, which will improve the clinical applicability of SENSE. This retrospective study (HF-SENSE: an improved partially parallel imaging using a high-pass filter) was approved by Institute Review Board of 2nd Affiliated Hospital of Zhejiang University (ethical approval number 2018–314). Participant for all images have informed consent that he knew the risks and agreed to participate in the research.

3D‐EPI blip‐up/down acquisition (BUDA) with CAIPI and joint Hankel structured low‐rank reconstruction for rapid distortion‐free high‐resolution T2* mapping

“…With staggered sampling across both shot and echo dimensions, the g‐factor and SNR benefit may be further improved. Further gains in image quality can be obtained using novel CAIPI sampling, 46–48 virtual coil concept, 49,50 and artificial sparsity‐based image reconstruction 23,51,52 . For instance, skipped‐CAIPI sampling with increased protocol flexibility for high‐resolution EPI 47 can be applied for SNR and efficiency benefit in future 3D‐BUDA.…”

“…Further gains in image quality can be obtained using novel CAIPI sampling, [46][47][48] virtual coil concept, 49,50 and artificial sparsity-based image reconstruction. 23,51,52 For instance, skipped-CAIPI sampling with increased protocol flexibility for high-resolution EPI 47 can be applied for SNR and efficiency benefit in future 3D-BUDA. Shot-selective 2D CAIPIRINHA can also be exploited to further push the limits of acceleration and resolution.…”

Fast and Accurate Tensor Completion With Total Variation Regularized Tensor Trains

“…When the condition number of original normal equation grows to 1118, the benefits of Tikhonov regularization become visible. On the other hand, we exploit the other gain by evaluating TTC, TTC-Tikhonov algorithms on completions of magnetic resonance imaging (MRI) scans of heads and necks 9 [39]. Table VI summarizes the dimensions of the benchmark images and the dimension factorizations considered.…”

Parallel imaging with a combination of sensitivity encoding and generative adversarial networks

“…The aim of SENSE is to exploit the diversity of the spatial sensitivity of the receiver coils, and use it to recover the image from its aliased counterpart. It conglomerates under-sampled data from each independent coil with the modulation of weighting profiles from all coil sensitivity maps (4,5). On the other hand, GRAPPA seeks to build a kernel function from the auto-calibration signal (ACS) lines, and then use them to interpolate the missing k-space data.…”

“…Taking SENSE as an example, the reconstruction performance depends on the accuracy of the estimated coil sensitivity maps (10). Noise in the estimated coil sensitivity maps will be amplified in the reconstructed images due to the illconditioned state of the inverse problem (9), which will cause severe aliasing artifacts (11). For some applications, sensitivity maps are often estimated from the ACS data, which contain some anatomical information, and cause certain residual artifacts in the reconstructed image.…”