Joint reconstruction of multiecho MR images using correlated sparsity

This works addresses the problem of reconstructing multiple T1- or T2-weighted images of the same anatomical cross section from partially sampled K-space data. Previous studies in reconstructing magnetic resonance (MR) images from partial samples of the K-space used compressed sensing (CS) techniques to exploit the spatial correlation of the images (leading to sparsity in wavelet domain). Such techniques can be employed to reconstruct the individual T1- or T2-weighted images. However, in the current context, the different images are not really independent; they are images of the same cross section and, hence, are highly correlated. We exploit the correlation between the images, along with the spatial correlation within the images to achieve better reconstruction results than exploiting spatial correlation only.For individual MR images, CS-based techniques lead to a sparsity-promoting optimization problem in the wavelet domain. In this article, we show that the same framework can be extended to incorporate correlation between images leading to group/row sparsity-promoting optimization. Algorithms for solving such optimization problems have already been developed in the CS literature. We show that significant improvement in reconstruction accuracy can be achieved by considering the correlation between different T1- and T2-weighted images. If the reconstruction accuracy is considered to be constant, our proposed group sparse formulation can yield the same result with 33% less K-space samples compared with simple sparsity-promoting reconstruction. Moreover, the reconstruction time by our proposed method is about two to four times less than the previous method.     

Exploiting the wavelet structure in compressed sensing MRI

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms.     

Sparsity-constrained SENSE reconstruction: An efficient implementation using a fast composite splitting algorithm

Parallel imaging and compressed sensing have been arguably the most successful and widely used techniques for fast magnetic resonance imaging (MRI). Recent studies have shown that the combination of these two techniques is useful for solving the inverse problem of recovering the image from highly under-sampled k-space data. In sparsity-enforced sensitivity encoding (SENSE) reconstruction, the optimization problem involves data fidelity (L2-norm) constraint and a number of L1-norm regularization terms (i.e. total variation or TV, and L1 norm). This makes the optimization problem difficult to solve due to the non-smooth nature of the regularization terms. In this paper, to effectively solve the sparsity-regularized SENSE reconstruction, we utilize a new optimization method, called fast composite splitting algorithm (FCSA), which was developed for compressed sensing MRI. By using a combination of variable splitting and operator splitting techniques, the FCSA algorithm decouples the large optimization problem into TV and L1 sub-problems, which are then, solved efficiently using existing fast methods. The operator splitting separates the smooth terms from the non-smooth terms, so that both terms are treated in an efficient manner. The final solution to the SENSE reconstruction is obtained by weighted solutions to the sub-problems through an iterative optimization procedure. The FCSA-based parallel MRI technique is tested on MR brain image reconstructions at various acceleration rates and with different sampling trajectories. The results indicate that, for sparsity-regularized SENSE reconstruction, the FCSA-based method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction method.     

Wavelet-based edge correlation incorporated iterative reconstruction for undersampled MRI

Undersampling k-space is an effective way to decrease acquisition time for MRI. However, aliasing artifacts introduced by undersampling may blur the edges of magnetic resonance images, which often contain important information for clinical diagnosis. Moreover, k-space data is often contaminated by the noise signals of unknown intensity. To better preserve the edge features while suppressing the aliasing artifacts and noises, we present a new wavelet-based algorithm for undersampled MRI reconstruction. The algorithm solves the image reconstruction as a standard optimization problem including a ?₂ data fidelity term and ?₁ sparsity regularization term. Rather than manually setting the regularization parameter for the ?₁ term, which is directly related to the threshold, an automatic estimated threshold adaptive to noise intensity is introduced in our proposed algorithm. In addition, a prior matrix based on edge correlation in wavelet domain is incorporated into the regularization term. Compared with nonlinear conjugate gradient descent algorithm, iterative shrinkage/thresholding algorithm, fast iterative soft-thresholding algorithm and the iterative thresholding algorithm using exponentially decreasing threshold, the proposed algorithm yields reconstructions with better edge recovery and noise suppression.     

Joint Reconstruction of Multi-contrast Images and Multi-channel Coil Sensitivities

Magnetic resonance imaging (MRI) has an important feature that it provides multiple images with different contrasts for complementary diagnostic information. However, a large amount of data is needed for multi-contrast images depiction, and thus, the scan is time-consuming. Many methods based on parallel magnetic resonance imaging (pMRI) and compressed sensing (CS) are applied to accelerate multi-contrast MR imaging. Nevertheless, the image reconstructed by sophisticated pMRI methods contains residual aliasing artifact that degrades the quality of the image when the acceleration factor is high. Other methods based on CS always suffer the regularization parameter-selecting problem. To address these issues, a new method is presented for joint multi-contrast image reconstruction and coil sensitivity estimation. The coil sensitivities can be shared during the reconstruction due to the identity of coil sensitivity profiles of different contrast images for imaging stationary tissues. The proposed method uses the coil sensitivities as sharable information during the reconstruction to improve the reconstruction quality. As a result, the residual aliasing artifact can be effectively removed in the reconstructed multi-contrast images even if the acceleration factor is high. Besides, as there is no regularization term in the proposed method, the troublesome regularization parameter selection in the CS can also be avoided. Results from multi-contrast in vivo experiments demonstrated that multi-contrast images can be jointly reconstructed by the proposed method with effective removal of the residual aliasing artifact at a high acceleration factor.     

Automatic knee cartilage segmentation from multi-contrast MR images using support vector machine classification with spatial dependencies

Accurate segmentation of knee cartilage is required to obtain quantitative cartilage measurements, which is crucial for the assessment of knee pathology caused by musculoskeletal diseases or sudden injuries. This paper presents an automatic knee cartilage segmentation technique which exploits a rich set of image features from multi-contrast magnetic resonance (MR) images and the spatial dependencies between neighbouring voxels. The image features and the spatial dependencies are modelled into a support vector machine (SVM)-based association potential and a discriminative random field (DRF)-based interaction potential. Subsequently, both potentials are incorporated into an inference graphical model such that the knee cartilage segmentation is cast into an optimal labelling problem which can be efficiently solved by loopy belief propagation. The effectiveness of the proposed technique is validated on a database of multi-contrast MR images. The experimental results show that using diverse forms of image and anatomical structure information as the features are helpful in improving the segmentation, and the joint SVM-DRF model is superior to the classification models based solely on DRF or SVM in terms of accuracy when the same features are used. The developed segmentation technique achieves good performance compared with gold standard segmentations and obtained higher average DSC values than the state-of-the-art automatic cartilage segmentation studies.     

Methods for quantitative image quality evaluation of MRI parallel reconstructions: detection and perceptual difference model

Many reconstruction algorithms are being proposed for parallel magnetic resonance imaging (MRI), which uses multiple coils and subsampled k-space data, and a quantitative method for comparison of algorithms is sorely needed. On such images, we compared three methods for quantitative image quality evaluation: human detection, computer detection model and a computer perceptual difference model (PDM). One-quarter sampling and three different reconstruction methods were investigated: a regularization method developed by Ying et al., a simplified regularization method and an iterative method proposed by Pruessmann et al. Images obtained from a full complement of k-space data were also included as reference images. Detection studies were performed using a simulated dark tumor added on MR images of fresh bovine liver. Human detection depended strongly on reconstruction methods used, with the two regularization methods achieving better performance than the iterative method. Images were also evaluated using detection by a channelized Hotelling observer model and by PDM scores. Both predicted the same trends as observed from human detection. We are encouraged that PDM gives trends similar to that for human detection studies. Its ease of use and applicability to a variety of MRI situations make it attractive for evaluating image quality in a variety of MR studies.     

Calibration-less multi-coil MR image reconstruction

In parallel magnetic resonance imaging (MRI), the problem is to reconstruct an image given the partial K-space scans from all the receiver coils. Depending on its position within the scanner, each coil has a different sensitivity profile. All existing parallel MRI techniques require estimation of certain parameters pertaining to the sensitivity profile, e.g., the sensitivity map needs to be estimated for the SENSE and SMASH and the interpolation weights need to be calibrated for GRAPPA and SPIRiT. The assumption is that the estimated parameters are applicable at the operational stage. This assumption does not always hold, consequently the reconstruction accuracies of existing parallel MRI methods may suffer. We propose a reconstruction method called Calibration-Less Multi-coil (CaLM) MRI. As the name suggests, our method does not require estimation of any parameters related to the sensitivity maps and hence does not require a calibration stage. CaLM MRI is an image domain method that produces a sensitivity encoded image for each coil. These images are finally combined by the sum-of-squares method to yield the final image. It is based on the theory of Compressed Sensing (CS). During reconstruction, the constraint that "all the coil images should appear similar" is introduced within the CS framework. This leads to a CS optimization problem that promotes group-sparsity. The results from our proposed method are comparable (at least for the data used in this work) with the best results that can be obtained from state-of-the-art methods.     

Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images

A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm²) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods.     

Image domain propeller fast spin echo

A new pulse sequence for high-resolution T2-weighted (T2-w) imaging is proposed — image domain propeller fast spin echo (iProp-FSE). Similar to the T2-w PROPELLER sequence, iProp-FSE acquires data in a segmented fashion, as blades that are acquired in multiple TRs. However, the iProp-FSE blades are formed in the image domain instead of in the k-space domain. Each iProp-FSE blade resembles a single-shot fast spin echo (SSFSE) sequence with a very narrow phase-encoding field of view (FOV), after which N rotated blade replicas yield the final full circular FOV. Our method of combining the image domain blade data to a full FOV image is detailed, and optimal choices of phase-encoding FOVs and receiver bandwidths were evaluated on phantom and volunteers. The results suggest that a phase FOV of 15–20%, a receiver bandwidth of ± 32–63 kHz and a subsequent readout time of about 300 ms provide a good tradeoff between signal-to-noise ratio (SNR) efficiency and T2 blurring. Comparisons between iProp-FSE, Cartesian FSE and PROPELLER were made on single-slice axial brain data, showing similar T2-w tissue contrast and SNR with great anatomical conspicuity at similar scan times — without colored noise or streaks from motion. A new slice interleaving order is also proposed to improve the multislice capabilities of iProp-FSE.     

大鼠眼球的高场磁共振成像研究

该文旨在研究微型磁共振（MRI）对大鼠眼球的成像效果和应用. 通过对10只SD大鼠的20只眼球进行7.0 T MRI检查,应用常规T1WI和T2WI序列高分辨率扫描;观察MRI图像上大鼠眼球的结构,并比较MRI测量与组织学显微镜下测量视网膜厚度结果. 磁共振扫描清楚地显示了所有受试大鼠眼球的主要结构,包括角膜、晶状体、玻璃体、视网膜、巩膜、虹膜、睫状体、视神经. 球壁结构磁共振图像层次与组织学结构层次有良好对应性; 磁共振视网膜厚度测量值与显微镜下视网膜厚度测量数据进行配对 t 检验,P＞0.05,二者无显著差异. 由此得出的结论是小动物MRI可以对大鼠眼球细微解剖结构进行无创性的成像,为我们提供了一个研究大鼠眼科疾病模型的形态学及功能变化的手段.      

Chemical exchange saturation transfer MRI using intermolecular double-quantum coherences with multiple refocusing pulses

Chemical exchange saturation transfer (CEST) provides a new type of image contrast in MRI. Due to the intrinsically low CEST effect, new and improved experimental techniques are required to achieve reliable and quantitative CEST images. In the present work, we proposed a novel and more sensitive CEST acquisition approach, based on the intermolecular double-quantum coherence with a module of multiple refocusing pulses (iDQC-MRP). Experiments were performed on creatine and egg white phantoms using a Varian 7 T animal MRI scanner. The iDQC-MRP CEST technique showed a substantial enhancement in CEST and nuclear Overhauser enhancement (NOE) signal intensities, compared to the standard single-quantum coherence approach. In addition, the iDQC-MRP approach increased the signal-to-noise ratio of acquired saturation images, compared to the conventional iDQC approach. The new iDQC-MRP CEST sequence provides a promising way for exploiting in vivo CEST and NOE imaging applications.     

Causal dynamic MRI reconstruction via nuclear norm minimization

This work addresses the problem of online reconstruction of dynamic magnetic resonance images (MRI). The proposed method reconstructs the difference between the images of previous and current time frames. This difference image is modeled as a rank deficient matrix and is solved from the partially sampled k-space data via nuclear norm minimization. Our proposed method has been compared against state-of-the-art offline and online reconstruction methods. Our method has similar reconstruction accuracy as the offline method and significantly higher accuracy compared to the online technique. It is about an order of magnitude faster than the online technique compared against. Our experimental data consisted of dynamic MRI data that were collected at 6 to 7 frames per second and having resolutions of 128×128 and 256×256 pixels per frame. Experimental evaluation indicates that our proposed method is capable of reconstructing 128×128 images at the rate of 4 frames per second and 256×256 images at the rate of 2 frames per second. The previous online method requires about 3.75 s for reconstructing each image. The improvement in reconstruction speed is clearly discernible. Moreover, our method has a reconstruction error that is about half that of the previous online method.     

Fast single image super-resolution using estimated low-frequency k-space data in MRI

PurposeSingle image super-resolution (SR) is highly desired in many fields but obtaining it is often technically limited in practice. The purpose of this study was to propose a simple, rapid and robust single image SR method in magnetic resonance (MR) imaging (MRI).MethodsThe idea is based on the mathematical formulation of the intrinsic link in k-space between a given (modulus) low-resolution (LR) image and the desired SR image. The method consists of two steps: 1) estimating the low-frequency k-space data of the desired SR image from a single LR image; 2) reconstructing the SR image using the estimated low-frequency and zero-filled high-frequency k-space data. The method was evaluated on digital phantom images, physical phantom MR images and real brain MR images, and compared with existing SR methods.ResultsThe proposed SR method exhibited a good robustness by reaching a clearly higher PSNR (25.77dB) and SSIM (0.991) averaged over different noise levels in comparison with existing edge-guided nonlinear interpolation (EGNI) (PSNR=23.78dB, SSIM=0.983), zero-filling (ZF) (PSNR=24.09dB, SSIM=0.985) and total variation (TV) (PSNR=24.54dB, SSIM=0.987) methods while presenting the same order of computation time as the ZF method but being much faster than the EGNI or TV method. The average PSNR or SSIM over different slice images of the proposed method (PSNR=26.33 dB or SSIM=0.955) was also higher than the EGNI (PSNR=25.07dB or SSIM=0.952), ZF (PSNR=24.97dB or SSIM=0.950) and TV (PSNR=25.70dB or SSIM=0.953) methods, demonstrating its good robustness to variation in anatomical structure of the images. Meanwhile, the proposed method always produced less ringing artifacts than the ZF method, gave a clearer image than the EGNI method, and did not exhibit any blocking effect presented in the TV method. In addition, the proposed method yielded the highest spatial consistency in the inter-slice dimension among the four methods.ConclusionsThis study proposed a fast, robust and efficient single image SR method with high spatial consistency in the inter-slice dimension for clinical MR images by estimating the low-frequency k-space data of the desired SR image from a single spatial modulus LR image.     

A robust algorithm for high-resolution dynamic MRI based on the partially separable functions model

A recently developed partially separable functions (PSF) model can be used to generate high-resolution dynamic magnetic resonance imaging (MRI). However, this method could not robustly reconstruct high-quality MR images because the estimation of the PSF parameters is often interfered by the noise of the sampled MR data. To improve the robustness of MRI reconstruction using the PSF model, we proposed a new algorithm to estimate the PSF parameters by jointly using robust principal component analysis and modified truncated singular value decomposition regularization methods, instead of using the least square fitting method in the original PSF model. The experiment results of in vivo cardiac MRI demonstrated that the proposed algorithm can robustly reconstruct dynamic MR images with higher signal-to-noise ratio and clearer anatomical structures in comparison with the previous PSF model.     

Hepatocellular lesions with increased iron uptake on superparamagnetic iron oxide-enhanced magnetic resonance imaging in cirrhosis or chronic hepatitis: comparison of four magnetic resonance sequences for lesion conspicuity

Purpose

The aim of this study was to determine the adequate MR sequence for the lesion conspicuity of hepatocellular lesions with increased iron uptake on superparamagnetic iron oxide (SPIO)-enhanced MRI.

Materials and Methods

SPIO-enhanced MRI was performed using a 1.5-T system. Among 25 patients with hypovascular hepatocellular nodules on contrast-enhanced dynamic CT (no early enhancement at arterial phase and hypoattenuation at equilibrium phase), 39 lesions with increased iron uptake on SPIO-enhanced MRI were evaluated. SPIO-enhanced MRI included (1) T1-weighted in-phase gradient recalled echo (GRE) images, (2) T2-weighted fast spin echo (FSE) images, (3) T2*-weighted GRE with moderate TE (7 ms) and (4) long TE (12 ms). The lesion-to-liver contrast-to-noise ratios of the hepatocellular nodule and the signal-to-noise ratio (SNR) of the hepatic parenchyma were calculated by one radiologist for a quantitative assessment. MR images were reviewed retrospectively by two independent radiologists to compare the subjective lesion conspicuity in each image set based on a four-point rating scale.

Result

The mean lesion-to-liver contrast-to-noise ratios with T2*-weighted GRE with moderate TE (7 ms) was highest (5.79±3.71) and was significantly higher than those with T1-weighted, in-phase images (3.79±3.23, P<.01), T2-weighted images (2.72±1.52, P<.001) and T2*-weighted GRE with long TE (12 ms) (3.93±2.69, P<.05). The subjective rating of lesion conspicuity was best on the T2*-weighted GRE with moderate TE (7 ms), followed by that on the T2*-weighted GRE with moderate TE (7 ms; P<.05).

Conclusion

T2*-weighted GRE sequence with moderate TE (7 ms) showed high lesion-to-liver contrast-to-noise ratios in hepatocellular lesions with increased iron uptake on SPIO-enhanced MRI, indicating better lesion conspicuity of hypointense hepatocellular nodules in cirrhosis or chronic hepatitis.     

Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography

Linear scan computed tomography (LCT) is of great benefit to online industrial scanning and security inspection due to its characteristics of straight-line source trajectory and high scanning speed. However, in practical applications of LCT, there are challenges to image reconstruction due to limited-angle and insufficient data. In this paper, a new reconstruction algorithm based on total-variation (TV) minimization is developed to reconstruct images from limited-angle and insufficient data in LCT. The main idea of our approach is to reformulate a TV problem as a linear equality constrained problem where the objective function is separable, and then minimize its augmented Lagrangian function by using alternating direction method (ADM) to solve subproblems. The proposed method is robust and efficient in the task of reconstruction by showing the convergence of ADM. The numerical simulations and real data reconstructions show that the proposed reconstruction method brings reasonable performance and outperforms some previous ones when applied to an LCT imaging problem.     

基于广义变分正则化的闪光照相图像重建算法

 针对闪光照相图像信噪比低的特点,提出了一种基于广义变分正则化的图像重建算法,该方法采用p-范数取代目前广泛采用的全变分范数作为正则项,构造了用于图像重建的展平泛函,将图像重建问题转化为目标泛函最优化问题,采用固定点迭代法求解图像重建的最优解。数值计算结果表明,该算法在重建过程中能够有效抑制图像噪声,并加大对图像边缘的保持能力,从而提高了图像重建质量,是一种有效且性能优良的闪光照相图像重建算法。     

In vivo hypoxia characterization using blood oxygen level dependent magnetic resonance imaging in a preclinical glioblastoma mouse model

PurposeHypoxia measurements can provide crucial information regarding tumor aggressiveness, however current preclinical approaches are limited. Blood oxygen level dependent (BOLD) Magnetic Resonance Imaging (MRI) has the potential to continuously monitor tumor pathophysiology (including hypoxia). The aim of this preliminary work was to develop and evaluate BOLD MRI followed by post-image analysis to identify regions of hypoxia in a murine glioblastoma (GBM) model.MethodsA murine orthotopic GBM model (GL261-luc2) was used and independent images were generated from multiple slices in four different mice. Image slices were randomized and split into training and validation cohorts. A 7 T MRI was used to acquire anatomical images using a fast-spin-echo (FSE) T2-weighted sequence. BOLD images were taken with a T2*-weighted gradient echo (GRE) and an oxygen challenge. Thirteen images were evaluated in a training cohort to develop the MRI sequence and optimize post-image analysis. An in-house MATLAB code was used to evaluate MR images and generate hypoxia maps for a range of thresholding and ΔT2* values, which were compared against respective pimonidazole sections to optimize image processing parameters. The remaining (n = 6) images were used as a validation group. Following imaging, mice were injected with pimonidazole and collected for immunohistochemistry (IHC). A test of correlation (Pearson's coefficient) and agreement (Bland-Altman plot) were conducted to evaluate the respective MRI slices and pimonidazole IHC sections.ResultsFor the training cohort, the optimized parameters of “thresholding” (20 ≤ T2* ≤ 35 ms) and ΔT2* (±4 ms) yielded a Pearson's correlation of 0.697. These parameters were applied to the validation cohort confirming a strong Pearson's correlation (0.749) when comparing the respective analyzed MR and pimonidazole images.ConclusionOur preliminary study supports the hypothesis that BOLD MRI is correlated with pimonidazole measurements of hypoxia in an orthotopic GBM mouse model. This technique has further potential to monitor hypoxia during tumor development and therapy.     

Optimization of hyperparameters for SMS reconstruction

PurposeSimultaneous multi-slice (SMS) imaging accelerates MRI data acquisition by exciting multiple image slices with a single radiofrequency pulse. Overlapping slices encoded in acquired signal are separated using a mathematical model, which requires estimation of image reconstruction kernels using calibration data. Several parameters used in SMS reconstruction impact the quality and fidelity of final images. Therefore, finding an optimal set of reconstruction parameters is critical to ensure that accelerated acquisition does not significantly degrade resulting image quality.MethodsGradient-echo echo planar imaging data were acquired with a range of SMS acceleration factors from a cohort of five volunteers with no known neurological pathology. Images were collected using two available phased-array head coils (a 48-channel array and a reduced diameter 32-channel array) that support SMS. Data from these coils were identically reconstructed offline using a range of coil compression factors and reconstruction kernel parameters. A hybrid space (k-x), externally-calibrated coil-by-coil slice unaliasing approach was used for image reconstruction. The image quality of the resulting reconstructed SMS images was assessed by evaluating correlations with identical echo-planar reference data acquired without SMS. A finger tapping functional MRI (fMRI) experiment was also performed and group analysis results were compared between data sets reconstructed with different coil compression levels.ResultsBetween the two RF coils tested in this study, the 32-channel coil with smaller dimensions clearly outperformed the larger 48-channel coil in our experiments. Generally, a large calibration region (144–192 samples) and small kernel sizes (2–4 samples) in k_y direction improved image quality. Use of regularization in the kernel fitting procedure had a notable impact on the fidelity of reconstructed images and a regularization value 0.0001 provided good image quality. With optimal selection of other hyperparameters in the hybrid space SMS unaliasing algorithm, coil compression caused small reduction in correlation between single-band and SMS unaliased images. Similarly, group analysis of fMRI results did not show a significant influence of coil compression on resulting image quality.ConclusionsThis study demonstrated that the hyperparameters used in SMS reconstruction need to be fine-tuned once the experimental factors such as the RF receive coil and SMS factor have been determined. A cursory evaluation of SMS reconstruction hyperparameter values is therefore recommended before conducting a full-scale quantitative study using SMS technologies.