Convolutional Autoencoder Effect on Parallel Magnetic Resonance Imaging

In parallel magnetic resonance imaging (MRI), the quality of image reconstruction is very important. The signal to noise ratio (SNR) of reconstruction image would be obviously reduced with the increase of the acceleration factors because of the ill-posed problem in the sensitivity encoding (SENSE) reconstruction. Through in-depth analysis of the total variation (TV) regularized SENSE reconstruction model, an efficient Bregman iteration algorithm was introduced to obtain the optimal solution and improve the image reconstruction quality. In this paper, the simulation experiments were performed on the real cardiac data and brain data of MRI, respectively. The experimental results demonstrated that the proposed algorithm can alleviate the aliasing artifacts and has a better image reconstruction effect. Compared with the conventional TV regularized SENSE reconstruction algorithm, under the condition of the higher acceleration factors, the SNR has also been improved and the normalized mean squared error (NMSE) of reconstruction image is also decreased. This shows that the proposed iteration TV SENSE image reconstruction algorithm is effective and feasible.

The application of parallel magnetic resonance (MR) imaging is increasing as clinicians continue to strive for improved spatial and temporal resolution, benefits that arise from the use of fewer phase encodings during imaging. To reconstruct images, extra information is needed to map the spatial sensitivity of each coil element, which may be accomplished by acquiring a calibration image in one common implementation of parallel MR imaging. Although obtaining a quick calibration image is an efficient method for gathering this information, corruption of the image or disharmony with subsequent images may lead to errors in reconstruction. Although conventional MR imaging sequences may be employed with parallel MR imaging, the altered image reconstruction introduces several new artifacts and changes the appearance of conventional artifacts. The altered appearance of traditional artifacts may obscure the source of the problem, and, in some cases, the severity of artifacts associated with parallel MR imaging may be exacerbated, hindering image interpretation. Several artifacts arise in the context of parallel MR imaging, including both traditional artifacts and those associated with parallel MR imaging.

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.

Both parallel Magnetic Resonance Imaging (pMRI) and Compressed Sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data in the k-space. So far, first attempts to combine sensitivity encoding (SENSE) imaging in pMRI with CS have been proposed in the context of Cartesian trajectories. Here, we extend these approaches to non-Cartesian trajectories by jointly formulating the CS and SENSE recovery in a hybrid Fourier/wavelet framework and optimizing a convex but nons-mooth criterion. On anatomical MRI data, we show that HYR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PICS outperforms wavelet-based regularized SENSE reconstruction. Our results are also in agreement with the Transform Point Spread Function (TPSF) criterion that measures the degree of incoherence of k-space undersampling schemes.

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

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

Parallel magnetic resonance imaging (MRI) techniques use spatial information from arrays of radiofrequency (RF) detector coils to accelerate imaging. A number of parallel MRI techniques have been described in recent years, and numerous clinical applications are currently being explored. The advent of practical parallel imaging presents various challenges for image reconstruction and RF system design. Recent advances in tailored SiMultaneous Acquisition of Spatial Harmonics (SMASH) image reconstructions are summarized. These advances enable robust SMASH imaging in arbitrary image planes with a wide range of coil array geometries. A generalized formalism is described which may be used to understand the relations between SMASH and SENSE, to derive typical implementations of each as special cases, and to form hybrid techniques combining some of the advantages of both. Accurate knowledge of coil sensitivities is crucial for parallel MRI, and errors in calibration represent one of the most common and the most pernicious sources of error in parallel image reconstructions. As one example, motion of the patient and/or the coil array between the sensitivity reference scan and the accelerated acquisition can lead to calibration errors and reconstruction artifacts. Self-calibrating parallel MRI approaches that address this problem by eliminating the need for external sensitivity references are reviewed. The ultimate achievable signal-to-noise ratio (SNR) for parallel MRI studies is closely tied to the geometry and sensitivity patterns of the coil arrays used for spatial encoding. Several parallel imaging array designs that depart from the traditional model of overlapped adjacent loop elements are described.

The application of partially parallel imaging techniques to regular clinical MRI studies has brought about the benefit of significantly faster acquisitions but at the cost of amplified and non-uniformly distributed noise, especially, for high acceleration factors. In this work, denoising of the images reconstructed by the sensitivity- encoding (SENSE) algorithm is presented. To efficiently remove noise and simultaneously preserve the image details, weighted total variation smoothing with weighted edge restoration has been developed. Denoising is guided by the g-factor of the SENSE reconstruction and image gradient that is representative of edge information. An automatic iteration termination scheme is proposed to balance denoising and edge preservation, and reduce the difficulty of parameter decision in conventional approaches. The proposed g-factor and gradient weighted denoising with edge restoration (g-DENOISER) method retains the image details better than the denoising techniques guided by only the g-factor and simultaneously suppresses the noise stronger than the techniques where g-factor was not used to adaptively adjust the denoiser parameters according to local noise characteristics. It can be effectively used to reduce noise in images acquired using high acceleration factors and reconstructed using SENSE.

To evaluate partially parallel three-dimensional (3D) magnetic resonance (MR) imaging for assessment of regional lung perfusion in healthy volunteers and patients suspected of having lung cancer or metastasis. Seven healthy volunteers and 20 patients suspected of having lung cancer or metastasis were examined with 3D gradient-echo MR imaging with partially parallel image acquisitions (fast low-angle shot 3D imaging; repetition time msec/echo time msec, 1.9/0.8; flip angle, 40 degrees; acceleration factor, two; number of reference k-space lines for calibration, 24; field of view, 500 x 440 mm; matrix, 256 x 123; slab thickness, 160 mm; number of partitions, 32; voxel size, 3.6 x 2.0 x 5.0 mm(3); acquisition time, 1.5 seconds) after administration of 0.1 mmol/kg of gadobenate dimeglumine. In volunteers, 3D MR perfusion data sets were assessed for topographic and temporal distribution of regional lung perfusion. Sensitivity, specificity, accuracy, and positive and negative predictive values for perfusion MR imaging for detecting perfusion abnormalities in patients were calculated, with conventional radionuclide perfusion scintigraphy as the standard of reference. Interobserver and intermodality agreement was determined by using kappa statistics. Topographic analysis of lung perfusion in volunteers revealed a significantly higher signal-to-noise ratio (SNR) of up to 327% in gravity-dependent lung areas. Temporal analysis similarly revealed much shorter lag time to peak enhancement in gravity-dependent lung areas. In patients, perfusion MR imaging achieved high sensitivity (88%-94%), specificity (100%), and accuracy (90%-95%) for detection of perfusion abnormalities. Interobserver agreement (kappa = 0.86) was very good and intermodality agreement (kappa = 0.69-0.83) was good to very good for detection of perfusion defects. A significant difference (P <.0001) in SNR was observed between normally perfused lung (14 +/- 7 [SD]) and perfusion defects (7 +/- 4) in patients. Partially parallel MR imaging with high spatial and temporal resolution allows assessment of regional lung perfusion and has high diagnostic accuracy for detecting perfusion abnormalities.

Fast acquisition in magnetic resonance imaging (MRI) is considered in this paper. Often, fast acquisition is achieved using parallel imaging (pMRI) techniques. It has been shown recently that the combination of pMRI and compressed sensing (CS), which enables exact reconstruction of sparse or compressible signals from a small number of random measurements, can accelerate the speed of MRI acquisition because the number of measurements are much smaller than that by pMRI per se. Also recently in CS, chaos filters were designed to obtain chaotic measurements. This chaotic CS approach potentially offers simpler hardware implementation. In this paper, we combine chaotic CS and pMRI. However, instead of using chaos filters, the measurements are obtained by chaotically undersampling the k-space. MRI image reconstruction is then performed by using nonlinear conjugate gradient optimization. For pMRI, we use the well-known approach SENSE - sensitivity encoding -, which requires an estimation of the sensitivity maps. The performance of the proposed method is analyzed using the point spread function, the transform point spread function, and the reconstruction error measure.

Functional magnetic resonance imaging (fMRI) and particularly resting state fMRI (rs-fMRI) is widely used to investigate resting state brain networks (RSNs) on the systems level. Echo planar imaging (EPI) is the state-of-the-art imaging technique for most fMRI studies. Therefore, improvements of EPI might lead to increased sensitivity for a large amount of studies performed every day. A number of developments to shorten acquisition time have been recently proposed and the multiband technique, allowing the simultaneous acquisition of multiple slices yielding an equivalent reduction of measurement time, is the most promising among them. While the prospect to significantly reduce acquisition time by means of high multiband acceleration factors (M) appears tempting, signal quality parameters and the sensitivity to detect common RSNs with increasing M-factor have only been partially investigated up to now. In this study, we therefore acquired rs-fMRI data from 20 healthy volunteers to systematically investigate signal characteristics and sensitivity for brain network activity in datasets with increasing M-factor, M = 2 − 4. Combined with an inplane, sensitivity encoding (SENSE), acceleration factor, S = 2, we applied a maximal acceleration factor of 8 (S2×M4). Our results suggest that an M-factor of 2 (total acceleration of 4) only causes negligible SNR decrease but reveals common RSN with increased sensitivity and stability. Further M-factor increase produced random artifacts as revealed by signal quality measures that may affect interpretation of RSNs under common scanning conditions. Given appropriate hardware, a mb-EPI sequence with a total acceleration of 4 significantly reduces overall scanning time and clearly increases sensitivity to detect common RSNs. Together, our results suggest mb-EPI at moderate acceleration factors as a novel standard for fMRI that might increase our understanding of network dynamics in healthy and diseased brains.

Integrated variable projection approach (IVAPA) for parallel magnetic resonance imaging

To test the feasibility and consistency of a deep-learning (DL) accelerated QSM method for deep brain nuclei evaluation. Participants were scanned with both parallel imaging (PI)-QSM and DL-QSM methods. The PI- and DL-QSM scans had identical imaging parameters other than acceleration factors (AF). The DL-QSM employed Poisson disk style under-sampling scheme and a previously developed cascaded CNN based reconstruction model, with acquisition time of 4:35, 3:15, and 2:11 for AF of 3, 4, and 5, respectively. For PI-QSM acquisition, the AF was 2 and the acquisition time was 6:46. The overall image similarity was assessed between PI- and DL-QSM images using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). QSM values from 7 deep brain nuclei were extracted and agreements between images with different Afs were assessed. Finally, the correlations between age and QSM values in the selected deep brain nuclei were evaluated. 59 participants were recruited. Compared to PI-QSM images, the mean SSIM of DL images were 0.87, 0.86, and 0.85 for AF of 3, 4, and 5. The mean PSNR were 44.56, 44.53, and 44.23. Susceptibility values from DL-QSM were highly consistent with routine PI-QSM images, with differences of less than 5% at the group level. Furthermore, the associations between age and QSM values could be consistently revealed. DL-QSM could be used for measuring susceptibility values of deep brain nucleus. An AF up to 5 did not significantly impact the correlation between age and susceptibility in deep brain nuclei.

SENSitivity Encoding (SENSE) is an effective mathematical formulation for reconstructing under-sampled MRI data obtained in Parallel Magnetic Resonance Imaging (Parallel MRI). The functional model includes a regularization term and a data fidelity term which need to be minimized to obtain a high quality MRI result. The proper choice of regularization is essential for image quality. In this paper, we show that Euler’s elastica is an effective regularization for Parallel MRI data reconstruction, and has advantages over the Total Variation (TV) regularization in improving image signal to noise ratio and image relative error. The Euler’s elastica functional is however complex to minimize as it is non-convex, non-smooth, and highly nonlinear. In this paper, we propose a new numerical method to solve Euler’s elastica regularized SENSE efficiently. This algorithm is based on a variable splitting approach and proper relaxation of the functional. Numerical examples are presented to show the effectiveness of the proposed Euler’s elastica algorithm in comparison to Bregman Operator Splitting with Variable Stepsize, a TV based algorithm.

Deep learning (DL) reconstruction has been successful in realizing otherwise impracticable acceleration factors and improving image quality in conventional MRI field strengths; however, there has been limited application to ultra-high-field MRI. The objective of this study was to evaluate the performance of a prototype DL-based image reconstruction technique in 7T MRI of the brain utilizing magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) and sampling perfection with application-optimized contrasts using different flip angle evolutions (SPACE) acquisitions, in comparison with reconstructions in conventional compressed sensing and controlled aliasing in parallel imaging techniques. This retrospective study involved 60 patients who underwent 7T brain MRI between June 2024 and October 2024, comprising 30 patients with MP2RAGE data and 30 patients with SPACE FLAIR data. Each set of raw data was reconstructed with DL-based reconstruction and conventional reconstruction. Image quality was independently assessed by 2 neuroradiologists by using a 5-point Likert scale, which included overall image quality, artifacts, sharpness, structural conspicuity, and noise level. Interobserver agreement was determined by using top-box analysis. Contrast-to-noise ratio (CNR) and noise levels were quantitatively evaluated and compared by using the Wilcoxon signed-rank test. DL-based reconstruction resulted in a significant increase in overall image quality and a reduction in subjective noise level for both MP2RAGE and SPACE FLAIR data (all P < .001), with no significant differences in image artifacts (all P > .05). When compared with standard reconstruction, the implementation of DL-based reconstruction yielded an increase in CNR of 49.5% (95% CI, 33.0%-59.0%) for MP2RAGE data and 90.6% (95% CI, 73.2%-117.7%) for SPACE FLAIR data, along with a decrease in noise of 33.5% (95% CI, 23.0%-38.0%) for MP2RAGE data and 47.5% (95% CI, 41.9%-52.6%) for SPACE FLAIR data. DL-based reconstruction of 7T MRI significantly enhanced image quality compared with conventional reconstruction without introducing image artifacts. The achievable high acceleration factors have the potential to substantially improve image quality and resolution in 7T MRI.