Partial fourier reconstruction through data fitting and convolution in k -space

AI-assisted compressed sensing and parallel imaging sequences for MRI of patients with nasopharyngeal carcinoma: comparison of their capabilities in terms of examination time and image quality

OBJECTIVE

To compare examination time and image quality between artificial intelligence (AI)-assisted compressed sensing (ACS) technique and parallel imaging (PI) technique in MRI of patients with nasopharyngeal carcinoma (NPC).

METHODS

Sixty-six patients with pathologically confirmed NPC underwent nasopharynx and neck examination using a 3.0-T MRI system. Transverse T2-weighted fast spin-echo (FSE) sequence, transverse T1-weighted FSE sequence, post-contrast transverse T1-weighted FSE sequence, and post-contrast coronal T1-weighted FSE were obtained by both ACS and PI techniques, respectively. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and duration of scanning of both sets of images analyzed by ACS and PI techniques were compared. The images from the ACS and PI techniques were scored for lesion detection, margin sharpness of lesions, artifacts, and overall image quality using the 5-point Likert scale.

RESULTS

The examination time with ACS technique was significantly shorter than that with PI technique (p < 0.0001). The comparison of SNR and CNR showed that ACS technique was significantly superior with PI technique (p < 0.005). Qualitative image analysis showed that the scores of lesion detection, margin sharpness of lesions, artifacts, and overall image quality were higher in the ACS sequences than those in the PI sequences (p < 0.0001). Inter-observer agreement was evaluated for all qualitative indicators for each method, in which the results showed satisfactory-to-excellent agreement (p < 0.0001).

CONCLUSION

Compared with the PI technique, the ACS technique for MR examination of NPC can not only shorten scanning time but also improve image quality.

CLINICAL RELEVANCE STATEMENT

The artificial intelligence (AI)-assisted compressed sensing (ACS) technique shortens examination time for patients with nasopharyngeal carcinoma, while improving the image quality and examination success rate, which will benefit more patients.

KEY POINTS

• Compared with the parallel imaging (PI) technique, the artificial intelligence (AI)-assisted compressed sensing (ACS) technique not only reduced examination time, but also improved image quality. • Artificial intelligence (AI)-assisted compressed sensing (ACS) pulls the state-of-the-art deep learning technique into the reconstruction procedure and helps find an optimal balance of imaging speed and image quality.

Influence of half Fourier and elliptical scanning (radial scan) on magnetic resonance images

This study aimed to investigate the effect of using slice partial Fourier (SPF), phase partial Fourier (PPF), and radial scan (Elliptical scanning) methods on image quality. Changes in signal-to-noise ratio (SNR), effective slice thickness, and in-plane resolution were measured in 3D-gradient echo when SPF, PPF, and radial scan were used. Effective slice thickness increased and SNR increased when SPF was used; in-plane resolution decreased and SNR decreased when PPF was used; effective slice thickness did not change, in-plane resolution decreased, and SNR increased when the radial scan method was used. The radial scan method reduces image quality and imaging time compared to those in the SPF and PPF methods.

K-UNN: k-space interpolation with untrained neural network

Recently, untrained neural networks (UNNs) have shown satisfactory performances for MR image reconstruction on random sampling trajectories without using additional full-sampled training data. However, the existing UNN-based approaches lack the modeling of physical priors, resulting in poor performance in some common scenarios (e.g., partial Fourier (PF), regular sampling, etc.) and the lack of theoretical guarantees for reconstruction accuracy. To bridge this gap, we propose a safeguarded k-space interpolation method for MRI using a specially designed UNN with a tripled architecture driven by three physical priors of the MR images (or k-space data), including transform sparsity, coil sensitivity smoothness, and phase smoothness. We also prove that the proposed method guarantees tight bounds for interpolated k-space data accuracy. Finally, ablation experiments show that the proposed method can characterize the physical priors of MR images well. Additionally, experiments show that the proposed method consistently outperforms traditional parallel imaging methods and existing UNNs, and is even competitive against supervised-trained deep learning methods in PF and regular undersampling reconstruction.

The feasibility investigation of AI -assisted compressed sensing in kidney MR imaging: an ultra-fast T2WI imaging technology

Object

To explore the feasibility and clinical application of AI -assisted compressed sensing (ACS) technology in kidney MR imaging.

Methods

33 patients were enrolled in this study, affiliated to our hospital from September 2020 to April 2021. The patients underwent T2-weighed sequences of both the ACS scan and the conventional respiratory navigator (NAVI) scan. We evaluated the subjective image quality scores, including the sharpness of image edge, artifact and the overall image quality, and compared the objective image quality indicators such as scanning time, signal-to-noise ratio (SNR), and contrast signal-to-noise ratio (CNR). The Wilcoxon’s rank sum test and the paired t test were used to compare the image quality between ACS and NAVI groups. The p-value less than 0.05 indicated a statistically significant difference.

Results

The edge sharpness of the ACS group was significant lower than that of the NAVI group (p < 0.01), however, there were no significant differences in the artifact and the overall rating of image quality between the two groups (p > 0.05). In terms of the objective image quality scores, the scanning time of the ACS group is significantly lower than that of control group. The SNR and CNR of ACS group were significantly higher than those of NAVI group (SNR:3.63 ± 0.76 vs 3.04 ± 0.44, p < 0.001; CNR: 14.44 ± 4.53 vs 12.05 ± 3.32, p < 0.001). In addition, the subjective and objective measurement results of the two radiologists were in good agreement (ICC = 0.61–0.88).

Conclusion

ACS technology has obvious advantages when applied to kidney MR imaging, which can realize ultra-fast MR imaging. The images can be acquired with a single breath-hold (17 s), which greatly shortens the scanning time. Moreover, the image quality is equal to or better than the conventional technology, which can meet the diagnostic requirements. Thus, it has obvious advantages in diagnosis for kidney disease patients with different tolerance levels for the clinical promotion.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00842-1.

Improving GRAPPA reconstruction using joint nonlinear kernel mapped and phase conjugated virtual coils

To improve the reconstruction condition and alleviate the noise amplification of GRAPPA reconstruction by aggregating the phase conjugated and nonlinear kernel mapped coils with the original physical coil. Nonlinear GRAPPA (NL-GRAPPA) is a kernel-based non-iterative approach which can reduce noise-induced error in GRAPPA reconstruction. And virtual conjugate coil (VCC) embeds the conjugate symmetric property of k-space into GRAPPA data synthesis to improve reconstruction condition. This work proposed NL-VCC-GRAPPA to jointly utilize the nonlinear mapped virtual coil and phase conjugated virtual coil to further reduce noise amplification in parallel imaging. In vivo static and dynamic 2D imaging accelerated by uniform undersampling schemes were performed to evaluate the proposed method in terms of visual image quality, root-mean-square-error (RMSE), and geometry factor (g-factor). The effects of acceleration factors, calibration data size and kernel shape on the proposed model were also separately analyzed and discussed. The proposed method illustrated improved visual image quality evidenced by reduced retrospective RMSE and prospective g-factor comparing with conventional GRAPPA and the recently proposed iterative SENSE-LORAKS reconstructions. Although a larger amount of calibration data and smaller kernel size were required to stabilize the calibration of fourfold extended kernel for the proposed method, it was non-iterative and relatively insensitive to parameter adjustment in the applications. The proposed NL-VCC-extension to conventional GRAPPA brings visible improvements for imaging scenarios accelerated by the widely available uniform undersampling schemes in a practically efficient manner without iteration.

Three-dimensional free breathing whole heart cardiovascular magnetic resonance T1 mapping at 3 T

BACKGROUND

This study demonstrates a three-dimensional (3D) free-breathing native myocardial T1 mapping sequence at 3 T.

METHODS

The proposed sequence acquires three differently T1-weighted volumes. The first two volumes receive a saturation pre-pulse with different recovery time. The third volume is acquired without magnetization preparation and after a significant recovery time. Respiratory navigator gating and volume-interleaved acquisition are adopted to mitigate misregistration. The proposed sequence was validated through simulation, phantom experiments and in vivo experiments in 12 healthy adult subjects.

RESULTS

In phantoms, good agreement on T1 measurement was achieved between the proposed sequence and the reference inversion recovery spin echo sequence (R2 = 0.99). Homogeneous 3D T1 maps were obtained from healthy adult subjects, with a T1 value of 1476 ± 53 ms and a coefficient of variation (CV) of 6.1 ± 1.4% over the whole left-ventricular myocardium. The averaged septal T1 was 1512 ± 60 ms with a CV of 2.1 ± 0.5%.

CONCLUSION

Free-breathing 3D native T1 mapping at 3 T is feasible and may be applicable in myocardial assessment. The proposed 3D T1 mapping sequence is suitable for applications in which larger coverage is desired beyond that available with single-shot parametric mapping, or breath-holding is unfeasible.

LORAKS makes better SENSE: Phase-constrained partial fourier SENSE reconstruction without phase calibration

PURPOSE

Parallel imaging and partial Fourier acquisition are two classical approaches for accelerated MRI. Methods that combine these approaches often rely on prior knowledge of the image phase, but the need to obtain this prior information can place practical restrictions on the data acquisition strategy. In this work, we propose and evaluate SENSE-LORAKS, which enables combined parallel imaging and partial Fourier reconstruction without requiring prior phase information.

THEORY AND METHODS

The proposed formulation is based on combining the classical SENSE model for parallel imaging data with the more recent LORAKS framework for MR image reconstruction using low-rank matrix modeling. Previous LORAKS-based methods have successfully enabled calibrationless partial Fourier parallel MRI reconstruction, but have been most successful with nonuniform sampling strategies that may be hard to implement for certain applications. By combining LORAKS with SENSE, we enable highly accelerated partial Fourier MRI reconstruction for a broader range of sampling trajectories, including widely used calibrationless uniformly undersampled trajectories.

RESULTS

Our empirical results with retrospectively undersampled datasets indicate that when SENSE-LORAKS reconstruction is combined with an appropriate k-space sampling trajectory, it can provide substantially better image quality at high-acceleration rates relative to existing state-of-the-art reconstruction approaches.

CONCLUSION

The SENSE-LORAKS framework provides promising new opportunities for highly accelerated MRI. Magn Reson Med 77:1021-1035, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Single-frequency excitation wideband MRI (SE-WMRI)

PURPOSE

In this study, single-frequency excitation wideband magnetic resonance imaging (MRI) (SE-WMRI) was proposed to obtain high-quality accelerated images by reducing phase-encoding steps while applying separation gradients.

METHODS

A zig-zag k-space trajectory with reduced phase-encoding steps and an increased readout sampling rate was proposed. A unique gradient design with buffer intervals near the trajectory turns was employed to avoid undersampling and image artifacts. A gridding method and Fourier transform were used for image reconstruction. Quantitative analysis was performed on phantom images to investigate the characteristics of the acceleration method.

RESULTS

The proposed method showed evident improvements in the accelerated phantom images, substantially reducing the ringing and blurring artifacts found using previous methods. Furthermore, the accelerated images exhibited the same signal-to-noise ratio as standard imaging. The accelerated in vivo experiment also produced the same quality as standard imaging.

CONCLUSIONS

The proposed SE-WMRI method can effectively remove image artifacts and acquire images of higher temporal or spatial resolutions with less compromise.

P-LORAKS: Low-rank modeling of local k-space neighborhoods with parallel imaging data

PURPOSE

To propose and evaluate P-LORAKS a new calibrationless parallel imaging reconstruction framework.

THEORY AND METHODS

LORAKS is a flexible and powerful framework that was recently proposed for constrained MRI reconstruction. LORAKS was based on the observation that certain matrices constructed from fully sampled k-space data should have low rank whenever the image has limited support or smooth phase, and made it possible to accurately reconstruct images from undersampled or noisy data using low-rank regularization. This paper introduces P-LORAKS, which extends LORAKS to the context of parallel imaging. This is achieved by combining the LORAKS matrices from different channels to yield a larger but more parsimonious low-rank matrix model of parallel imaging data. This new model can be used to regularize the reconstruction of undersampled parallel imaging data, and implicitly imposes phase, support, and parallel imaging constraints without needing to calibrate phase, support, or sensitivity profiles.

RESULTS

The capabilities of P-LORAKS are evaluated with retrospectively undersampled data and compared against existing parallel MRI reconstruction methods. Results show that P-LORAKS can improve parallel imaging reconstruction quality, and can enable the use of new k-space trajectories that are not compatible with existing reconstruction methods.

CONCLUSION

The P-LORAKS framewok provides a new and effective way to regularize parallel imaging reconstruction.

An L1-norm phase constraint for half-Fourier compressed sensing in 3D MR imaging

OBJECTIVE

In most half-Fourier imaging methods, explicit phase replacement is used. In combination with parallel imaging, or compressed sensing, half-Fourier reconstruction is usually performed in a separate step. The purpose of this paper is to report that integration of half-Fourier reconstruction into iterative reconstruction minimizes reconstruction errors.

MATERIALS AND METHODS

The L1-norm phase constraint for half-Fourier imaging proposed in this work is compared with the L2-norm variant of the same algorithm, with several typical half-Fourier reconstruction methods. Half-Fourier imaging with the proposed phase constraint can be seamlessly combined with parallel imaging and compressed sensing to achieve high acceleration factors.

RESULTS

In simulations and in in-vivo experiments half-Fourier imaging with the proposed L1-norm phase constraint enables superior performance both reconstruction of image details and with regard to robustness against phase estimation errors.

CONCLUSION

The performance and feasibility of half-Fourier imaging with the proposed L1-norm phase constraint is reported. Its seamless combination with parallel imaging and compressed sensing enables use of greater acceleration in 3D MR imaging.

Three-dimensional whole-heart T2 mapping at 3T

PURPOSE

Detecting variations in myocardial water content with T2 mapping is superior to conventional T2 -weighted MRI since quantification enables direct observation of complicated pathology. Most commonly used T2 mapping techniques are limited in achievable spatial and/or temporal resolution, both of which reduce accuracy due to partial-volume averaging and misregistration between images. The goal of this study was to validate a novel free breathing T2 mapping sequence that overcomes these limitations.

METHODS

The proposed technique was made insensitive to heart rate variability through the use of a saturation prepulse to reset magnetization every heartbeat. Respiratory navigator-gated, differentially T2 -weighted volumes were interleaved per heartbeat, guaranteeing registered images and robust voxel-by-voxel T2 maps. Free breathing acquisitions removed limits on spatial resolution and allowed short diastolic windows. Accuracy was quantified with simulations and phantoms.

RESULTS

Homogeneous three-dimensional (3D) T2 maps were obtained from normal human subjects and swine. Normal human and swine left ventricular T2 values were 42.3 ± 4.0 and 43.5 ± 4.3 ms, respectively. The T2 value for edematous myocardium obtained from a swine model of acute myocardial infarction was 59.1 ± 7.1 ms.

CONCLUSION

Free-breathing accurate 3D T2 mapping is feasible and may be applicable in myocardial assessment in lieu of current clinical black blood, T2 -weighted techniques.

Low-rank modeling of local k-space neighborhoods (LORAKS) for constrained MRI

Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-rank matrices when the image has limited spatial support or slowly varying phase. Based on this, we develop a novel and flexible framework for constrained image reconstruction that uses low-rank matrix modeling of local k-space neighborhoods (LORAKS). A new regularization penalty and corresponding algorithm for promoting low-rank are also introduced. The potential of LORAKS is demonstrated with simulated and experimental data for a range of denoising and sparse-sampling applications. LORAKS is also compared against state-of-the-art methods like homodyne reconstruction, l1-norm minimization, and total variation minimization, and is demonstrated to have distinct features and advantages. In addition, while calibration-based support and phase constraints are commonly used in existing methods, the LORAKS framework enables calibrationless use of these constraints.

Extrapolation and phase correction of non-uniformly broadened signals

The initial part of FID-signals cannot always be acquired experimentally. This is particularly true for signals characterised by strong inhomogeneous broadening, such as those in porous materials, e.g. cements, soils and rocks, those measured by portable NMR-apparatus, or EPR-signals. Here we report on a numerical method we designed to extrapolate those initial missing parts, i.e. to retrieve their amplitude and phase. Should the entire signal be available from an experiment, the algorithm can still be used as an automatic phase-corrector and a low-pass filter. The method is based on the use of cardinal series, applies to any oversampled signals and requires no prior knowledge of the system under study. We show that the method can also be used to restore entire one-dimensional MRI-data sets from those in which less than half of the k-space was sampled, thus not only potentially allowing to speed up data acquisition - when extended to two or three dimensions, but also to circumvent phase-distortions usually encountered when exploring the k-space near its origin.