Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning

PURPOSE

To develop a method for building MRI reconstruction neural networks robust to changes in signal-to-noise ratio (SNR) and trainable with a limited number of fully sampled scans.

METHODS

We propose Noise2Recon, a consistency training method for SNR-robust accelerated MRI reconstruction that can use both fully sampled (labeled) and undersampled (unlabeled) scans. Noise2Recon uses unlabeled data by enforcing consistency between model reconstructions of undersampled scans and their noise-augmented counterparts. Noise2Recon was compared to compressed sensing and both supervised and self-supervised deep learning baselines. Experiments were conducted using retrospectively accelerated data from the mridata three-dimensional fast-spin-echo knee and two-dimensional fastMRI brain datasets. All methods were evaluated in label-limited settings and among out-of-distribution (OOD) shifts, including changes in SNR, acceleration factors, and datasets. An extensive ablation study was conducted to characterize the sensitivity of Noise2Recon to hyperparameter choices.

RESULTS

In label-limited settings, Noise2Recon achieved better structural similarity, peak signal-to-noise ratio, and normalized-RMS error than all baselines and matched performance of supervised models, which were trained with14 × $$ 14\times $$ more fully sampled scans. Noise2Recon outperformed all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to OOD acceleration factors. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability.

CONCLUSION

Noise2Recon is a label-efficient reconstruction method that is robust to distribution shifts, such as changes in SNR, acceleration factors, and others, with limited or no fully sampled training data.

SLfRank: Shinnar-Le-Roux Pulse Design With Reduced Energy and Accurate Phase Profiles Using Rank Factorization

The Shinnar-Le-Roux (SLR) algorithm is widely used to design frequency selective pulses with large flip angles. We improve its design process to generate pulses with lower energy (by as much as 26%) and more accurate phase profiles. Concretely, the SLR algorithm consists of two steps: (1) an invertible transform between frequency selective pulses and polynomial pairs that represent Cayley-Klein (CK) parameters and (2) the design of the CK polynomial pair to match the desired magnetization profiles. Because the CK polynomial pair is bi-linearly coupled, the original algorithm sequentially solves for each polynomial instead of jointly. This results in sub-optimal pulses. Instead, we leverage a convex relaxation technique, commonly used for low rank matrix recovery, to address the bi-linearity. Our numerical experiments show that the resulting pulses are almost always globally optimal in practice. For slice excitation, the proposed algorithm results in more accurate linear phase profiles. And in general the improved pulses have lower energy than the original SLR pulses.

Rapid fat-water separated T1 mapping using a single-shot radial inversion-recovery spoiled gradient recalled pulse sequence

T1 mapping is increasingly used in clinical practice and research studies. With limited scan time, existing techniques often have limited spatial resolution, contrast resolution and slice coverage. High fat concentrations yield complex errors in Look-Locker T1 methods. In this study, a dual-echo 2D radial inversion-recovery T1 (DEradIR-T1) technique was developed for fast fat-water separated T1 mapping. The DEradIR-T1 technique was tested in phantoms, 5 volunteers and 28 patients using a 3 T clinical MRI scanner. In our study, simulations were performed to analyze the composite (fat + water) and water-only T1 under different echo times (TE). In standardized phantoms, an inversion-recovery spin echo (IR-SE) sequence with and without fat saturation pulses served as a T1 reference. Parameter mapping with DEradIR-T1 was also assessed in vivo, and values were compared with modified Look-Locker inversion recovery (MOLLI). Bland-Altman analysis and two-tailed paired t-tests were used to compare the parameter maps from DEradIR-T1 with the references. Simulations of the composite and water-only T1 under different TE values and levels of fat matched the in vivo studies. T1 maps from DEradIR-T1 on a NIST phantom (Pcomp  = 0.97) and a Calimetrix fat-water phantom (Pwater  = 0.56) matched with the references. In vivo T1 was compared with that of MOLLI: R comp 2 = 0.77 ; R water 2 = 0.72 . In this work, intravoxel fat is found to have a variable, echo-time-dependent effect on measured T1 values, and this effect may be mitigated using the proposed DRradIR-T1.

Artifact- and content-specific quality assessment for MRI with image rulers

In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality assessment (IQA) would enable real-time remediation. Existing IQA works for MRI give only a general quality score, agnostic to the cause of and solution to low-quality scans. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of two of the most common artifacts in MRI: noise and motion. It achieves accuracies of around 90%, 6% better than the best previous method examined, and 3% better than human experts on noise assessment. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and generalizability.

Utilizing the Wavelet Transform's Structure in Compressed Sensing

Compressed sensing has empowered quality image reconstruction with fewer data samples than previously thought possible. These techniques rely on a sparsifying linear transformation. The Daubechies wavelet transform is commonly used for this purpose. In this work, we take advantage of the structure of this wavelet transform and identify an affine transformation that increases the sparsity of the result. After inclusion of this affine transformation, we modify the resulting optimization problem to comply with the form of the Basis Pursuit Denoising problem. Finally, we show theoretically that this yields a lower bound on the error of the reconstruction and present results where solving this modified problem yields images of higher quality for the same sampling patterns using both magnetic resonance and optical images.

Biopsy marker localization with thermo-acoustic ultrasound for lumpectomy guidance

PURPOSE

Almost one in four lumpectomies fails to fully remove cancerous tissue from the breast, requiring reoperation. This high failure rate suggests that existing lumpectomy guidance methods are inadequate for allowing surgeons to consistently identify the proper volume of tissue for excision. Current guidance techniques either provide little information about the tumor position or require surgeons to frequently switch between making incisions and manually probing for a marker placed at the lesion site. This article explores the feasibility of thermo-acoustic ultrasound (TAUS) to enable hands-free localization of metallic biopsy markers throughout surgery, which would allow for continuous visualization of the lesion site in the breast without the interruption of surgery. In a TAUS-based localization system, microwave excitations would be transmitted into the breast, and the amplification in microwave absorption around the metallic markers would generate acoustic signals from the marker sites through the thermo-acoustic effect. Detection and ranging of these signals by multiple acoustic receivers on the breast could then enable marker localization through acoustic multilateration.

METHODS

Physics simulations were used to characterize the TAUS signals generated from different markers by microwave excitations. First, electromagnetic simulations determined the spatial pattern of the amplification in microwave absorption around the markers. Then, acoustic simulations characterized the acoustic fields generated from these markers at various acoustic frequencies. TAUS-based one-dimensional (1D) ranging of two metallic markers-including a biopsy marker that is FDA-approved for clinical use-immersed in saline was also performed using a bench-top setup. To perform TAUS acquisitions, a microwave applicator was driven by 2.66 GHz microwave signals that were amplitude-modulated by chirps at the desired acoustic excitation frequencies, and the resulting TAUS signal from the markers was detected by an ultrasonic transducer.

RESULTS

The simulation results show that the geometry of the marker strongly impacts the quantity and spatial pattern of both the microwave absorption around the marker and the resulting TAUS signal generated from the marker. The simulated TAUS signal maps and acoustic frequency responses also make clear that the marker geometry plays an important role in determining the overall system response. Using the bench-top setup, TAUS detection and 1D localization of the markers were successfully demonstrated for multiple different combinations of microwave applicator and metallic marker. These initial results indicate that TAUS-based localization of biopsy markers is feasible.

CONCLUSIONS

Through microwave excitations and acoustic detection, TAUS can be used to localize metallic biopsy markers. With further development, TAUS opens new avenues to enable a more intuitive lumpectomy guidance system that could help to achieve better lumpectomy outcomes.

Fast variable density Poisson-disc sample generation with directional variation for compressed sensing in MRI

We present a fast method for generating random samples according to a variable density poisson-disc distribution. A minimum parameter value is used to create a background grid array for keeping track of those points that might affect any new candidate point; this reduces the number of conflicts that must be checked before acceptance of a new point, thus reducing the number of computations required. We demonstrate the algorithm's ability to generate variable density poisson-disc sampling patterns according to a parameterized function, including patterns where the variations in density are a function of direction. We further show that these sampling patterns are appropriate for compressed sensing applications. Finally, we present a method to generate patterns with a specific acceleration rate.

Multi-Domain Image Completion for Random Missing Input Data

Multi-domain data are widely leveraged in vision applications taking advantage of complementary information from different modalities, e.g., brain tumor segmentation from multi-parametric magnetic resonance imaging (MRI). However, due to possible data corruption and different imaging protocols, the availability of images for each domain could vary amongst multiple data sources in practice, which makes it challenging to build a universal model with a varied set of input data. To tackle this problem, we propose a general approach to complete the random missing domain(s) data in real applications. Specifically, we develop a novel multi-domain image completion method that utilizes a generative adversarial network (GAN) with a representational disentanglement scheme to extract shared content encoding and separate style encoding across multiple domains. We further illustrate that the learned representation in multi-domain image completion could be leveraged for high-level tasks, e.g., segmentation, by introducing a unified framework consisting of image completion and segmentation with a shared content encoder. The experiments demonstrate consistent performance improvement on three datasets for brain tumor segmentation, prostate segmentation, and facial expression image completion respectively.

Wasserstein GANs for MR Imaging: From Paired to Unpaired Training

Lack of ground-truth MR images impedes the common supervised training of neural networks for image reconstruction. To cope with this challenge, this article leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset. The reconstruction networks consist of a generator which suppresses the input image artifacts, and a discriminator using a pool of (unpaired) labels to adjust the reconstruction quality. The generator is an unrolled neural network - a cascade of convolutional and data consistency layers. The discriminator is also a multilayer CNN that plays the role of a critic scoring the quality of reconstructed images based on the Wasserstein distance. Our experiments with knee MRI datasets demonstrate that the proposed unpaired training enables diagnostic-quality reconstruction when high-quality image labels are not available for the input types of interest, or when the amount of labels is small. In addition, our adversarial training scheme can achieve better image quality (as rated by expert radiologists) compared with the paired training schemes with pixel-wise loss.

Synthesize High-Quality Multi-Contrast Magnetic Resonance Imaging From Multi-Echo Acquisition Using Multi-Task Deep Generative Model

Multi-echo saturation recovery sequence can provide redundant information to synthesize multi-contrast magnetic resonance imaging. Traditional synthesis methods, such as GE's MAGiC platform, employ a model-fitting approach to generate parameter-weighted contrasts. However, models' over-simplification, as well as imperfections in the acquisition, can lead to undesirable reconstruction artifacts, especially in T2-FLAIR contrast. To improve the image quality, in this study, a multi-task deep learning model is developed to synthesize multi-contrast neuroimaging jointly using both signal relaxation relationships and spatial information. Compared with previous deep learning-based synthesis, the correlation between different destination contrast is utilized to enhance reconstruction quality. To improve model generalizability and evaluate clinical significance, the proposed model was trained and tested on a large multi-center dataset, including healthy subjects and patients with pathology. Results from both quantitative comparison and clinical reader study demonstrate that the multi-task formulation leads to more efficient and accurate contrast synthesis than previous methods.

Rosette Trajectories Enable Ungated, Motion-Robust, Simultaneous Cardiac and Liver T2 * Iron Assessment

BACKGROUND

Quantitative T₂ * MRI is the standard of care for the assessment of iron overload. However, patient motion corrupts T₂ * estimates.

PURPOSE

To develop and evaluate a motion-robust, simultaneous cardiac and liver T₂ * imaging approach using non-Cartesian, rosette sampling and a model-based reconstruction as compared to clinical-standard Cartesian MRI.

STUDY TYPE

Prospective.

PHANTOM/POPULATION

Six ferumoxytol-containing phantoms (26-288 μg/mL). Eight healthy subjects and 18 patients referred for clinically indicated iron overload assessment.

FIELD STRENGTH/SEQUENCE

1.5T, 2D Cartesian and rosette gradient echo (GRE) ASSESSMENT: GRE T₂ * values were validated in ferumoxytol phantoms. In healthy subjects, test-retest and spatial coefficient of variation (CoV) analysis was performed during three breathing conditions. Cartesian and rosette T₂ * were compared using correlation and Bland-Altman analysis. Images were rated by three experienced radiologists on a 5-point scale.

STATISTICAL TESTS

Linear regression, analysis of variance (ANOVA), and paired Student's t-testing were used to compare reproducibility and variability metrics in Cartesian and rosette scans. The Wilcoxon rank test was used to assess reader score comparisons and reader reliability was measured using intraclass correlation analysis.

RESULTS

Rosette R2* (1/T₂ *) was linearly correlated with ferumoxytol concentration (r² = 1.00) and not significantly different than Cartesian values (P = 0.16). During breath-holding, ungated rosette liver and heart T₂ * had lower spatial CoV (liver: 18.4 ± 9.3% Cartesian, 8.8% ± 3.4% rosette, P = 0.02, heart: 37.7% ± 14.3% Cartesian, 13.4% ± 1.7% rosette, P = 0.001) and higher-quality scores (liver: 3.3 [3.0-3.6] Cartesian, 4.7 [4.1-4.9] rosette, P = 0.005, heart: 3.0 [2.3-3] Cartesian, 4.5 [3.8-5.0] rosette, P = 0.005) compared to Cartesian values. During free-breathing and failed breath-holding, Cartesian images had very poor to average image quality with significant artifacts, whereas rosette remained very good, with minimal artifacts (P = 0.001).

DATA CONCLUSION

Rosette k-sampling with a model-based reconstruction offers a clinically useful motion-robust T₂ * mapping approach for iron quantification. J. MAGN. RESON. IMAGING 2020;52:1688-1698.

DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging

Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and 'one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework. In this paper, we propose a novel multi-scale unrolled deep learning framework which learns deep image priors through multi-scale CNN and is combined with unrolled framework to enforce data-consistency and model knowledge. Essentially, this framework combines the best of both learning paradigms:model-based and data-centric learning paradigms. Proposed method is verified using several experiments on numerous data sets.

Compressed Sensing: From Research to Clinical Practice with Deep Neural Networks

Compressed sensing (CS) reconstruction methods leverage sparse structure in underlying signals to recover high-resolution images from highly undersampled measurements. When applied to magnetic resonance imaging (MRI), CS has the potential to dramatically shorten MRI scan times, increase diagnostic value, and improve overall patient experience. However, CS has several shortcomings which limit its clinical translation such as: 1) artifacts arising from inaccurate sparse modelling assumptions, 2) extensive parameter tuning required for each clinical application, and 3) clinically infeasible reconstruction times. Recently, CS has been extended to incorporate deep neural networks as a way of learning complex image priors from historical exam data. Commonly referred to as unrolled neural networks, these techniques have proven to be a compelling and practical approach to address the challenges of sparse CS. In this tutorial, we will review the classical compressed sensing formulation and outline steps needed to transform this formulation into a deep learning-based reconstruction framework. Supplementary open source code in Python will be used to demonstrate this approach with open databases. Further, we will discuss considerations in applying unrolled neural networks in the clinical setting.

Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep learning

BACKGROUND

Current self-calibration and reconstruction methods for wave-encoded single-shot fast spin echo imaging (SSFSE) requires long computational time, especially when high accuracy is needed.

PURPOSE

To develop and investigate the clinical feasibility of data-driven self-calibration and reconstruction of wave-encoded SSFSE imaging for computation time reduction and quality improvement.

STUDY TYPE

Prospective controlled clinical trial.

SUBJECTS

With Institutional Review Board approval, the proposed method was assessed on 29 consecutive adult patients (18 males, 11 females, range, 24-77 years).

FIELD STRENGTH/SEQUENCE

A wave-encoded variable-density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable 3.5× acceleration with full-Fourier acquisitions. Data-driven calibration of wave-encoding point-spread function (PSF) was developed using a trained deep neural network. Data-driven reconstruction was developed with another set of neural networks based on the calibrated wave-encoding PSF. Training of the calibration and reconstruction networks was performed on 15,783 2D wave-encoded SSFSE abdominal images.

ASSESSMENT

Image quality of the proposed data-driven approach was compared independently and blindly with a conventional approach using iterative self-calibration and reconstruction with parallel imaging and compressed sensing by three radiologists on a scale from -2 to 2 for noise, contrast, sharpness, artifacts, and confidence. Computation time of these two approaches was also compared.

STATISTICAL TESTS

Wilcoxon signed-rank tests were used to compare image quality and two-tailed t-tests were used to compare computation time with P values of under 0.05 considered statistically significant.

RESULTS

An average 2.1-fold speedup in computation was achieved using the proposed method. The proposed data-driven self-calibration and reconstruction approach significantly reduced the perceived noise level (mean scores 0.82, P < 0.0001).

DATA CONCLUSION

The proposed data-driven calibration and reconstruction achieved twice faster computation with reduced perceived noise, providing a fast and robust self-calibration and reconstruction for clinical abdominal SSFSE imaging.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:841-853.

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones

PURPOSE

To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.

METHODS

A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.18 ms ± 0.38) and a long readout scan (3.35 ms ± 0.74) at 3 T. Short readout scans, with longer scan times but negligible off-resonance blurring, were used as reference images and augmented with additional off-resonance for supervised training examples. Long readout scans, with greater off-resonance artifacts but shorter scan time, were corrected by autofocus and Off-ResNet and compared with short readout scans by normalized RMS error, structural similarity index, and peak SNR. Scans were also compared by scoring on 8 anatomical features by two radiologists, using analysis of variance with post hoc Tukey's test and two one-sided t-tests. Reader agreement was determined with intraclass correlation.

RESULTS

The total scan time for long readout scans was on average 59.3% shorter than short readout scans. Images from Off-ResNet had superior normalized RMS error, structural similarity index, and peak SNR compared with uncorrected images across ±1 kHz off-resonance (P < .01). The proposed method had superior normalized RMS error over -677 Hz to +1 kHz and superior structural similarity index and peak SNR over ±1 kHz compared with autofocus (P < .01). Radiologic scoring demonstrated that long readout scans corrected with Off-ResNet were noninferior to short readout scans (P < .05).

CONCLUSION

The proposed method can correct off-resonance artifacts from rapid long-readout 3D cones scans to a noninferior image quality compared with diagnostically standard short readout scans.

Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs

Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including 16 male patients and 23 female patients (mean age, 66 years ± 6 and 68 years ± 9, respectively), who underwent simultaneous amyloid (fluorine 18 [18F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed. One hundredth of the raw list-mode PET data were randomly chosen to simulate a low-dose (1%) acquisition. Convolutional neural networks were implemented with low-dose PET and multiple MR images (PET-plus-MR model) or with low-dose PET alone (PET-only) as inputs to predict full-dose PET images. Quality of the synthesized images was evaluated while Bland-Altman plots assessed the agreement of regional standard uptake value ratios (SUVRs) between image types. Two readers scored image quality on a five-point scale (5 = excellent) and determined amyloid status (positive or negative). Statistical analyses were carried out to assess the difference of image quality metrics and reader agreement and to determine confidence intervals (CIs) for reading results. Results The synthesized images (especially from the PET-plus-MR model) showed marked improvement on all quality metrics compared with the low-dose image. All PET-plus-MR images scored 3 or higher, with proportions of images rated greater than 3 similar to those for the full-dose images (-10% difference [eight of 80 readings], 95% CI: -15%, -5%). Accuracy for amyloid status was high (71 of 80 readings [89%]) and similar to intrareader reproducibility of full-dose images (73 of 80 [91%]). The PET-plus-MR model also had the smallest mean and variance for SUVR difference to full-dose images. Conclusion Simultaneously acquired MRI and ultra-low-dose PET data can be used to synthesize full-dose-like amyloid PET images. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Catana in this issue.

Deep Generative Adversarial Neural Networks for Compressive Sensing MRI

Undersampled magnetic resonance image (MRI) reconstruction is typically an ill-posed linear inverse task. The time and resource intensive computations require tradeoffs between accuracy and speed. In addition, state-of-the-art compressed sensing (CS) analytics are not cognizant of the image diagnostic quality. To address these challenges, we propose a novel CS framework that uses generative adversarial networks (GAN) to model the (low-dimensional) manifold of high-quality MR images. Leveraging a mixture of least-squares (LS) GANs and pixel-wise l1/l2 cost, a deep residual network with skip connections is trained as the generator that learns to remove the aliasing artifacts by projecting onto the image manifold. The LSGAN learns the texture details, while the l1/l2 cost suppresses high-frequency noise. A discriminator network, which is a multilayer convolutional neural network (CNN), plays the role of a perceptual cost that is then jointly trained based on high-quality MR images to score the quality of retrieved images. In the operational phase, an initial aliased estimate (e.g., simply obtained by zero-filling) is propagated into the trained generator to output the desired reconstruction. This demands a very low computational overhead. Extensive evaluations are performed on a large contrast-enhanced MR dataset of pediatric patients. Images rated by expert radiologists corroborate that GANCS retrieves higher quality images with improved fine texture details compared with conventional Wavelet-based and dictionary-learning-based CS schemes as well as with deep-learning-based schemes using pixel-wise training. In addition, it offers reconstruction times of under a few milliseconds, which are two orders of magnitude faster than the current state-of-the-art CS-MRI schemes.

Automatically Determining the Confocal Parameters From OCT B-Scans for Quantification of the Attenuation Coefficients

The attenuation coefficient is a relevant biomarker for many diagnostic medical applications. Recently, the Depth-Resolved Confocal (DRC) technique was developed to automatically estimate the attenuation coefficients from Optical Coherence Tomography (OCT) data with pixel-level resolution. However, DRC requires that the confocal function parameters (i.e., focal plane location and apparent Rayleigh range) be known a priori. In this paper, we present the autoConfocal algorithm: a simple, automatic method for estimating those parameters directly from OCT imagery when the focal plane is within the sample. We present autoConfocal+DRC results on phantom data, ex-vivo biological tissue data, and in-vivo clinical data.

Hyperpolarized 13C MRI: Path to Clinical Translation in Oncology

This white paper discusses prospects for advancing hyperpolarization technology to better understand cancer metabolism, identify current obstacles to HP (hyperpolarized) 13C magnetic resonance imaging's (MRI's) widespread clinical use, and provide recommendations for overcoming them. Since the publication of the first NIH white paper on hyperpolarized 13C MRI in 2011, preclinical studies involving [1-13C]pyruvate as well a number of other 13C labeled metabolic substrates have demonstrated this technology's capacity to provide unique metabolic information. A dose-ranging study of HP [1-13C]pyruvate in patients with prostate cancer established safety and feasibility of this technique. Additional studies are ongoing in prostate, brain, breast, liver, cervical, and ovarian cancer. Technology for generating and delivering hyperpolarized agents has evolved, and new MR data acquisition sequences and improved MRI hardware have been developed. It will be important to continue investigation and development of existing and new probes in animal models. Improved polarization technology, efficient radiofrequency coils, and reliable pulse sequences are all important objectives to enable exploration of the technology in healthy control subjects and patient populations. It will be critical to determine how HP 13C MRI might fill existing needs in current clinical research and practice, and complement existing metabolic imaging modalities. Financial sponsorship and integration of academia, industry, and government efforts will be important factors in translating the technology for clinical research in oncology. This white paper is intended to provide recommendations with this goal in mind.

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks

Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.25 times acceleration in 157 patients referred for abdominal imaging (mean age, 11 years; range, 1-34 years; 72 males [mean age, 10 years; range, 1-26 years] and 85 females [mean age, 12 years; range, 1-34 years]) between March 2016 and April 2017. A VN was trained based on the parallel imaging and compressed sensing (PICS) reconstruction of 130 patients. The remaining 27 patients were used for evaluation. Image quality was evaluated in an independent blinded fashion by three radiologists in terms of overall image quality, perceived signal-to-noise ratio, image contrast, sharpness, and residual artifacts with scores ranging from 1 (nondiagnostic) to 5 (excellent). Wilcoxon tests were performed to test the hypothesis that there was no significant difference between VN and PICS. Results VN achieved improved perceived signal-to-noise ratio (P = .01) and improved sharpness (P < .001), with no difference in image contrast (P = .24) and residual artifacts (P = .07). In terms of overall image quality, VN performed better than did PICS (P = .02). Average reconstruction time ± standard deviation was 5.60 seconds ± 1.30 per section for PICS and 0.19 second ± 0.04 per section for VN. Conclusion Compared with the conventional parallel imaging and compressed sensing reconstruction (PICS), the variational network (VN) approach accelerates the reconstruction of variable-density single-shot fast spin-echo sequences and achieves improved overall image quality with higher perceived signal-to-noise ratio and sharpness. © RSNA, 2018 Online supplemental material is available for this article.

ISLES 2016 and 2017-Benchmarking Ischemic Stroke Lesion Outcome Prediction Based on Multispectral MRI

Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability. ISLES 2016 and 2017 focused on lesion outcome prediction after ischemic stroke: By providing a uniformly pre-processed data set, researchers from all over the world could apply their algorithm directly. A total of nine teams participated in ISLES 2015, and 15 teams participated in ISLES 2016. Their performance was evaluated in a fair and transparent way to identify the state-of-the-art among all submissions. Top ranked teams almost always employed deep learning tools, which were predominately convolutional neural networks (CNNs). Despite the great efforts, lesion outcome prediction persists challenging. The annotated data set remains publicly available and new approaches can be compared directly via the online evaluation system, serving as a continuing benchmark (www.isles-challenge.org).

Body diffusion-weighted imaging using magnetization prepared single-shot fast spin echo and extended parallel imaging signal averaging

PURPOSE

This work demonstrates a magnetization prepared diffusion-weighted single-shot fast spin echo (SS-FSE) pulse sequence for the application of body imaging to improve robustness to geometric distortion. This work also proposes a scan averaging technique that is superior to magnitude averaging and is not subject to artifacts due to object phase.

THEORY AND METHODS

This single-shot sequence is robust against violation of the Carr-Purcell-Meiboom-Gill (CPMG) condition. This is achieved by dephasing the signal after diffusion weighting and tipping the MG component of the signal onto the longitudinal axis while the non-MG component is spoiled. The MG signal component is then excited and captured using a traditional SS-FSE sequence, although the echo needs to be recalled prior to each echo. Extended Parallel Imaging (ExtPI) averaging is used where coil sensitivities from the multiple acquisitions are concatenated into one large parallel imaging (PI) problem. The size of the PI problem is reduced by SVD-based coil compression which also provides background noise suppression. This sequence and reconstruction are evaluated in simulation, phantom scans, and in vivo abdominal clinical cases.

RESULTS

Simulations show that the sequence generates a stable signal throughout the echo train which leads to good image quality. This sequence is inherently low-SNR, but much of the SNR can be regained through scan averaging and the proposed ExtPI reconstruction. In vivo results show that the proposed method is able to provide diffusion encoded images while mitigating geometric distortion artifacts compared to EPI.

CONCLUSION

This work presents a diffusion-prepared SS-FSE sequence that is robust against the violation of the CPMG condition while providing diffusion contrast in clinical cases. Magn Reson Med 79:3032-3044, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Technique development of 3D dynamic CS-EPSI for hyperpolarized 13 C pyruvate MR molecular imaging of human prostate cancer

PURPOSE

The purpose of this study was to develop a new 3D dynamic carbon-13 compressed sensing echoplanar spectroscopic imaging (EPSI) MR sequence and test it in phantoms, animal models, and then in prostate cancer patients to image the metabolic conversion of hyperpolarized [1-¹³ C]pyruvate to [1-¹³ C]lactate with whole gland coverage at high spatial and temporal resolution.

METHODS

A 3D dynamic compressed sensing (CS)-EPSI sequence with spectral-spatial excitation was designed to meet the required spatial coverage, time and spatial resolution, and RF limitations of the 3T MR scanner for its clinical translation for prostate cancer patient imaging. After phantom testing, animal studies were performed in rats and transgenic mice with prostate cancers. For patient studies, a GE SPINlab polarizer (GE Healthcare, Waukesha, WI) was used to produce hyperpolarized sterile GMP [1-¹³ C]pyruvate. 3D dynamic ¹³ C CS-EPSI data were acquired starting 5 s after injection throughout the gland with a spatial resolution of 0.5 cm³ , 18 time frames, 2-s temporal resolution, and 36 s total acquisition time.

RESULTS

Through preclinical testing, the 3D CS-EPSI sequence developed in this project was shown to provide the desired spectral, temporal, and spatial 5D HP ¹³ C MR data. In human studies, the 3D dynamic HP CS-EPSI approach provided first-ever simultaneously volumetric and dynamic images of the LDH-catalyzed conversion of [1-¹³ C]pyruvate to [1-¹³ C]lactate in a biopsy-proven prostate cancer patient with full gland coverage.

CONCLUSION

The results demonstrate the feasibility to characterize prostate cancer metabolism in animals, and now patients using this new 3D dynamic HP MR technique to measure k_PL , the kinetic rate constant of [1-¹³ C]pyruvate to [1-¹³ C]lactate conversion.

Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI

BACKGROUND

There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration.

PURPOSE

To reduce gadolinium dose in contrast-enhanced brain MRI using a deep learning method.

STUDY TYPE

Retrospective, crossover.

POPULATION

Sixty patients receiving clinically indicated contrast-enhanced brain MRI.

SEQUENCE

3D T₁ -weighted inversion-recovery prepped fast-spoiled-gradient-echo (IR-FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR-FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low-dose (0.01mmol/kg) and 100% full-dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full-dose images from the precontrast and low-dose images. Synthesized full-dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma.

ASSESSMENT

For both test sets, low-dose, true full-dose, and the synthesized full-dose postcontrast image sets were compared quantitatively using peak-signal-to-noise-ratios (PSNR) and structural-similarity-index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion-artifact suppression, and contrast enhancement compared with precontrast images.

STATISTICAL ANALYSIS

Results were assessed using paired t-tests and noninferiority tests.

RESULTS

The proposed deep learning method yielded significant (n = 50, P < 0.001) improvements over the low-dose images (>5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P < 0.001) were significantly increased. Compared to true full-dose images, the synthesized full-dose images have a slight but not significant reduction in image quality (n = 20, P = 0.083) and contrast enhancement (n = 20, P = 0.068). Slightly better (n = 20, P = 0.039) motion-artifact suppression was noted in the synthesized images. The noninferiority test rejects the inferiority of the synthesized to true full-dose images for image quality (95% CI: -14-9%), artifacts suppression (95% CI: -5-20%), and contrast enhancement (95% CI: -13-6%).

DATA CONCLUSION

With the proposed deep learning method, gadolinium dose can be reduced 10-fold while preserving contrast information and avoiding significant image quality degradation.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 5 J. MAGN. RESON. IMAGING 2018;48:330-340.

Thermo-Acoustic Ultrasound for Detection of RF-Induced Device Lead Heating in MRI

Patients who have implanted medical devices with long conductive leads are often restricted from receiving MRI scans due to the danger of RF-induced heating near the lead tips. Phantom studies have shown that this heating varies significantly on a case-by-case basis, indicating that many patients with implanted devices can receive clinically useful MRI scans without harm. However, the difficulty of predicting RF-induced lead tip heating prior to scanning prevents numerous implant recipients from being scanned. Here, we demonstrate that thermo-acoustic ultrasound (TAUS) has the potential to be utilized for a pre-scan procedure assessing the risk of RF-induced lead tip heating in MRI. A system was developed to detect TAUS signals by four different TAUS acquisition methods. We then integrated this system with an MRI scanner and detected a peak in RF power absorption near the tip of a model lead when transmitting from the scanner's body coil. We also developed and experimentally validated simulations to characterize the thermo-acoustic signal generated near lead tips. These results indicate that TAUS is a promising method for assessing RF implant safety, and with further development, a TAUS pre-scan could allow many more patients to have access to MRI scans of significant clinical value.

Robust Self-Calibrating nCPMG Acquisition: Application to Body Diffusion-Weighted Imaging

This paper demonstrates a robust diffusion-weighted single-shot fast spin echo (SS-FSE) sequence in the presence of significant off-resonance, which includes a variable-density acquisition and a self-calibrated reconstruction as improvements. A non-Carr-Purcell-Meiboom-Gill (nCPMG) SS-FSE acquisition stabilizes both the main and parasitic echo families for each echo. This preserves both the in-phase and quadrature components of the magnetization throughout the echo train. However, nCPMG SS-FSE also promotes aliasing of the quadrature component, which complicates reconstruction. A new acquisition and reconstruction approach is presented here, where the field-of-view is effectively doubled, but a partial k-space and variable density sampling is used to improve scan efficiency. The technique is presented in phantom scans to validate SNR and robustness against rapidly varying object phase. In vivo healthy volunteer examples and the clinical cases are demonstrated in abdominal imaging. This new approach provides comparable SNR to previous nCPMG acquisition techniques as well as providing more uniform apparent diffusion coefficient maps in phantom scans. In vivo scans suggest that this method is more robust against motion than previous approaches. The proposed reconstruction is an improvement to the nCPMG sequence as it is auto-calibrating and is justified to accurately treat the signal model for the nCPMG SS-FSE sequence.

Autocalibrating motion-corrected wave-encoding for highly accelerated free-breathing abdominal MRI

PURPOSE

To develop a motion-robust wave-encoding technique for highly accelerated free-breathing abdominal MRI.

METHODS

A comprehensive 3D wave-encoding-based method was developed to enable fast free-breathing abdominal imaging: (a) auto-calibration for wave-encoding was designed to avoid extra scan for coil sensitivity measurement; (b) intrinsic butterfly navigators were used to track respiratory motion; (c) variable-density sampling was included to enable compressed sensing; (d) golden-angle radial-Cartesian hybrid view-ordering was incorporated to improve motion robustness; and (e) localized rigid motion correction was combined with parallel imaging compressed sensing reconstruction to reconstruct the highly accelerated wave-encoded datasets. The proposed method was tested on six subjects and image quality was compared with standard accelerated Cartesian acquisition both with and without respiratory triggering. Inverse gradient entropy and normalized gradient squared metrics were calculated, testing whether image quality was improved using paired t-tests.

RESULTS

For respiratory-triggered scans, wave-encoding significantly reduced residual aliasing and blurring compared with standard Cartesian acquisition (metrics suggesting P < 0.05). For non-respiratory-triggered scans, the proposed method yielded significantly better motion correction compared with standard motion-corrected Cartesian acquisition (metrics suggesting P < 0.01).

CONCLUSION

The proposed methods can reduce motion artifacts and improve overall image quality of highly accelerated free-breathing abdominal MRI. Magn Reson Med 78:1757-1766, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Frequency shifting reduces but does not eliminate acoustic interference between echolocating bats: A theoretical analysis

Bats have been observed to shift the frequency of their echolocation calls in the presence of other echolocating bats, ostensibly as a way to reduce acoustic interference. Few studies, however, have examined the theoretical efficacy of such jamming avoidance responses. The present study uses the wideband ambiguity function to analyze the effects of acoustic interference from conspecifics and congeneric heterospecifics on the target acquisition ability of Myotis californicus and Myotis yumanensis, specifically whether unilateral or bilateral frequency shifts reduce the effects of such interference. Model results suggest that in conspecific interactions, M. yumanensis recovers its target acquisition ability more completely and with less absolute frequency shift than does M. californicus, but that alternative methods of jamming avoidance may be easier to implement. The optimal strategy for reducing heterospecific interference is for M. californicus to downshift its call and M. yumanensis to upshift its call, which exaggerates a preexisting difference in mean frequency between the calls of the two species. Further empirical research would elucidate whether these species do in practice actively employ frequency shifting or other means for jamming avoidance, as well as illuminate the role of acoustic interference in niche partitioning.

Rapid compressed sensing reconstruction of 3D non-Cartesian MRI

PURPOSE

Conventional non-Cartesian compressed sensing requires multiple nonuniform Fourier transforms every iteration, which is computationally expensive. Accordingly, time-consuming reconstructions have slowed the adoption of undersampled 3D non-Cartesian acquisitions into clinical protocols. In this work we investigate several approaches to minimize reconstruction times without sacrificing accuracy.

METHODS

The reconstruction problem can be reformatted to exploit the Toeplitz structure of matrices that are evaluated every iteration, but it requires larger oversampling than what is strictly required by nonuniform Fourier transforms. Accordingly, we investigate relative speeds of the two approaches for various nonuniform Fourier transform kernel sizes and oversampling for both GPU and CPU implementations. Second, we introduce a method to minimize matrix sizes by estimating the image support. Finally, density compensation weights have been used as a preconditioning matrix to improve convergence, but this increases noise. We propose a more general approach to preconditioning that allows a trade-off between accuracy and convergence speed.

RESULTS

When using a GPU, the Toeplitz approach was faster for all practical parameters. Second, it was found that properly accounting for image support can prevent aliasing errors with minimal impact on reconstruction time. Third, the proposed preconditioning scheme improved convergence rates by an order of magnitude with negligible impact on noise.

CONCLUSION

With the proposed methods, 3D non-Cartesian compressed sensing with clinically relevant reconstruction times (<2 min) is feasible using practical computer resources. Magn Reson Med 79:2685-2692, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Self-Calibrating Wave-Encoded Variable-Density Single-Shot Fast Spin Echo Imaging

BACKGROUND

It is highly desirable in clinical abdominal MR scans to accelerate single-shot fast spin echo (SSFSE) imaging and reduce blurring due to T₂ decay and partial-Fourier acquisition.

PURPOSE

To develop and investigate the clinical feasibility of wave-encoded variable-density SSFSE imaging for improved image quality and scan time reduction.

STUDY TYPE

Prospective controlled clinical trial.

SUBJECTS

With Institutional Review Board approval and informed consent, the proposed method was assessed on 20 consecutive adult patients (10 male, 10 female, range, 24-84 years).

FIELD STRENGTH/SEQUENCE

A wave-encoded variable-density SSFSE sequence was developed for clinical 3.0T abdominal scans to enable high acceleration (3.5×) with full-Fourier acquisitions by: 1) introducing wave encoding with self-refocusing gradient waveforms to improve acquisition efficiency; 2) developing self-calibrated estimation of wave-encoding point-spread function and coil sensitivity to improve motion robustness; and 3) incorporating a parallel imaging and compressed sensing reconstruction to reconstruct highly accelerated datasets.

ASSESSMENT

Image quality was compared pairwise with standard Cartesian acquisition independently and blindly by two radiologists on a scale from -2 to 2 for noise, contrast, confidence, sharpness, and artifacts. The average ratio of scan time between these two approaches was also compared.

STATISTICAL TESTS

A Wilcoxon signed-rank tests with a P value under 0.05 considered statistically significant.

RESULTS

Wave-encoded variable-density SSFSE significantly reduced the perceived noise level and improved the sharpness of the abdominal wall and the kidneys compared with standard acquisition (mean scores 0.8, 1.2, and 0.8, respectively, P < 0.003). No significant difference was observed in relation to other features (P = 0.11). An average of 21% decrease in scan time was achieved using the proposed method.

DATA CONCLUSION

Wave-encoded variable-density sampling SSFSE achieves improved image quality with clinically relevant echo time and reduced scan time, thus providing a fast and robust approach for clinical SSFSE imaging.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2018;47:954-966.

Slice profile effects on nCPMG SS-FSE

PURPOSE

To determine the effects of the RF refocusing pulse profile on the magnitude of the transverse signal smoothness throughout the echo train in non-Carr-Purcell-Meiboom-Gill (nCPMG) single-shot fast spin echo (SS-FSE) imaging and to design an RF refocusing pulse that provides improved signal stability. THEORY AND METHODS: nCPMG SS-FSE quadratic phase modulation requires sufficiently high and uniform refocusing flip angle to achieve a stable signal. Typically, refocusing pulses used in SS-FSE sequences are designed for minimum duration to minimize echo spacing and as a consequence have poor selectivity. However, delay-insensitive variable rate excitation Shinnar-Le Roux (DV-SLR) refocusing pulses can achieve both improved selectivity as well as a short duration. This class of RF pulse is compared against a traditional low time-bandwidth refocusing pulse in a nCPMG SS-FSE in simulation, phantom, and in vivo.

RESULTS

DV-SLR pulses achieve a more stable signal in simulation, phantom, and in vivo cases while maintaining an appropriately short duration as well as not dramatically increasing specific absorption rate (SAR) accumulation.

CONCLUSION

The nCPMG SS-FSE method demonstrates improved robustness when a more selective refocusing pulse is used. Refocusing pulses that use a time-varying excitation gradient can achieve this selectivity while maintaining short echo spacing. Magn Reson Med 79:430-438, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Formulation of image fusion as a constrained least squares optimization problem

Fusing a lower resolution color image with a higher resolution monochrome image is a common practice in medical imaging. By incorporating spatial context and/or improving the signal-to-noise ratio, it provides clinicians with a single frame of the most complete information for diagnosis. In this paper, image fusion is formulated as a convex optimization problem that avoids image decomposition and permits operations at the pixel level. This results in a highly efficient and embarrassingly parallelizable algorithm based on widely available robust and simple numerical methods that realizes the fused image as the global minimizer of the convex optimization problem.

Body Diffusion Weighted Imaging Using Non-CPMG Fast Spin Echo

SS-FSE is a fast technique that does not suffer from off-resonance distortions to the degree that EPI does. Unlike EPI, SS-FSE is ill-suited to diffusion weighted imaging (DWI) due to the Carr-Purcell-Meiboom-Geill (CPMG) condition. Non-CPMG phase cycling does accommodate SS-FSE and DWI but places constraints on reconstruction, which are resolved here through parallel imaging. Additionally, improved echo stability can be achieved by using short duration and highly selective DIVERSE radiofrequency pulses. Here, signal-to-noise ratio (SNR) comparisons between EPI and nCPMG SS-FSE acquisitions and reconstruction techniques give similar values. Diffusion imaging with nCPMG SS-FSE gives similar SNR to an EPI acquisition, though apparent diffusion coefficient values are higher than seen with EPI. In vivo images have good image quality with little distortion. This method has the ability to capture distortion-free DWI images near areas of significant off-resonance as well as preserve adequate SNR. Parallel imaging and DIVERSE refocusing RF pulses allow shorter ETL compared to previous implementations and thus reduces phase encode direction blur and SAR accumulation.

A Millimeter-Wave Digital Link for Wireless MRI

A millimeter (mm) wave radio is presented in this work to support wireless MRI data transmission. High path loss and availability of wide bandwidth make mm-waves an ideal candidate for short range, high data rata communication required for wireless MRI. The proposed system uses a custom designed integrated chip (IC) mm-wave radio with 60 GHz as radio frequency carrier. In this work, we assess performance in a 1.5 T MRI field, with the addition of optical links between the console room and magnet. The system uses ON-OFF keying (OOK) modulation for data transmission and supports data rates from 200 Mb/s to 2.5 Gb/s for distances up-to 65 cm. The presence of highly directional, linearly polarized, on-chip dipole antennas on the mm-wave radio along with the time division multiplexing (TDM) circuitry allows multiple wireless links to be created simultaneously with minimal inter-channel interference. This leads to a highly scalable solution for wireless MRI.

Depletion-Mode GaN HEMT Q-Spoil Switches for MRI Coils

Q-spoiling is the process of decoupling an MRI receive coil to protect the equipment and patient. Conventionally, Q-spoiling is performed using a PIN diode switch that draws significant current. In this work, a Q-spoiling technique using a depletion-mode Gallium Nitride HEMT device was developed for coil detuning at both 1.5 T and 3 T MRI. The circuits with conventional PIN diode Q-spoiling and the GaN HEMT device were implemented on surface coils. SNR was measured and compared for all surfaces coils. At both 1.5 T and 3 T, comparable SNR was achieved for all coils with the proposed technique and conventional Q-spoiling. The GaN HEMT device has significantly reduced the required power for Q-spoiling. The GaN HEMT device also provides useful safety features by detuning the coil when unpowered.

Spectrally selective three-dimensional dynamic balanced steady-state free precession for hyperpolarized C-13 metabolic imaging with spectrally selective radiofrequency pulses

PURPOSE

Balanced steady-state free precession (bSSFP) sequences can provide superior signal-to-noise ratio efficiency for hyperpolarized (HP) carbon-13 (¹³ C) magnetic resonance imaging by efficiently utilizing the nonrecoverable magnetization, but managing their spectral response is challenging in the context of metabolic imaging. A new spectrally selective bSSFP sequence was developed for fast imaging of multiple HP ¹³ C metabolites with high spatiotemporal resolution.

THEORY AND METHODS

This novel approach for bSSFP spectral selectivity incorporates optimized short-duration spectrally selective radiofrequency pulses within a bSSFP pulse train and a carefully chosen repetition time to avoid banding artifacts.

RESULTS

The sequence enabled subsecond 3D dynamic spectrally selective imaging of ¹³ C metabolites of copolarized [1-¹³ C]pyruvate and [¹³ C]urea at 2-mm isotropic resolution, with excellent spectral selectivity (∼100:1). The sequence was successfully tested in phantom studies and in vivo studies with normal mice.

CONCLUSION

This sequence is expected to benefit applications requiring dynamic volumetric imaging of metabolically active ¹³ C compounds at high spatiotemporal resolution, including preclinical studies at high field and, potentially, clinical studies. Magn Reson Med 78:963-975, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Hybrid-Space SENSE Reconstruction for Simultaneous Multi-Slice MRI

Simultaneous Multi-Slice (SMS) magnetic resonance imaging (MRI) is a rapidly evolving technique for increasing imaging speed. Controlled aliasing techniques utilize periodic undersampling patterns to help mitigate the loss in signal-to-noise ratio (SNR) in SMS MRI. To evaluate the performance of different undersampling patterns, a quantitative description of the image SNR loss is needed. Additionally, eddy current effects in echo planar imaging (EPI) lead to slice-specific Nyquist ghosting artifacts. These artifacts cannot be accurately corrected for each individual slice before or after slice-unaliasing. In this work, we propose a hybrid-space sensitivity encoding (SENSE) reconstruction framework for SMS MRI by adopting a three-dimensional representation of the SMS acquisition. Analytical SNR loss maps are derived for SMS acquisitions with arbitrary phase encoding undersampling patterns. Moreover, we propose a matrix-decoding correction method that corrects the slice-specific Nyquist ghosting artifacts in SMS EPI acquisitions. Brain images demonstrate that the proposed hybrid-space SENSE reconstruction generates images with comparable quality to commonly used split-slice-generalized autocalibrating partially parallel acquisition reconstruction. The analytical SNR loss maps agree with those calculated by a Monte Carlo based method, but require less computation time for high quality maps. The analytical maps enable a fair comparison between the performances of coherent and incoherent SMS undersampling patterns. Phantom and brain SMS EPI images show that the matrix-decoding method performs better than the single-slice and slice-averaged Nyquist ghosting correction methods under the hybrid-space SENSE reconstruction framework.

Resolving phase ambiguity in dual-echo dixon imaging using a projected power method

PURPOSE

To develop a fast and robust method to resolve phase ambiguity in dual-echo Dixon imaging.

METHODS

A major challenge in dual-echo Dixon imaging is to estimate the phase error resulting from field inhomogeneity. In this work, a binary quadratic optimization program was formulated to resolve the phase ambiguity. A projected power method was developed to efficiently solve the optimization problem. Both the 1-peak fat model and 6-peak fat model were applied to three-dimensional (3D) datasets. Additionally, the proposed method was extended to dynamic magnetic resonance imaging (MRI) applications using the 6-peak fat model. With institutional review board (IRB) approval and patient consent/assent, the proposed method was evaluated and compared with region growing on 29 consecutive 3D high-resolution patient datasets.

RESULTS

Fast and robust water/fat separation was achieved by the proposed method in different representative 3D datasets and dynamic 3D datasets. Superior water/fat separation was achieved using the 6-peak fat model compared with the 1-peak fat model. Compared to region growing, the proposed method reduced water/fat swaps from 76 to 7% of the patient cohort.

CONCLUSION

The proposed method can achieve fast and robust phase error estimation in dual-echo Dixon imaging. Magn Reson Med 77:2066-2076, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Improved cortical bone specificity in UTE MR Imaging

PURPOSE

Methods for direct visualization of compact bone using MRI have application in several "MR-informed" technologies, such as MR-guided focused ultrasound, MR-PET reconstruction and MR-guided radiation therapy. The specificity of bone imaging can be improved by manipulating image sensitivity to Bloch relaxation phenomena, facilitating distinction of bone from other tissues detected by MRI.

METHODS

From Bloch equation dynamics, excitation pulses suitable for creating specific sensitivity to short-T₂ magnetization from cortical bone are identified. These pulses are used with UTE subtraction demonstrate feasibility of MR imaging of compact bone with positive contrast.

RESULTS

MR images of bone structures are acquired with contrast similar to that observed in x-ray CT images. Through comparison of MR signal intensities with CT Hounsfield units of the skull, the similarity of contrast is quantified. The MR technique is also demonstrated in other regions of the body that are relevant for interventional procedures, such as the shoulder, pelvis and leg.

CONCLUSION

Matching RF excitation pulses to relaxation rates improves the specificity to bone of short-T₂ contrast. It is demonstrated with a UTE sequence to acquire images of cortical bone with positive contrast, and the contrast is verified by comparison with x-ray CT. Magn Reson Med 77:684-695, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Multiband RF pulses with improved performance via convex optimization

Selective RF pulses are commonly designed with the desired profile as a low pass filter frequency response. However, for many MRI and NMR applications, the spectrum is sparse with signals existing at a few discrete resonant frequencies. By specifying a multiband profile and releasing the constraint on "don't-care" regions, the RF pulse performance can be improved to enable a shorter duration, sharper transition, or lower peak B1 amplitude. In this project, a framework for designing multiband RF pulses with improved performance was developed based on the Shinnar-Le Roux (SLR) algorithm and convex optimization. It can create several types of RF pulses with multiband magnitude profiles, arbitrary phase profiles and generalized flip angles. The advantage of this framework with a convex optimization approach is the flexible trade-off of different pulse characteristics. Designs for specialized selective RF pulses for balanced SSFP hyperpolarized (HP) (13)C MRI, a dualband saturation RF pulse for (1)H MR spectroscopy, and a pre-saturation pulse for HP (13)C study were developed and tested.

Development and testing of hyperpolarized (13)C MR calibrationless parallel imaging

A calibrationless parallel imaging technique developed previously for (1)H MRI was modified and tested for hyperpolarized (13)C MRI for applications requiring large FOV and high spatial resolution. The technique was demonstrated with both retrospective and prospective under-sampled data acquired in phantom and in vivo rat studies. A 2-fold acceleration was achieved using a 2D symmetric EPI readout equipped with random blips on the phase encode dimension. Reconstructed images showed excellent qualitative agreement with fully sampled data. Further acceleration can be achieved using acquisition schemes that incorporate multi-dimensional under-sampling.

Comprehensive motion-compensated highly accelerated 4D flow MRI with ferumoxytol enhancement for pediatric congenital heart disease

PURPOSE

To develop and evaluate motion-compensation and compressed-sensing techniques in 4D flow MRI for anatomical assessment in a comprehensive ferumoxytol-enhanced congenital heart disease (CHD) exam.

MATERIALS AND METHODS

A Cartesian 4D flow sequence was developed to enable intrinsic navigation and two variable-density sampling schemes: VDPoisson and VDRad. Four compressed-sensing methods were developed: A) VDPoisson scan reconstructed using spatial wavelets; B) added temporal total variation to A; C) VDRad scan using the same reconstruction as in B; and D) added motion compensation to C. With Institutional Review Board (IRB) approval and Health Insurance Portability and Accountability Act (HIPAA) compliance, 23 consecutive patients (eight females, mean 6.3 years) referred for ferumoxytol-enhanced CHD 3T MRI were recruited. Images were acquired and reconstructed using methods A-D. Two cardiovascular radiologists independently scored the images on a 5-point scale. These readers performed a paired wall motion and functional assessment between method D and 2D balanced steady-state free precession (bSSFP) CINE for 16 cases.

RESULTS

Method D had higher diagnostic image quality for most anatomical features (mean 3.8-4.8) compared to A (2.0-3.6), B (2.2-3.7), and C (2.9-3.9) with P < 0.05 with good interobserver agreement (κ ≥ 0.49). Method D had similar or better assessment of myocardial borders and cardiac motion compared to 2D bSSFP (P < 0.05, κ ≥ 0.77). All methods had good internal agreement in comparing aortic with pulmonic flow (BA mean < 0.02%, r > 0.85) and compared to method A (BA mean < 0.13%, r > 0.84) with P < 0.01.

CONCLUSION

Flow, functional, and anatomical assessment in CHD with ferumoxytol-enhanced 4D flow is feasible and can be significantly improved using motion compensation and compressed sensing. J. Magn. Reson. Imaging 2016;43:1355-1368.

Automated, Depth-Resolved Estimation of the Attenuation Coefficient From Optical Coherence Tomography Data

We present a method for automated, depth-resolved extraction of the attenuation coefficient from Optical Coherence Tomography (OCT) data. In contrast to previous automated, depth-resolved methods, the Depth-Resolved Confocal (DRC) technique derives an invertible mapping between the measured OCT intensity data and the attenuation coefficient while considering the confocal function and sensitivity fall-off, which are critical to ensure accurate measurements of the attenuation coefficient in practical settings (e.g., clinical endoscopy). We also show that further improvement of the estimated attenuation coefficient is possible by formulating image denoising as a convex optimization problem that we term Intensity Weighted Horizontal Total Variation (iwhTV). The performance and accuracy of DRC alone and DRC+iwhTV are validated with simulated data, optical phantoms, and ex-vivo porcine tissue. Our results suggest that implementation of DRC+iwhTV represents a novel way to improve OCT contrast for better tissue characterization through quantitative imaging.

ESPIRiT--an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA

PURPOSE

Parallel imaging allows the reconstruction of images from undersampled multicoil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k-space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm.

THEORY AND METHODS

A theoretical analysis shows: (1) The correlations in k-space are encoded in the null space of a calibration matrix. (2) Both approaches restrict the solution to a subspace spanned by the sensitivities. (3) The sensitivities appear as the main eigenvector of a reconstruction operator computed from the null space. The basic assumptions and the quality of the sensitivity maps are evaluated in experimental examples. The appearance of additional eigenvectors motivates an extended SENSE reconstruction with multiple maps, which is compared to existing methods.

RESULTS

The existence of a null space and the high quality of the extracted sensitivities are confirmed. The extended reconstruction combines all advantages of SENSE with robustness to certain errors similar to GRAPPA.

CONCLUSION

In this article the gap between both approaches is finally bridged. A new autocalibration technique combines the benefits of both.

Chemical shift separation with controlled aliasing for hyperpolarized (13) C metabolic imaging

PURPOSE

A chemical shift separation technique for hyperpolarized (13) C metabolic imaging with high spatial and temporal resolution was developed. Specifically, a fast three-dimensional pulse sequence and a reconstruction method were implemented to acquire signals from multiple (13) C species simultaneously with subsequent separation into individual images.

THEORY AND METHODS

A stack of flyback echo-planar imaging readouts and a set of multiband excitation radiofrequency pulses were designed to spatially modulate aliasing patterns of the acquired metabolite images, which translated the chemical shift separation problem into parallel imaging reconstruction problem. An eight-channel coil array was used for data acquisition and a parallel imaging method based on nonlinear inversion was developed to separate the aliased images.

RESULTS

Simultaneous acquisitions of pyruvate and lactate in a phantom study and in vivo rat experiments were performed. The results demonstrated successful separation of the metabolite distributions into individual images having high spatial resolution.

CONCLUSION

This method demonstrated the ability to provide accelerated metabolite imaging in hyperpolarized (13) C MR using multichannel coils, tailored readout, and specialized RF pulses.