Magnetic Resonance Fingerprinting with compressed sensing and distance metric learning

Magnetic Resonance Fingerprinting (MRF) is a novel technique that simultaneously estimates multiple tissue-related parameters, such as the longitudinal relaxation time T1, the transverse relaxation time T2, off resonance frequency B0 and proton density, from a scanned object in just tens of seconds. However, the MRF method suffers from aliasing artifacts because it significantly undersamples the k-space data. In this work, we propose a compressed sensing (CS) framework for simultaneously estimating multiple tissue-related parameters based on the MRF method. It is more robust to low sampling ratio and is therefore more efficient in estimating MR parameters for all voxels of an object. Furthermore, the MRF method requires identifying the nearest atoms of the query fingerprints from the MR-signal-evolution dictionary with the L2 distance. However, we observed that the L2 distance is not always a proper metric to measure the similarities between MR Fingerprints. Adaptively learning a distance metric from the undersampled training data can significantly improve the matching accuracy of the query fingerprints. Numerical results on extensive simulated cases show that our method substantially outperforms stateof-the-art methods in terms of accuracy of parameter estimation

HYDRA: Hybrid Deep Magnetic Resonance Fingerprinting

Purpose: Magnetic resonance fingerprinting (MRF) methods typically rely on dictio-nary matching to map the temporal MRF signals to quantitative tissue parameters. Such approaches suffer from inherent discretization errors, as well as high computational complexity as the dictionary size grows. To alleviate these issues, we propose a HYbrid Deep magnetic ResonAnce fingerprinting approach, referred to as HYDRA. Methods: HYDRA involves two stages: a model-based signature restoration phase and a learning-based parameter restoration phase. Signal restoration is implemented using low-rank based de-aliasing techniques while parameter restoration is performed using a deep nonlocal residual convolutional neural network. The designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. In test mode, it takes a temporal MRF signal as input and produces the corresponding tissue parameters. Results: We validated our approach on both synthetic data and anatomical data generated from a healthy subject. The results demonstrate that, in contrast to conventional dictionary-matching based MRF techniques, our approach significantly improves inference speed by eliminating the time-consuming dictionary matching operation, and alleviates discretization errors by outputting continuous-valued parameters. We further avoid the need to store a large dictionary, thus reducing memory requirements. Conclusions: Our approach demonstrates advantages in terms of inference speed, accuracy and storage requirements over competing MRF method

Magnetic Resonance Fingerprinting Using a Residual Convolutional Neural Network

Conventional dictionary matching based MR Fingerprinting (MRF) reconstruction approaches suffer from time-consuming operations that map temporal MRF signals to quantitative tissue parameters. In this paper, we design a 1-D residual convolutional neural network to perform the signature-to-parameter mapping in order to improve inference speed and accuracy. In particular, a 1-D convolutional neural network with shortcuts, a.k.a skip connections, for residual learning is developed using a TensorFlow platform. To avoid the requirement for a large amount of MRF data, the designed network is trained on synthesized MRF data simulated with the Bloch equations and fast imaging with steady state precession (FISP) sequences. The proposed approach was validated on both synthetic data and phantom data generated from a healthy subject. The reconstruction performance demonstrates a significantly improved speed - only 1.6s for reconstructing a pair of T1/T2 maps of size 128 × 128 - 50× faster than the original dictionary matching based method. The better performance was also confirmed by improved signal to noise ratio (SNR) and reduced root mean square error (RMSE). Furthermore, it is more compact to store a network instead of a large dictionary

Machine learning in Magnetic Resonance Imaging: Image reconstruction.

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction. A wide range of approaches have been proposed, which can be applied in k-space and/or image-space. Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation. In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends

MR Shuffling: Accelerated Single-Scan Multi-Contrast Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is an attractive medical imaging modality as it is non-invasive and does not involve ionizing radiation. Routine clinical MRI exams obtain MR images corresponding to different soft tissue contrast by performing multiple scans. When two-dimensional (2D) imaging is used, these scans are often repeated in other scanning planes. As a result, the number of scans comprising an MRI exam leads to prohibitively long exam times as compared to other medical imaging modalities such as computed tomography. Many approaches have been designed to accelerate the MRI acquisition while maintaining diagnostic quality.One approach is to collect multiple measurements while the MRI signal is evolving due to relaxation. This enables a reduction in scan time, as fewer acquisition windows are needed to collect the same number of measurements. However, when the temporal aspect of the acquisition is left unmodeled, artifacts are likely to appear in the reconstruction. Most often, these artifacts manifest as image blurring. The effect depends on the acquisition parameters as well as the tissue relaxation itself, resulting in spatially varying blurring. The severity of the artifacts is directly related to the level of acceleration, and thus presents a tradeoff with scan time. The effect is amplified when imaging in three dimensions, severely limiting scan efficiency. Volumetric variants would be used if not for the blurring, as they are able to reconstruct images at isotropic resolution and support mutli-planar reformatting.Another established acceleration technique, called parallel imaging, takes advantage of spatially sensitive receive coil arrays to collect multiple MRI measurements in parallel. Thus, the acquisition is shortened, and the reconstruction uses the spatial sensitivity information to recover the image. More recently, methods have been developed that leverage image structure such as sparsity and low rank to reduce the required number of samples for a well-posed reconstruction. Compressed sensing and its low rank extensions use these concepts to acquire incoherent measurements below the Nyquist rate. These techniques are especially suited to MRI, as incoherent measurements can be easily achieved through pseudo-random under-sampling. As the mechanisms behind parallel imaging and compressed sensing are fundamentally different, they can be combined to achieve even higher acceleration.This dissertation proposes accelerated MRI acquisition and reconstruction techniques that account for the temporal dynamics of the MR signal. The methods build off of parallel imaging and compressed sensing to reduce scan time and flexibly model the temporal relaxation behavior. By randomly shuffling the sampling in the acquisition stage and imposing low rank constraints in the reconstruction stage, intrinsic physical parameters are modeled and their dynamics are recovered as multiple images of varying tissue contrast. Additionally, blurring artifacts are significantly reduced, as the temporal dynamics are accounted for in the reconstruction.This dissertation first introduces T2 Shuffling, a volumetric technique that reduces blurring and reconstructs multiple T2-weighted image contrasts from a single acquisition. The method is integrated into a clinical hospital environment and evaluated on patients. Next, this dissertation develops a fast and distributed reconstruction for T2 Shuffling that achieves clinically relevant processing time latency. Clinical validation results are shown comparing T2 Shuffling as a single-sequence alternative to conventional pediatric knee MRI. Based off the compelling results, a fast targeted knee MRI using T2 Shuffling is implemented, enabling same-day access to MRI at one-third the cost compared to the conventional exam. To date, over 2,400 T2 Shuffling patient scans have been performed.Continuing the theme of accelerated multi-contrast imaging, this dissertation extends the temporal signal model with T1-T2 Shuffling. Building off of T2 Shuffling, the new method additionally samples multiple points along the saturation recovery curve by varying the repetition time durations during the scan. Since the signal dynamics are governed by both T1 recovery and T2 relaxation, the reconstruction captures information about both intrinsic tissue parameters. As a result, multiple target synthetic contrast images are reconstructed, all from a single scan. Approaches for selecting the sequence parameters are provided, and the method is evaluated on in vivo brain imaging of a volunteer.Altogether, these methods comprise the theme of MR Shuffling, and may open new pathways toward fast clinical MRI

Coupled Dictionary Learning for Multi-contrast MRI Reconstruction

Magnetic resonance (MR) imaging tasks often involve multiple contrasts, such as T1-weighted, T2-weighted and Fluid-attenuated inversion recovery (FLAIR) data. These contrasts capture information associated with the same underlying anatomy and thus exhibit similarities in either structure level or gray level. In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage the dependency correlation between different contrasts for guided or joint reconstruction from their under-sampled k-space data. Our approach iterates between three stages: coupled dictionary learning, coupled sparse denoising, and enforcing k-space consistency. The first stage learns a set of dictionaries that not only are adaptive to the contrasts, but also capture correlations among multiple contrasts in a sparse transform domain. By capitalizing on the learned dictionaries, the second stage performs coupled sparse coding to remove the aliasing and noise in the corrupted contrasts. The third stage enforces consistency between the denoised contrasts and the measurements in the k-space domain. Numerical experiments, consisting of retrospective under-sampling of various MRI contrasts with a variety of sampling schemes, demonstrate that CDLMRI is capable of capturing structural dependencies between different contrasts. The learned priors indicate notable advantages in multi-contrast MR imaging and promising applications in quantitative MR imaging such as MR fingerprinting

Advanced sparse sampling techniques for accelerating structural and quantitative MRI

Magnetic Resonance Imaging (MRI) has become a routine clinical procedure for the screening, diagnosis and treatment monitoring of various clinical conditions. Although MRI has highly desirable properties such as being completely non-ionizing and providing excellent soft tissue contrast which has resulted in its widespread usage across the gamut of clinical applications, it is limited by a slow data acquisition process. Several techniques have been developed over the years that have considerably improved the speed of MRI but there is still a clinical need to further accelerate MRI for many clinical applications. This thesis focuses on two recent advances in MRI acceleration to reduce the overall patient scan time. The first part of the thesis describes the development of a fast 3D neuroimaging methodology that has been implemented in a clinical Magnetic Resonance (MR) sequence which was accelerated using a combination of compressed sensing and sampling order optimization of acquired measurements. This methodology reduced the overall scan time by more than 60% compared to the normal scan time while also producing images of acceptable quality for clinical diagnosis. The clinical utility of accelerated neuroimaging is demonstrated by conducting a healthy volunteer study on eight subjects using this fast 3D MRI method. The results of the radiological diagnostic quality assessments that were carried out on the accelerated human brain MR images by four experienced neuroradiologists are presented. The results show that accelerated MR neuroimaging retained sufficient clinical diagnostic value for certain clinical applications. The second part of the thesis describes the development of an accelerated Cartesian sampling scheme for a rapid quantitative MR method called Magnetic Resonance Fingerprinting (MRF). This method was able to simultaneously generate quantitative multi-parametric maps such as T1, T2 and proton density (PD) maps in a very short scan duration that is clinically acceptable. The developed Cartesian sampling method using Echo Planar Imaging (EPI) is compared with conventional spiral sampling that is generally used for MR fingerprinting. The ability of novel iterative reconstruction techniques to improve the multi-parametric estimation accuracy is also demonstrated. The results show that accelerated Cartesian MR fingerprinting can be an alternative to conventional spiral MR fingerprinting

Deep Learning for the Acceleration of Magnetic Resonance Fingerprinting

Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that can simultaneously measure multiple important tissue properties of the human body. Although MRF has demonstrated improved scan efficiency compared to conventional techniques, further acceleration is still desired for translation into routine clinical practice. The purpose of this work is to accelerate MRF acquisition by developing a new tissue quantification method for MRF that allows accurate quantification with less sampling data. Most existing approaches use the MRF signal evolution at each individual pixel to estimate tissue properties without considering the spatial association among neighboring pixels. In this report, I propose a spatially-constrained quantification method that uses signals at multiple neighboring pixels to better estimate tissue properties at the central pixel. Specifically, I have designed a unique two-step deep learning model to estimate the tissue property (T1 or T2) maps from the observed MRF signals, which is comprised of 1) a feature extraction module to reduce the dimension of signals by extracting a low-dimensional feature vector from the high-dimensional signal evolution and 2) a spatially-constrained quantification module to exploit the spatial information from the extracted feature maps to generate the final tissue property map. A corresponding two-step training strategy has been developed for network training. The proposed method was tested on highly undersampled MRF data acquired from human brains. The experimental results demonstrated that the proposed method can achieve accurate quantification for T1 and T2 relaxation times using only 1/4 time points of the original sequence (i.e., four times of acceleration for MRF acquisition). Furthermore, a rapid 2D MRF technique with a sub-millimeter in-plane resolution was developed using deep-learning-based quantification approach for brain T1 and T2 quantification. Specifically, the 2D acquisition was performed using a FISP sequence and a spiral trajectory with 0.8 mm in-plane resolution. A novel network architecture, i.e., residual channel attention U-Net, was proposed to improve high resolution details in the estimated tissue maps. Quantitative brain imaging was performed with five adults and two pediatric subjects and the performance of the proposed approach was compared to several existing methods in the literature. In vivo measurements with both adult and pediatric subjects show that high quality T1 and T2 mapping with 0.8 mm in-plane resolution was achieved in 7.5 sec per slice. The proposed deep learning method outperformed existing algorithms in tissue quantification with improved accuracy. Compared to the standard U-Net, high resolution details in brain tissues were better preserved by the proposed residual channel attention U-Net. The experiments on pediatric subjects further demonstrated the potential of the proposed technique for fast pediatric neuroimaging. Alongside the reduced data acquisition time, five-fold acceleration in tissue property mapping was also achieved with the proposed method.Master of Scienc