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.

Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

OBJECTIVES: To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. METHODS: The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. RESULTS: The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0 ms for benign and 63.3 ± 16.0 ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. CONCLUSIONS: Free-breathing abdominal T2 mapping is achieved in about 100 ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. KEY POINTS: • MOLED achieves free-breathing abdominal T2 mapping in about 100 ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

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.

MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T2 Mapping With Severe Head Motion Using Overlapping-Echo Acquisition.

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust [Formula: see text] mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for [Formula: see text] mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in [Formula: see text] maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at https://github.com/qinqinyang/MOST-DL.

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.

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.

Simultaneous multi-banding and multi-echo phase encoding for the accelerated acquisition of high-resolution volumetric diffusivity maps by spatiotemporally encoded MRI.

PURPOSE: Spatiotemporal Encoding (SPEN) is an ultrafast imaging technique where the low-bandwidth axis is rasterized in a joint spatial/k-domain. SPEN benefits from increased robustness to field inhomogeneities, folding-free reconstruction of subsampled data, and an ability to combine multiple interleaved or signal averaged scans -yet its relatively high SAR complicates volumetric uses. Here we show how this can be alleviated by merging simultaneous multi-band excitation, with intra-slab multi-echo (ME) phase encoding, for the acquisition of high definition volumetric DWI/DTI data. METHODS: A protocol involving phase-cycling of simultaneous multi-banded z-slab excitations in independently ky-interleaved scans, together with ME trains that kz-encoded positions within these slabs, was implemented. A reconstruction incorporating a CAIPIRINHA-like encoding of the multiple bands and exploiting SPEN's ability to deliver self-referenced, per-shot phase maps, then led to high-definition diffusivity acquisitions, with reduced SAR and acquisition times vis-à-vis non-optimized 3D counterparts. RESULTS: The new protocol was used to collect full brain 3 T DTI experiments at a variety of nominal voxel sizes, ranging from 1.95 to 2.54 mm3. In general, the new protocol yielded superior sensitivity and fewer distortions than what could be observed in comparably timed phase-encoded 3D SPEN, multi-slice 2D SPEN, or optimized EPI counterparts. CONCLUSIONS: A robust procedure for acquiring volumetric DWI/DTI data was developed and demonstrated.

Dynamic T2 mapping by multi-spin-echo spatiotemporal encoding.

PURPOSE: To develop a pulse sequence for acquiring robust, quantitative T2 relaxation maps in real time. METHODS: The pulse scheme relies on fully refocused spatiotemporally encoded multi-spin-echo trains, which provide images that are significantly less distorted than spin-echo echo planar imaging-based counterparts. This enables single-shot T2 mapping in inhomogeneity-prone regions. Another advantage of these schemes stems from their ability to interleave multiple scans in a reference-free manner, providing an option to increase sensitivity and spatial resolution with minimal motional artifacts. RESULTS: The method was implemented in preclinical and clinical scanners, where single-shot acquisitions delivered reliable T2 maps in ≤200 ms with ≈250 µm and ≈3 mm resolutions, respectively. Ca. 4 times higher spatial resolutions were achieved for the motion-compensated interleaved versions of these acquisitions, delivering T2 maps in ca. 10 s per slice. These maps were nearly indistinguishable from multi-scan relaxometric maps requiring orders-of-magnitude longer acquisitions; this was confirmed by mice head and real-time mice abdomen 7T scans performed following contrast-agent injections, as well as by 3T human brain and breast scans. CONCLUSION: This study introduced and demonstrated a new approach for acquiring rapid and quantitative T2 data, which is particularly reliable when operating at high fields and/or targeting heterogeneous organs or regions.

Motion-tolerant diffusion mapping based on single-shot overlapping-echo detachment (OLED) planar imaging.

PURPOSE: A new diffusion-mapping method based on single-shot overlapping-echo detachment (DM-OLED) planar-imaging sequence, along with a corresponding separation algorithm, is proposed to achieve reliable quantitative diffusion mapping in a single shot. The method can resist the effects of motion and help in detecting the quick variation of diffusion under different physiological status. METHODS: The echo-planar imaging method is combined with two excitation pulses with small flip angle to gain overlapping-echo signal in a single shot. Then the overlapping signals are separated by a separation algorithm and used for diffusion computation. Numerical simulation, phantom, and in vivo rat experiments were performed to verify the efficiency, accuracy, and motion tolerance of DM-OLED. RESULTS: The DM-OLED sequence could obtain reliable diffusion maps within milliseconds in numerical simulation, phantom, and in vivo experiments. Compared with conventional diffusion mapping with spin-echo echo-planar imaging, DM-OLED has higher time resolution and fewer motion-incurred errors in the apparent diffusion coefficient maps. CONCLUSIONS: As a reliable fast diffusion measurement tool, DM-OLED shows promise for real-time dynamic diffusion mapping and functional magnetic resonance imaging. Magn Reson Med 80:200-210, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Albumin-based nanoparticles loaded with hydrophobic gadolinium chelates as T1-T2 dual-mode contrast agents for accurate liver tumor imaging.

Magnetic resonance contrast agents with T1-T2 dual mode contrast capability have attracted considerable interest because they offer complementary and synergistic diagnostic information, leading to high imaging sensitivity and accurate diagnosis. Here, we reported a facile strategy to construct albumin based nanoparticles loaded with hydrophobic gadolinium chelates by hydrophobic interaction for magnetic resonance imaging (MRI). We synthesized a glycyrrhetinic acid-containing Gd-DOTA derivative (GGD) and loaded GGD molecules into BSA nanoparticles to form GGD-BSA nanoparticles (GGD-BSA NPs). The large size and porous structure endow GGD-BSA NPs with geometrical confinement, which restricts the tumbling of GGD and the diffusion of surrounding water molecules. As a result, GGD-BSA NPs exhibit ultrahigh T1 and T2 relaxivities, which are approximately 8-fold higher than those of gadolinium-based clinical contrast agents at 0.5 T. Besides, due to the intrinsic properties of their components, GGD-BSA NPs show good biocompatibility in vitro and in vivo, which warrants their great potential in clinical translation. Furthermore, GGD-BSA NPs show remarkable sensitivity in noninvasive detection of liver tumors by self-confirmed T1-T2 dual-mode contrast-enhanced MRI. All of these merits make GGD-BSA NPs a potential candidate for fruitful biomedical and preclinical applications.

Deep Photoacoustic/Luminescence/Magnetic Resonance Multimodal Imaging in Living Subjects Using High-Efficiency Upconversion Nanocomposites.

A gadolinium-doped multi-shell upconversion nanoparticle under 800 nm excitation is synthesized with a 10-fold fluorescence-intensity enhancement over that under 980 nm. The nanoformulations exhibit excellent photoacoustic/luminescence/magnetic resonance tri-modal imaging capabilities, enabling visualization of tumor morphology and microvessel distribution at a new imaging depth.