Sensitivity Encoding Reconstruction Research Articles

Low-rank motion correction for accelerated free-breathing first-pass myocardial perfusion imaging.

Develop a novel approach for accelerated 2D free-breathing myocardial perfusion via low-rank motion-corrected (LRMC) reconstructions. Myocardial perfusion imaging requires high spatial and temporal resolution, despite scan time constraints. Here, we incorporate LRMC models into the reconstruction-encoding operator, together with high-dimensionality patch-based regularization, to produce high quality, motion-corrected myocardial perfusion series from free-breathing acquisitions. The proposed framework estimates beat-to-beat nonrigid respiratory (and any other incidental) motion and the dynamic contrast subspace from the actual acquired data, which are then incorporated into the proposed LRMC reconstruction. LRMC was compared with iterative SENSitivity Encoding(SENSE) (itSENSE) and low-rank plus sparse (LpS) reconstruction in 10 patients based on image-quality scoring and ranking by two clinical expert readers. LRMC achieved significantly improved results relative to itSENSE and LpS in terms of image sharpness, temporal coefficient of variation, and expert reader evaluation. Left ventricle image sharpness was approximately 75%, 79%, and 86% for itSENSE, LpS and LRMC, respectively, indicating improved image sharpness for the proposed approach. Corresponding temporal coefficient of variation results were 23%, 11% and 7%, demonstrating improved temporal fidelity of the perfusion signal with the proposed LRMC. Corresponding clinical expert reader scores (1-5, from poor to excellent image quality) were 3.3, 3.9 and 4.9, demonstrating improved image quality with the proposed LRMC, in agreement with the automated metrics. LRMC produces motion-corrected myocardial perfusion in free-breathing acquisitions with substantially improved image quality when compared with iterative SENSE and LpS reconstructions.

Liver diffusion-weighted MR imaging with L1-regularized iterative sensitivity encoding reconstruction based on single-shot echo-planar imaging: initial clinical experience

To investigate whether combining L1-regularized iterative sensitivity encoding (SENSE) reconstruction and single-shot echo planar imaging (EPI) is useful in hepatic DWI. Single-shot EPI-DWI with L1-regularized iterative SENSE reconstruction (L1-DWI) and conventional parallel imaging-based reconstruction (conv-DWI) in liver MRI were compared in volunteers and patients. For the patient cohort, 75 subjects (60 ± 13 years) with 349 focal liver lesions (FLL) were included. Patient groups A and B were used to reduce acquisition time or improve spatial resolution, respectively. Image parameters were rated on a 5-point scale. The number of FLLs was recorded; in case of discrepancy, the reason for non-detectability was analyzed. In volunteers, higher signal-to-noise ratio (24.4 ± 5.6 vs. 12.2 ± 2.3, p < 0.001 at b = 0; 19.3 ± 2.8 vs. 9.8 ± 1.6, p < 0.001 at b = 800) and lower standard deviation of the apparent diffusion coefficient-values (0.17 vs. 0.20 mm2/s, p < 0.05) were found on L1-DWI compared to conv-DWI. In patients, image ratings were similar for all parameters except for “conspicuity of FLLs” which was rated significantly lower on L1-DWI vs. conv-DWI (4.7 ± 0.6 vs. 4.2 ± 0.9, p < 0.05) in group A. In five patients, 11/349 FLLs were not detectable on L1-DWI, but on conv-DWI. L1-regularized iterative reconstruction of single-shot EPI DWI can accelerate image acquisition or improve spatial resolution. However, our finding that FLLs were non-detectable on L1-DWI warrants further research.

Accelerating whole-heart 3D T2 mapping: Impact of undersampling strategies and reconstruction techniques.

PurposeWe aim to determine an advantageous approach for the acceleration of high spatial resolution 3D cardiac T2 relaxometry data by comparing the performance of different undersampling patterns and reconstruction methods over a range of acceleration rates.MethodsMulti-volume 3D high-resolution cardiac images were acquired fully and undersampled retrospectively using 1) optimal CAIPIRINHA and 2) a variable density random (VDR) sampling. Data were reconstructed using 1) multi-volume sensitivity encoding (SENSE), 2) joint-sparsity SENSE and 3) model-based SENSE. Four metrics were calculated on 3 naïve swine and 8 normal human subjects over a whole left-ventricular region of interest: root-mean-square error (RMSE) of image signal intensity, RMSE of T2, the bias of mean T2, and standard deviation (SD) of T2. Fully sampled data and volume-by-volume SENSE with standard equally spaced undersampling were used as references. The Jaccard index calculated from one swine with acute myocardial infarction (MI) was used to demonstrate preservation of segmentation of edematous tissues with elevated T2.ResultsIn naïve swine and normal human subjects, all methods had similar performance when the net reduction factor (Rnet) <2.5. VDR sampling with model-based SENSE showed the lowest RMSEs (10.5%-14.2%) and SDs (+1.7–2.4 ms) of T2 when Rnet>2.5, while VDR sampling with the joint-sparsity SENSE had the lowest bias of mean T2 (0.0–1.1ms) when Rnet>3. The RMSEs of parametric T2 values (9.2%-24.6%) were larger than for image signal intensities (5.2%-18.4%). In the swine with MI, VDR sampling with either joint-sparsity or model-based SENSE showed consistently higher Jaccard index for all Rnet (0.71–0.50) than volume-by-volume SENSE (0.68–0.30).ConclusionsRetrospective exploration of undersampling and reconstruction in 3D whole-heart T2 parametric mapping revealed that maps were more sensitive to undersampling than images, presenting a more stringent limiting factor on Rnet. The combination of VDR sampling patterns with model-based or joint-sparsity SENSE reconstructions were more robust for Rnet>3.

GPU based parallel framework for receiver coil sensitivity estimation in SENSE reconstruction

Magnetic Resonance Imaging (MRI) uses non-ionizing radiations and is safer as compared to CT and X-ray imaging. MRI is broadly used around the globe for medical diagnostics. One main limitation of MRI is its long data acquisition time. Parallel MRI (pMRI) was introduced in late 1990's to reduce the MRI data acquisition time. In pMRI, data is acquired by under-sampling the Phase Encoding (PE) steps which introduces aliasing artefacts in the MR images. SENSitivity Encoding (SENSE) is a pMRI based method that reconstructs fully sampled MR image from the acquired under-sampled data using the sensitivity information of receiver coils. In SENSE, precise estimation of the receiver coil sensitivity maps is vital to obtain good quality images. Eigen-value method (a recently proposed method in literature for the estimation of receiver coil sensitivity information) does not require a pre-scan image unlike other conventional methods of sensitivity estimation. However, Eigen-value method is computationally intensive and takes a significant amount of time to estimate the receiver coil sensitivity maps. This work proposes a parallel framework for Eigen-value method of receiver coil sensitivity estimation that exploits its inherent parallelism using Graphics Processing Units (GPUs). We evaluated the performance of the proposed algorithm on in-vivo and simulated MRI datasets (i.e. human head and simulated phantom datasets) with Peak Signal-to-Noise Ratio (PSNR) and Artefact Power (AP) as evaluation metrics. The results show that the proposed GPU implementation reduces the execution time of Eigen-value method of receiver coil sensitivity estimation (providing up to 30 times speed up in our experiments) without degrading the quality of the reconstructed image.

Transfer learning in deep neural network-based receiver coil sensitivity map estimation

The success of parallel Magnetic Resonance Imaging algorithms like SENSitivity Encoding (SENSE) depends on an accurate estimation of the receiver coil sensitivity maps. Deep learning-based receiver coil sensitivity map estimation depends upon the size of training dataset and generalization capabilities of the trained neural network. When there is a mismatch between the training and testing datasets, retraining of the neural networks is required from a scratch which is costly and time consuming. A transfer learning approach, i.e., end-to-end fine-tuning is proposed to address the data scarcity and generalization problems of deep learning-based receiver coil sensitivity map estimation. First, generalization capabilities of a pre-trained U-Net (initially trained on 1.5T receiver coil sensitivity maps) are thoroughly assessed for 3T receiver coil sensitivity map estimation. Later, end-to-end fine-tuning is performed on the pre-trained U-Net to estimate the 3T receiver coil sensitivity maps. Peak Signal-to-Noise Ratio, Root Mean Square Error and central line profiles (of the SENSE reconstructed images) show a successful SENSE reconstruction by utilizing the receiver coil sensitivity maps estimated by the proposed method.

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

Magnetic resonance imaging (MRI) has the limitation of low imaging speed. Acceleration methods using under-sampled k-space data have been widely exploited to improve data acquisition without reducing the image quality. Sensitivity encoding (SENSE) is the most commonly used method for multi-channel imaging. However, SENSE has the drawback of severe g-factor artifacts when the under-sampling factor is high. This paper applies generative adversarial networks (GAN) to remove g-factor artifacts from SENSE reconstructions. Our method was evaluated on a public knee database containing 20 healthy participants. We compared our method with conventional GAN using zero-filled (ZF) images as input. Structural similarity (SSIM), peak signal to noise ratio (PSNR), and normalized mean square error (NMSE) were calculated for the assessment of image quality. A paired student's t-test was conducted to compare the image quality metrics between the different methods. Statistical significance was considered at P<0.01. The proposed method outperformed SENSE, variational network (VN), and ZF + GAN methods in terms of SSIM (SENSE + GAN: 0.81±0.06, SENSE: 0.40±0.07, VN: 0.79±0.06, ZF + GAN: 0.77±0.06), PSNR (SENSE + GAN: 31.90±1.66, SENSE: 22.70±1.99, VN: 31.35±2.01, ZF + GAN: 29.95±1.59), and NMSE (×10-7) (SENSE + GAN: 0.95±0.34, SENSE: 4.81±1.33, VN: 0.97±0.30, ZF + GAN: 1.60±0.84) with an under-sampling factor of up to 6-fold. This study demonstrated the feasibility of using GAN to improve the performance of SENSE reconstruction. The improvement of reconstruction is more obvious for higher under-sampling rates, which shows great potential for many clinical applications.

Performance Evaluation of New Nonlocal Total Variation Noise Reduction Algorithm in Parallel Magnetic Resonance Imaging with Sensitivity Encoding Reconstruction

Parallel magnetic resonance imaging (pMRI) can acquire high temporal resolution to obtain anatomical images. Among the parallel-imaging techniques, sensitivity encoding (SENSE) is the most widely used. During the SENSE process, they are previously limited by signal-to-noise ratio degradation and aliasing artifacts owing to the subsampling effect. Therefore, the objective of this study was to develop and evaluate a novel nonlocal total variation (new-NLTV) noise reduction algorithm in pMRI with SENSE reconstruction in both simulation and experiments. According to the results, the proposed algorithm was able to achieve impressive results using quantitative evaluation factors in simulation and real phantom images. The contrast-to-noise ratio and coefficient of variation for the algorithm, in particular, were 8.24 and 7.15 times better, respectively, than those of the noisy image in the phantom study. In conclusion, this study successfully demonstrated the effectiveness of the new-NLTV noise reduction algorithm in pMRI with SENSE reconstruction.

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

BackgroundOne 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).MethodsFirst, 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.ResultsBoth 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.ConclusionsThe 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.

Over-discretized SENSE reconstruction and B0 correction for accelerated non-lipid-suppressed 1 H FID MRSI of the human brain at 9.4 T.

The aim of this work was to use post-processing methods to improve the data quality of metabolite maps acquired on the human brain at 9.4T with accelerated acquisition schemes. This was accomplished by combining an improved sensitivity encoding (SENSE) reconstruction with a B0 correction of spatially over-discretized magnetic resonance spectroscopic imaging (MRSI) data. Since MRSI scans suffer from long scan duration, investigating different acceleration techniques has recently been the focus of several studies. Due to strong B0 inhomogeneity and strict specific absorption rate (SAR) limitations at ultra-high fields, the use of a low-SAR sequence combined with an acceleration technique that is compatible with dynamic B0 shim updating is preferable. Hence, in this study, a non-lipid-suppressed ultra-short TE and TR1 H free induction decay MRSI sequence is combined with an in-plane SENSE acceleration technique to obtain high-resolution metabolite maps in a clinically feasible scan time. One of the major issues in applying parallel imaging techniques to non-lipid-suppressed MRSI is the presence of strong lipid aliasing artifacts, which if not thoroughly resolved will hinder the accurate quantification of the metabolites of interest. To achieve a more robust reconstruction, an over-discretized SENSE reconstruction (with direct control over the shape of the spatial response function) was combined with an over-discretized B0 correction. This method is compared with conventional SENSE reconstruction for seven acceleration schemes on four healthy volunteers. The over-discretized method consistently outperformed conventional SENSE, resulting in an average of 23±1.2% higher signal-to-noise ratio and 8±2.9% less error in the fitting of the N-acetylaspartate signal over a whole brain slice. The highest achievable acceleration factor with the proposed technique was determined to be 4. Finally, using the over-discretized method, high-resolution (97μL nominal voxel size) metabolite maps can be acquired in 3.75min at 9.4T. This enables the acquisition of high-resolution metabolite maps with more spatial coverage at ultra-high fields.

Super Slice Interpolation For Generating Thin-Slice Images From Multichannel Multislice MRI Data.

This study aims to develop a super slice interpolation (SSI) method that generates thin-slice images from multichannel multislice images by exploiting the intra-slice coil sensitivity variations. SSI first calculates the thin-slice sensitivity maps by through-plane interpolation of the sensitivity maps computed from the acquired multislice images. It then reconstructs multiple thin-slice images from each acquired image using a through-plane regularized sensitivity encoding (SENSE) like procedure that consists of an initial SENSE reconstruction and denoising to set the prior information image, and subsequent regularized SENSE reconstruction. We evaluated SSI using multislice brain and abdominal images with typical slice thickness. SSI successfully separated each acquired image into two thinner ones without magnitude bias. Compared with the original thick-slice images, SSI revealed more anatomical details that were consistent with those in the separately acquired thin-slice images. SSI presents a novel slice interpolation approach to obtain thin-slice images from the multichannel thick-slice images.

QR-decomposition based SENSE reconstruction using parallel architecture

Magnetic Resonance Imaging (MRI) is a powerful medical imaging technique that provides essential clinical information about the human body. One major limitation of MRI is its long scan time. Implementation of advance MRI algorithms on a parallel architecture (to exploit inherent parallelism) has a great potential to reduce the scan time. Sensitivity Encoding (SENSE) is a Parallel Magnetic Resonance Imaging (pMRI) algorithm that utilizes receiver coil sensitivities to reconstruct MR images from the acquired under-sampled k-space data. At the heart of SENSE lies inversion of a rectangular encoding matrix. This work presents a novel implementation of GPU based SENSE algorithm, which employs QR decomposition for the inversion of the rectangular encoding matrix. For a fair comparison, the performance of the proposed GPU based SENSE reconstruction is evaluated against single and multicore CPU using openMP. Several experiments against various acceleration factors (AFs) are performed using multichannel (8, 12 and 30) phantom and in-vivo human head and cardiac datasets. Experimental results show that GPU significantly reduces the computation time of SENSE reconstruction as compared to multi-core CPU (approximately 12x speedup) and single-core CPU (approximately 53x speedup) without any degradation in the quality of the reconstructed images.

Optimizing Image Reconstruction in SENSE Using GPU

Parallel magnetic resonance imaging (MRI) (pMRI) uses multiple receiver coils to reduce the MRI scan time. To accelerate the data acquisition process in MRI, less amount of data is acquired from the scanner which leads to artifacts in the reconstructed images. SENSitivity Encoding (SENSE) is a reconstruction algorithm in pMRI to remove aliasing artifacts from the undersampled multi coil data and recovers fully sampled images. The main limitation of SENSE is computing inverse of the encoding matrix. This work proposes the inversion of encoding matrix using Jacobi singular value decomposition (SVD) algorithm for image reconstruction on GPUs to accelerate the reconstruction process. The performance of Jacobi SVD is compared with Gauss–Jordan algorithm. The simulations are performed on two datasets (brain and cardiac) with acceleration factors 2, 4, 6 and 8. The results show that the graphics processing unit (GPU) provides a speed up to 21.6 times as compared to CPU reconstruction. Jacobi SVD algorithm performs better in terms of acceleration in reconstructions on GPUs as compared to Gauss–Jordan method. The proposed algorithm is suitable for any number of coils and acceleration factors for SENSE reconstruction on real time processing systems.

FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities

Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10−2) and comparable artefact power (<9×10−4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.

Robust EPI Nyquist ghost removal by incorporating phase error correction with sensitivity encoding (PEC-SENSE).

The existing approach of Nyquist ghost correction by parallel imaging in echo planar imaging (EPI) can suffer from image noise amplification. We propose a method that estimates a phase error map from multi-channel data itself and incorporates it into the sensitivity encoding (SENSE) reconstruction for Nyquist ghost correction without compromising the image SNR. This method first reconstructs two ghost-free images from positive and negative echoes using SENSE, respectively, from which the phase error map is computed. This map is then incorporated into the coil sensitivity maps for the negative echo image during the joint SENSE reconstruction of all k-space data to obtain the final ghost-free image. Phantom and in vivo EPI experiments at 7 T and 3 T were performed to evaluate the proposed method. Nyquist ghost was effectively removed in all images even under oblique imaging and poor eddy current conditions. Resulting image signal-to-noise ratio (SNR) was comparable to that by the traditional linear phase error correction method and higher than that by a previous SENSE-based parallel imaging correction approach. The proposed correction method can robustly eliminate Nyquist ghost while preserving the image SNR. This approach requires no additional calibration data beyond standard coil sensitivity maps and can be readily applied to all EPI applications. Magn Reson Med 79:943-951, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Combining a reduced field of excitation with SENSE-based parallel imaging for maximum imaging efficiency.

PurposeTo show that a combination of parallel imaging using sensitivity encoding (SENSE) and inner volume imaging (IVI) combines the known benefits of both techniques. SENSE with a reduced field of excitation (rFOX) is termed rSENSE.Theory and MethodsThe noise level in SENSE reconstructions is reduced by removing voxels from the unfolding process that are rendered silent by using rFOX. The silent voxels need to be identified beforehand, this is done by using rFOX in the coil sensitivity maps. In vivo experiments were performed at 7 Tesla using a 32‐channel receive coil.ResultsGood image quality could be obtained in vivo with rSENSE at acceleration factors that are higher than could be obtained using SENSE or IVI alone. With rSENSE we were also able to accelerate scans using an rFOX that was purposely designed to be imperfect or incompatible at all with IVI.ConclusionrSENSE has been demonstrated in vivo with two‐dimensionally selective radiofrequency pulses. Besides allowing additional scan acceleration, it offers a greater robustness and flexibility than IVI. The proposed method can be used with other field strengths, anatomies and other rFOX techniques. Magn Reson Med 78:88–96, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution Non Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Hybrid-Space SENSE Reconstruction for Simultaneous Multi-Slice MRI.

Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

Accelerating chemical exchange saturation transfer (CEST) MRI by combining compressed sensing and sensitivity encoding techniques.

To evaluate the feasibility of accelerated chemical-exchange-saturation-transfer (CEST) imaging using a combination of compressed sensing (CS) and sensitivity encoding (SENSE) at 3 Tesla. Two healthy volunteers and six high-grade glioma patients were recruited. Raw CEST image k-space data were acquired (with varied radiofrequency saturation power levels for the healthy volunteer study), and a sequential CS and SENSE reconstruction (CS-SENSE) was assessed. The MTRasym (3.5 ppm) signals were compared with varied CS-SENSE acceleration factors. In the healthy volunteer study, a CS-SENSE acceleration factor of R = 2 × 2 (CS × SENSE) was achieved without compromising the reconstructed MTRasym (3.5 ppm) image quality. The MTRasym (3.5 ppm) signals obtained from the CS-SENSE reconstruction with R = 2 × 2 were well preserved compared with the reference image (R = 2 for only SENSE). In the glioma patient study, the MTRasym (3.5 ppm) signals were significantly higher in the tumor region (Gd-enhancing tumor core) than in the normal-appearing white matter (P < 0.001). There was no significant MTRasym (3.5 ppm) difference between the reference image and CS-SENSE-reconstructed image in the acceleration factor of R = 2 × 2. Combining the SENSE technique with CS (R = 2 × 2) enables considerable acceleration of CEST image acquisition and potentially has a wide range of clinical applications. Magn Reson Med 77:779-786, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

The Need and Initial Practice of Parallel Imaging and Compressed Sensing in Hyperpolarized 13C MRI in vivo.

The Need and Initial Practice of Parallel Imaging and Compressed Sensing in Hyperpolarized 13C MRI in vivo.

Slab profile encoding (PEN) for minimizing slab boundary artifact in three‐dimensional diffusion‐weighted multislab acquisition

To propose a method for mitigating slab boundary artifacts in three-dimensional (3D) multislab diffusion imaging with no or minimal increases in scan time. The multislab acquisition was treated as parallel imaging acquisition where the slab profiles acted as the traditional receiver sensitivity profiles. All the slabs were then reconstructed simultaneously along the slab direction using Cartesian-based sensitivity encoding (SENSE) reconstruction. The slab profile estimation was performed using either a Bloch simulation or a calibration scan. Both phantom and in vivo results showed negligible slab boundary artifacts after reconstruction using the proposed method. The performance of the proposed method is comparable to the state-of-the-art slab combination method without the scan time penalty that depends on the number of acquired volumes. The obtained g-factor map of the SENSE reconstruction problem showed a maximum g-factor of 1.7 in the region of interest. We proposed a novel method for mitigating slab boundary artifacts in 3D diffusion imaging by treating the multislab acquisition as a parallel imaging acquisition and reconstructing all slabs simultaneously using Cartesian SENSE. Unlike existing methods, the scan time increase, if any, does not scale with the number of image volumes acquired.

Reference Scanの位置における画質の検討

In magnetic resonance imaging (MRI), we can use sensitivity encoding (SENSE) as a parallel imaging reconstruct from aliasing based on the sensitivity map of each coil element by reference scan. We researched SENSE reconstruction depending on the position of the reference scan. 1.5 T MRI (Achiva, Philips), 32-channel SENSE cardiac coil and rectangular solid phantom (nickel chloride) were used. Seven slices (A to G: from head to foot) were set at the phantom, slice thickness was 1 cm, and each slice interval was 5 cm. When T2 weighted axial images (TR: 3000 ms, TE: 120 ms) were taken, the center of the reference scan (C-Ref) was changed A to G, SENSE factor was changed 1 to 5, and number of signals averaged (NSA) was changed 1 to 3 with fold over suppression. Signal-to-noise ratio (SNR) and coefficient of variation (CV) from 9 regions of interest (ROI) of axial images were calculated. The SNR of slices A and G were lower than the other slices. %CV of slices F with C-Ref D was 69.4% lower than C-Ref A. %CV of slices D to F were higher than the other slices when C-Ref was A. %CV had no relation with SNR. Therefore, a 30 cm area around the center of reference can obtain best SENSE reconstruction.