Pseudo multishot echo-planar imaging for geometric distortion improvement

Conventional echo-planar imaging (EPI) uses a radiofrequency pulse for excitation and a prolonged echo train to sample k space, while off-resonance and T₂ * decay effects caused by magnetic susceptibility variation accumulate within each echo, leading to geometric distortion. Multishot EPI methods, which divide k space into segments, can shorten the effective echo spacing and reduce the distortion on EPI images. But multiple shots cost longer scan time and render susceptibility to motion. In this study, we propose a new "multishot" EPI method termed pseudo multishot EPI (pmsEPI), in which phase-encoding lines are segmented as in multishot EPI but are collected within a single shot. With the magnetization divided into different pathways via interleaved excitation instead of refocusing in a single long echo train, the total phase error accumulation is reduced in each segmented acquisition, thereby improving distortion of the resultant EPI image. The performance of the pmsEPI method is demonstrated by phantom and in vivo brain experiments on a 3-T scanner. The experimental results show that the distortion displacements of pmsEPI acquisition compared with conventional EPI decrease by 50% with two pseudo shots and 66% with three pseudo shots, validating the ability of the method to obtain images with reduced distortion in a single shot, although magnetization splitting may induce more than 40% SNR loss and minor artifacts. Specifically, the ability of pmsEPI in diffusion-weighted imaging with different trajectory options is highlighted, and the flexibility is demonstrated in a single-shot blip up and down acquisition.

Rapid high-fidelity T 2 * mapping using single-shot overlapping-echo acquisition and deep learning reconstruction

PURPOSE

To develop and evaluate a single-shot quantitative MRI technique called GRE-MOLED (gradient-echo multiple overlapping-echo detachment) for rapid T 2 * $$ {T}_2^{\ast } $$ mapping.

METHODS

In GRE-MOLED, multiple echoes with different TEs are generated and captured in a single shot of the k-space through MOLED encoding and EPI readout. A deep neural network, trained by synthetic data, was employed for end-to-end parametric mapping from overlapping-echo signals. GRE-MOLED uses pure GRE acquisition with a single echo train to deliver T 2 * $$ {T}_2^{\ast } $$ maps less than 90 ms per slice. The self-registered B₀ information modulated in image phase was utilized for distortion-corrected parametric mapping. The proposed method was evaluated in phantoms, healthy volunteers, and task-based FMRI experiments.

RESULTS

The quantitative results of GRE-MOLED T 2 * $$ {T}_2^{\ast } $$ mapping demonstrated good agreement with those obtained from the multi-echo GRE method (Pearson's correlation coefficient = 0.991 and 0.973 for phantom and in vivo brains, respectively). High intrasubject repeatability (coefficient of variation <1.0%) were also achieved in scan-rescan test. Enabled by deep learning reconstruction, GRE-MOLED showed excellent robustness to geometric distortion, noise, and random subject motion. Compared to the conventional FMRI approach, GRE-MOLED also achieved a higher temporal SNR and BOLD sensitivity in task-based FMRI.

CONCLUSION

GRE-MOLED is a new real-time technique for T 2 * $$ {T}_2^{\ast } $$ quantification with high efficiency and quality, and it has the potential to be a better quantitative BOLD detection method.

Single-shot T2 mapping via multi-echo-train multiple overlapping-echo detachment planar imaging and multitask deep learning

BACKGROUND

Quantitative magnetic resonance imaging provides robust biomarkers in clinics. Nevertheless, the lengthy scan time reduces imaging throughput and increases the susceptibility of imaging results to motion. In this context, a single-shot T₂ mapping method based on multiple overlapping-echo detachment (MOLED) planar imaging was presented, but the relatively small echo time range limits its accuracy, especially in tissues with large T₂ .

PURPOSE

In this work we proposed a novel single-shot method, Multi-Echo-Train Multiple OverLapping-Echo Detachment (METMOLED) planar imaging, to accommodate a large range of T₂ quantification without additional measurements to rectify signal degeneration arisen from refocusing pulse imperfection.

METHODS

Multiple echo-train techniques were integrated into the MOLED sequence to capture larger TE information. Maps of T₂ , B₁ , and spin density were reconstructed synchronously from acquired METMOLED data via multitask deep learning. A typical U-Net was trained with 3000/600 synthetic data with geometric/brain patterns to learn the mapping relationship between METMOLED signals and quantitative maps. The refocusing pulse imperfection was settled through the inherent information of METMOLED data and auxiliary tasks.

RESULTS

Experimental results on the digital brain (structural similarity (SSIM) index = 0.975/0.991/0.988 for MOLED/METMOLED-2/METMOLED-3, hyphenated number denotes the number of echo-trains), physical phantom (the slope of linear fitting with reference T₂ map = 1.047/1.017/1.006 for MOLED/METMOLED-2/METMOLED-3), and human brain (Pearson's correlation coefficient (PCC) = 0.9581/0.9760/0.9900 for MOLED/METMOLED-2/METMOLED-3) demonstrated that the METMOLED improved the quantitative accuracy and the tissue details in contrast to the MOLED. These improvements were more pronounced in tissues with large T₂ and in application scenarios with high temporal resolution (PCC = 0.8692/0.9465/0.9743 for MOLED/METMOLED-2/METMOLED-3). Moreover, the METMOLED could rectify the signal deviations induced by the non-ideal slice profiles of refocusing pulses without additional measurements. A preliminary measurement also demonstrated that the METMOLED is highly repeatable (mean coefficient of variation (CV) = 1.65%).

CONCLUSIONS

METMOLED breaks the restriction of echo-train length to TE and implements unbiased T₂ estimates in an extensive range. Furthermore, it corrects the effect of refocusing pulse inaccuracy without additional measurements or signal post-processing, thus retaining its single-shot characteristic. This technique would be beneficial for accurate T₂ quantification.

Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T₂, T₂*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T₂, and T₁-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.

A simultaneous multi-slice T2 mapping framework based on overlapping-echo detachment planar imaging and deep learning reconstruction

PURPOSE

Quantitative MRI (qMRI) is of great importance to clinical medicine and scientific research. However, most qMRI techniques are time-consuming and sensitive to motion, especially when a large 3D volume is imaged. To accelerate the acquisition, a framework is proposed to realize reliable simultaneous multi-slice T₂ mapping.

METHODS

The simultaneous multi-slice T₂ mapping framework is based on overlapping-echo detachment (OLED) planar imaging (dubbed SMS-OLED). Multi-slice overlapping-echo signals were generated by multiple excitation pulses together with echo-shifting gradients. The signals were excited and acquired with a single-channel coil. U-Net was used to reconstruct T₂ maps from the acquired overlapping-echo image.

RESULTS

Single-shot double-slice and two-shot triple-slice SMS-OLED scan schemes were designed according to the framework for evaluation. Simulations, water phantom, and in vivo rat brain experiments were carried out. Overlapping-echo signals were acquired, and T₂ maps were reconstructed and compared with references. The results demonstrate the superior performance of our method.

CONCLUSION

Two slices of T₂ maps can be obtained in a single shot within hundreds of milliseconds. Higher quality multi-slice T₂ maps can be obtained via multiple shots. SMS-OLED provides a lower specific absorption rate scheme compared with conventional SMS methods with a coil with only a single receiver channel. The new method is of potential in dynamic qMRI and functional qMRI where temporal resolution is vital.

Robust Single-Shot T2 Mapping via Multiple Overlapping-Echo Acquisition and Deep Neural Network

Quantitative magnetic resonance imaging (MRI) is of great value to both clinical diagnosis and scientific research. However, most MRI experiments remain qualitative, especially dynamic MRI, because repeated sampling with variable weighting parameter makes quantitative imaging time-consuming and sensitive to motion artifacts. A single-shot quantitative T₂ mapping method based on multiple overlapping-echo acquisition (dubbed MOLED-4) was proposed to obtain reliable T₂ mapping in milliseconds. Different from traditional MRI acceleration methods, such as compressed sensing and parallel imaging, MOLED-4 accelerates quantitative T₂ mapping via synchronized multisampling and then deep learning to map the complex nonlinear relationship that is difficult to solve by traditional optimization-based methods. The results of simulation, phantom, and in vivo human brain experiments show the great performance of the proposed method. The principle of MOLED-4 may be extended to other ultrafast quantitative parameter mappings and potentially lead to new dynamic MRI with high efficiency to catch quantitative variation of tissue properties.

Accelerating multi-slice spatiotemporally encoded MRI with simultaneous echo refocusing

Single-shot spatiotemporally encoded (SPEN) ultrafast magnetic resonance imaging (MRI) is of great value to both scientific research and clinical application owing to its capability for delivering MR images with greater robustness to magnetic field inhomogeneity and chemical-shift displacement effects than conventional methods like EPI due to high effective phase-encoded bandwidth. Many SPEN MRI methods have been developed, among which multi-slice SPEN MRI arises as a promising supplement to ultrafast multi-slice sampling. In this work, we propose a new multi-slice SPEN MRI method, termed multi-echo segmented SPEN (ME-SeSPEN) method, which produces multiple images within a single train of echoes and successively samples widely separated slices. The resulting images were reconstructed using de-convolution super-resolved algorithm. The robustness and efficiency of the proposed method were demonstrated by phantom, lemon and in vivo experiments in comparison with spin-echo EPI, spin-echo simultaneous echo refocusing (SER), and segmented SPEN (SeSPEN) MRI. The results indicate that the new method effectively shortens the sampling time (20% reduction practically in comparison with SeSPEN when two slices are simultaneously sampled). ME-SeSPEN also reduces eddy current effects while maintaining the benefits of SPEN MRI, such as similar robustness to field inhomogeneity, spatial resolution and signal-to-noise ratio to SeSPEN MRI. The new method will promote the versatility of multi-slice MRI in practical applications.