The effects of SENSE on PROPELLER imaging

Improving deep PROPELLER MRI via synthetic blade augmentation and enhanced generalization

In PROPELLER MRI, obtaining sufficient high-quality blade data remains a challenge, so the efficiency and generalization of deep learning-based reconstruction models are deteriorated. Due to narrow rotated and translated blades acquired in PROPELLER, the technique of data augmentation that is used for deep learning-based Cartesian MRI reconstruction cannot be directly applied. To address the issue, this paper introduces a novel approach for the generation of synthetic PROPELLER blades, and it is subsequently employed in data augmentation for undersampled blades reconstruction. The principal aim of this study is to address the challenges of reconstructing undersampled blades to enhance both image quality and computational efficiency. Evaluation metrics including PSNR, NMSE, and SSIM indicate superior performance of the model trained with augmented data compared to non-augmented counterparts. The synthetic blade augmentation significantly enhances the model's generalization capability and enables robust performance across varying imaging conditions. Furthermore, the study demonstrates the feasibility of utilizing synthetic blades exclusively in the training phase, suggesting a reduced dependency on real PROPELLER blades. This innovation in synthetic blade generation and data augmentation technique contributes to enhanced image quality and improved generalization capability of the associated deep learning model for PROPELLER MRI reconstruction.

Improved reconstruction for highly accelerated propeller diffusion 1.5 T clinical MRI

PURPOSE

Propeller fast-spin-echo diffusion magnetic resonance imaging (FSE-dMRI) is essential for the diagnosis of Cholesteatoma. However, at clinical 1.5 T MRI, its signal-to-noise ratio (SNR) remains relatively low. To gain sufficient SNR, signal averaging (number of excitations, NEX) is usually used with the cost of prolonged scan time. In this work, we leveraged the benefits of Locally Low Rank (LLR) constrained reconstruction to enhance the SNR. Furthermore, we enhanced both the speed and SNR by employing Convolutional Neural Networks (CNNs) for the accelerated PROPELLER FSE-dMRI on a 1.5 T clinical scanner.

METHODS

Residual U-Net (RU-Net) was found to be efficient for propeller FSE-dMRI data. It was trained to predict 2-NEX images obtained by Locally Low Rank (LLR) constrained reconstruction and used 1-NEX images obtained via simplified reconstruction as the inputs. The brain scans from healthy volunteers and patients with cholesteatoma were performed for model training and testing. The performance of trained networks was evaluated with normalized root-mean-square-error (NRMSE), structural similarity index measure (SSIM), and peak SNR (PSNR).

RESULTS

For 4 × under-sampled with 7 blades data, online reconstruction appears to provide suboptimal images-some small details are missing due to high noise interferences. Offline LLR enables suppression of noises and discovering some small structures. RU-Net demonstrated further improvement compared to LLR by increasing 18.87% of PSNR, 2.11% of SSIM, and reducing 53.84% of NRMSE. Moreover, RU-Net is about 1500 × faster than LLR (0.03 vs. 47.59 s/slice).

CONCLUSION

The LLR remarkably enhances the SNR compared to online reconstruction. Moreover, RU-Net improves propeller FSE-dMRI as reflected in PSNR, SSIM, and NRMSE. It requires only 1-NEX data, which allows a 2 × scan time reduction. In addition, its speed is approximately 1500 times faster than that of LLR-constrained reconstruction.

Comparison of deep learning-based reconstruction of PROPELLER Shoulder MRI with conventional reconstruction

OBJECTIVE

To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder.

MATERIALS AND METHODS

We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics.

RESULTS

Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1).

CONCLUSION

Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.

T2 Turbo Spin Echo With Compressed Sensing and Propeller Acquisition (Sampling k-Space by Utilizing Rotating Blades) for Fast and Motion Robust Prostate MRI: Comparison With Conventional Acquisition

OBJECTIVES

The aim of this study was to compare a new compressed sensing (CS) method for T2-weighted propeller acquisitions (T2 CS ) with conventional T2-weighted propeller sequences (T2 conv ) in terms of achieving a higher image quality, while reducing the acquisition time.

MATERIALS AND METHODS

Male participants with a clinical suspicion of prostate cancer were prospectively enrolled and underwent prostate magnetic resonance imaging at 3 T. Axial and sagittal images of the T2 conv sequence and the T2 CS sequence were acquired. Sequences were qualitatively assessed by 2 blinded radiologists concerning artifacts, image-sharpness, lesion conspicuity, capsule delineation, and overall image quality using 5-point Likert items ranging from 1 (nondiagnostic) to 5 (excellent). The apparent signal-to-noise ratio and apparent contrast-to-noise ratio were evaluated. PI-RADS scores were assessed for both sequences. Statistical analysis was performed by using Wilcoxon signed rank test and paired samples t test. Intrarater and interrater reliability of qualitative image evaluation was assessed using intraclass correlation coefficient (ICC) estimates.

RESULTS

A total of 29 male participants were included (mean age, 66 ± 8 years). The acquisition time of the T2 CS sequence was respectively 26% (axial plane) and 24% (sagittal plane) shorter compared with the T2 conv sequence (eg, axial: 171 vs 232 seconds; P < 0.001). In the axial plane, the T2 CS sequence had fewer artifacts (4 [4-4.5] vs 4 [3-4]; P < 0.001), better image-sharpness (4 [4-4.5] vs 3 [3-3.5]; P < 0.001), better capsule delineation (4 [3-4] vs 3 [3-3.5]; P < 0.001), and better overall image quality (4 [4-4] vs 4 [3-4]; P < 0.001) compared with the T2 conv sequence. The ratings of lesion conspicuity were similar (4 [4-4] vs 4 [3-4]; P = 0.166). In the sagittal plane, the T2 CS sequence outperformed the T2 conv sequence in the categories artifacts (4 [4-4] vs 3 [3-4]; P < 0.001), image sharpness (4 [4-5] vs 4 [3-4]; P < 0.001), lesion conspicuity (4 [4-4] vs 4 [3-4]; P = 0.002), and overall image quality (4 [4-4] vs 4 [3-4]; P = 0.002). Capsule delineation was similar between both sequences (3 [3-4] vs 3 [3-3]; P = 0.07). Intraobserver and interobserver reliability for qualitative scoring were good (ICC intra: 0.92; ICC inter: 0.86). Quantitative analysis revealed a higher apparent signal-to-noise ratio (eg, axial: 52.2 ± 9.7 vs 22.8 ± 3.6; P < 0.001) and a higher apparent contrast-to-noise ratio (eg, axial: 44.0 ± 9.6 vs 18.6 ± 3.7; P ≤ 0.001) of the T2 CS sequence. PI-RADS scores were the same for both sequences in all participants.

CONCLUSIONS

CS-accelerated T2-weighted propeller acquisition had a superior image quality compared with conventional T2-weighted propeller sequences while significantly reducing the acquisition time.

Parallel MR image reconstruction based on triple cycle optimization

The self-calibration parallel imaging (SC-SENSE) method reconstructs the image by estimating the coil sensitivity matrix. In order to obtain the sensitivity matrix, it is necessary to take a small amount of automatic calibration signal lines (ACSL) in the center of k-space. This method uses the data of the central region to obtain the sensitivity matrix, and then the reconstructed image is obtained. This paper proposed the triple cycle optimization (TCO) method to continuously optimize reconstructed images. The proposed TCO method takes the sensitivity matrix obtained by ACSL and substituted the reconstructed image as the initial data generation into the loop, and estimates the k-space data repeatedly. A new sensitivity matrix is obtained by using k-space data and the reconstructed image, and a stable triple cycle is obtained. In the cycle, all data are optimized to a certain extent, including the reconstructed image. Experimental results show that under the same sampling density, images reconstructed by using the triple cycle optimization method have lower noise and artifacts than those of the traditional method. When combined with the variable density sampling method, the effect is remarkable with a much low sampling rate.

Group feature selection for enhancing information gain in MRI reconstruction

Magnetic resonance imaging (MRI) has revolutionized the radiology. As a leading medical imaging modality, MRI not only visualizes the structures inside body, but also produces functional imaging. However, due to the slow imaging speed constrained by the MR physics, MRI cost is expensive, and patient may feel not comfortable in a scanner for a long time. Parallel MRI has accelerated the imaging speed through the sub-Nyquist sampling strategy and the missing data are interpolated by the multiple coil data acquired. Kernel learning has been used in the parallel MRI reconstruction to learn the interpolation weights and re-construct the undersampled data. However, noise and aliasing artifacts still exist in the reconstructed image and a large number of auto-calibration signal lines are needed. To further improve the kernel learning-based MRI reconstruction and accelerate the speed, this paper proposes a group feature selection strategy to improve the learning performance and enhance the reconstruction quality. An explicit kernel mapping is used for selecting a subset of features which contribute most to estimate the missing k-space data. The experimental results show that the learning behaviours can be better predicted and therefore the reconstructed image quality is improved.

Robustness of a Combined Modified Dixon and PROPELLER Sequence with Two Interleaved Echoes in Clinical Head and Neck MRI

Purpose:

The combination of modified Dixon (mDixon) and periodically rotated overlapping parallel lines with enhanced reconstruction sequence with two interleaved echoes, which promotes uniform fat-suppression and motion insensitivity, has recently become available for commercial magnetic resonance imaging (MRI) scanners. To compare the robustness of this combination sequence with that of standard Cartesian mDixon sequence for fat-suppressed T₂-weighted imaging in clinical head and neck MRI.

Methods:

Fifty patients with head and neck tumors were involved this study. All patients underwent MRI using both the combination and standard sequences. Two radiologists independently scored motion artifacts and water–fat separation error using a 4-point scale (1, unacceptable; 4, excellent). Furthermore, comprehensive comparative evaluation was performed using a 5-point scale (1, substantially inferior; 5, substantially superior). Data were statistically analyzed using the Wilcoxon signed-rank test.

Results:

In the motion artifact assessment, ratings of 3 or 4 points were assigned to 45% (observer-1, 58.0%; observer-2, 32.0%) and 97% (100%; 94.0%) of images for the standard and combination sequences, respectively (P < 0.001). For the water–fat separation error assessment, ratings of 3 or 4 points were assigned to 100% (100%; 100%) and 85% (84.0%; 86.0%) of images, respectively (P < 0.001). In the comprehensive evaluation, of the 100 cases (observer-1, 50; observer-2, 50), 96 were rated at four or five points. In cases with slight or no motion artifacts and water–fat separation errors, the combination sequence was superior to the standard sequence in term of noise and sharpness, and equal in terms of contrast.

Conclusion:

Although water–fat separation errors increased significantly in the combination sequence, most of these were acceptable. The significantly decreased motion artifacts in the combination sequence significantly improved image quality overall.

Improving the Otsu method for MRA image vessel extraction via resampling and ensemble learning

Accurate extraction of vessels plays an important role in assisting diagnosis, treatment, and surgical planning. The Otsu method has been used for extracting vessels in medical images. However, blood vessels in magnetic resonance angiography (MRA) image are considered as a sparse distribution. Pixels on vessels in MRA image are considered as an imbalanced data in classification of vessels and non-vessel tissues. To extract vessels accurately, a novel method using resampling technique and ensemble learning is proposed for solving the imbalanced classification problem. Each pixel is sampled multiple times through multiple local patches within the image. Then, vessel or non-vessel tissue is determined by the ensemble voting mechanism via a p-tile algorithm. Experimental results show that the proposed method is able to outperform the traditional Otsu method by extracting vessels in MRA images more accurately.

Robotic Path Planning Using A* Algorithm for Automatic Navigation in Magnetic Resonance Angiography

Magnetic resonance navigation (MRN) is an emerging research technique in recent years. The micro/nano robots existing in vessels can be driven by magnetic gradients given by MR scanner. As a non-invasive vascular imaging technique, Magnetic resonance angiography (MRA) is able to provide a vascular network of an anatomy without injection of contrast agent. In order to automatically guide and drive micro/nano robots to target in vascular network, a navigation strategy is desired. In this paper, a novel path planning algorithm based on A* search is proposed. The MRA image is preliminarily processed to extract major vessels. Then, pixel-based A* search algorithm identifies the shortest path between start point and target without human supervision. Experimental results on both of simulation image and MRA image demonstrate that the proposed method is able to accomplish path planning automatically in MRA image. That path can guide the injected micro/nano robots to navigate in the blood vessels.

Rotating single-shot acquisition (RoSA) with composite reconstruction for fast high-resolution diffusion imaging

PURPOSE

To accelerate high-resolution diffusion imaging, rotating single-shot acquisition (RoSA) with composite reconstruction is proposed. Acceleration was achieved by acquiring only one rotating single-shot blade per diffusion direction, and high-resolution diffusion-weighted (DW) images were reconstructed by using similarities of neighboring DW images. A parallel imaging technique was implemented in RoSA to further improve the image quality and acquisition speed. RoSA performance was evaluated by simulation and human experiments.

METHODS

A brain tensor phantom was developed to determine an optimal blade size and rotation angle by considering similarity in DW images, off-resonance effects, and k-space coverage. With the optimal parameters, RoSA MR pulse sequence and reconstruction algorithm were developed to acquire human brain data. For comparison, multishot echo planar imaging (EPI) and conventional single-shot EPI sequences were performed with matched scan time, resolution, field of view, and diffusion directions.

RESULTS

The simulation indicated an optimal blade size of 48 × 256 and a 30 ° rotation angle. For 1 × 1 mm² in-plane resolution, RoSA was 12 times faster than the multishot acquisition with comparable image quality. With the same acquisition time as SS-EPI, RoSA provided superior image quality and minimum geometric distortion.

CONCLUSION

RoSA offers fast, high-quality, high-resolution diffusion images. The composite image reconstruction is model-free and compatible with various diffusion computation approaches including parametric and nonparametric analyses. Magn Reson Med 79:264-275, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

A New Joint-Blade SENSE Reconstruction for Accelerated PROPELLER MRI

PROPELLER technique is widely used in MRI examinations for being motion insensitive, but it prolongs scan time and is restricted mainly to T2 contrast. Parallel imaging can accelerate PROPELLER and enable more flexible contrasts. Here, we propose a multi-step joint-blade (MJB) SENSE reconstruction to reduce the noise amplification in parallel imaging accelerated PROPELLER. MJB SENSE utilizes the fact that PROPELLER blades contain sharable information and blade-combined images can serve as regularization references. It consists of three steps. First, conventional blade-combined images are obtained using the conventional simple single-blade (SSB) SENSE, which reconstructs each blade separately. Second, the blade-combined images are employed as regularization for blade-wise noise reduction. Last, with virtual high-frequency data resampled from the previous step, all blades are jointly reconstructed to form the final images. Simulations were performed to evaluate the proposed MJB SENSE for noise reduction and motion correction. MJB SENSE was also applied to both T2-weighted and T1-weighted in vivo brain data. Compared to SSB SENSE, MJB SENSE greatly reduced the noise amplification at various acceleration factors, leading to increased image SNR in all simulation and in vivo experiments, including T1-weighted imaging with short echo trains. Furthermore, it preserved motion correction capability and was computationally efficient.

[Modern magnetic resonance imaging of the liver]

Magnetic resonance imaging (MRI) of the liver has become an essential tool in the radiological diagnostics of both focal and diffuse diseases of the liver and is subject to constant change due to technological progress. Recently, important improvements could be achieved by innovations regarding MR hardware, sequences and postprocessing methods. The diagnostic spectrum of MRI could be broadened particularly due to new examination sequences, while at the same time scanning time could be shortened and image quality has been improved. The aim of this article is to explain both the technological background and the clinical application of recent MR sequence developments and to present the scope of a modern MRI protocol for the liver.