Keywords: Image Reconstruction, Image Reconstruction, Cardiac motion, RF Arrays & Systems,Machine Learning/Artificial Intelligence

Motivation: To enable more accessible and less costly monitoring of cardiovascular mechanical function. 

Goal(s): Perform a feasibility study into the potential of imaging the heart based on scattering parameters of an RF antenna array

Approach: An MRI inspired reconstruction network was trained based on 150 in silico simulations of MRI segmented heart models. The method predicts 2D-maps of dielectric property changes and was tested in silico and in vivo on a healthy control. 

Results: In silico validation shows that it is feasible to reconstruct the shape and size of the heart, as well as left and right ventricular volumes, based on RF scattering measurements. 

Impact: This work shows the feasibility of imaging the heart from differential scattering parameter measurements and by using MRI inspired reconstruction. Preliminary results warrant further investigation into acquiring paired MRI and RF scattering measurements in human subjects.

Keywords: Image Reconstruction, Image Reconstruction

Motivation: 5D free-running whole heart CMR offers CT-quality images but requires hours-long reconstruction time, preventing clinical usage.  Therefore, a more efficient reconstruction algorithm is needed.

Goal(s): We propose to use the advanced numerical algorithm to reduce the reconstruction time while preserving image quality.

Approach: A variable projection augmented Lagrangian (VPAL) method for 5D motion-resolved image reconstruction was developed and compared with the state-of-the-art alternating direction method of multipliers (ADMM) on 15 5D free-running raw data sets.

Results: When compared to the ADMM method, VPAL reduced the reconstruction time by 60%, preserved image similarity, had equivalent ejection fraction measurements, and had superior radiologist ratings.

Impact: This study shows that using an advanced numerical algorithm for highly under-sampled MR reconstruction both reduces computational time and results in better image quality for diagnostics, bringing 5D free-running imaging closer to clinical usage.

Keywords: Image Reconstruction, Image Reconstruction, QSM

Motivation: Conventional compressed sensing-based 4D respiratory motion-resolved image reconstruction techniques are limited in their ability to utilize correlations between echoes for free-breathing 3D multi-echo liver MRI, which is inherently a 5D imaging problem.

Goal(s): Develop a 5D reconstruction algorithm to jointly exploit correlations between echoes and motion states.

Approach: 5D reconstruction with sparsity constraints along the motion and echo dimensions was developed to reconstruct retrospectively undersampled k-space data and compared with the 4D reconstruction on one volunteer and one patient.

Results: Compared to the 4D reconstruction, the proposed reconstruction showed comparable image quality to the reference (undersampling factor=1) and more reliable liver R₂^*/QSM values.

Impact: Using the proposed 5D image reconstruction, the scan time of free-breathing 3D multi-echo liver MRI for R₂^*/QSM can be reduced while preserving image quality and providing reliable R₂^*/QSM values.

Keywords: Image Reconstruction, Pancreas

Motivation:  4D-MRI could improve online MR-guided radiotherapy treatments on the MR-Linac, but long reconstruction times hinder clinical implementation.

Goal(s): To reconstruct and evaluate respiratory-correlated and time-resolved 4D-MRIs from different acquisitions.

Approach: We reconstructed 4D-MRIs using the XD-GRASP and GRASP-Pro algorithms from a clinical protocol (stack-of-stars) and a new Cartesian spiral sequence. We compared the reconstructions regarding diaphragm motion.

Results: Respiratory-resolved 4D-MRIs were reconstructed under 4 minutes from current acquisitions. Time-resolved 4D-MRIs from clinical acquisitions showed significant artefacts limiting the achievable temporal resolution which can be overcome by using a different acquisition.

Impact: Respiratory-correlated 4D-MRIs reconstruction times were compatible with online radiotherapy constraints for pancreatic cancer patients on the MR-Linac. Reaching high temporal resolutions in reconstructing time-resolved 4D-MRIs is not currently possible from the current clinical protocol.

Keywords: Image Reconstruction, Image Reconstruction

Motivation: Static sequences cannot capture movement and current real-time MRI sequences cannot provide volumetric coverage. Our motivation is too bridge this gap.

Goal(s): This work aims to extend a standard real-time MRI sequence to use multiband excitation to improve real-time volumetric coverage for diagnostic purposes.

Approach: Development of a radial sequence incorporating multiband excitation. A compressed sensing reconstruction algorithm is also implemented. Phantom and in-vivo images are acquired and analysed to assess image quality and reconstruction times.

Results: We present both phantom and in-vivo images acquired using this approach. The proposed reconstruction approach results in a higher SNR compared to conventional reconstruction.

Impact: We present improvements in real-time MRI by adding multiband excitation, allowing for the dynamic imaging of complex movements across a volume. This will improve the monitoring and diagnostic capabilities of real-time MRI, for example in maxillofacial surgery or orthopaedics.

Keywords: Image Reconstruction, Heart

Motivation: The beating of the heart is predictable, and existing methods mainly focus on sparsity and low rank, ignore the predictability.

Goal(s): Our goal is to improve the quality of dMRI reconstruction through predictability.

Approach: Introduced a method to extract predictability latent vectors and reconstruct images based on it.

Results: We used highly undersampled data for reconstruction and compared it with L+S,the experimental results indicate that we have achieved better reconstruction results than L+S.

Impact: This work use predictability for dMRI reconstruction without the use of sparsity and low rank.It is possible to introduce a new perspective for the reconstruction of dynamic images.

Keywords: Image Reconstruction, Image Reconstruction, local perturbation responses, point-spread functions, resolution analysis, dynamic imaging, real-time MRI

Motivation: Real-time MRI can provide powerful insights into dynamic processes, but practical experimental limitations have led to the widespread use of  undersampled data. While advanced reconstruction methods can mitigate  undersampling artifacts, these methods are unlikely to be perfect, and their rigorous validation  has been a longstanding open problem.

Goal(s): To introduce a new reference-free approach for evaluating real-time MRI results.

Approach: We introduce a framework for measuring spatiotemporal resolution in real-time MRI, based on the propagation of spatiotemporal perturbations through image reconstruction.

Results: The proposed approach is sensitive to spatiotemporal resolution features, and provides valuable new information for the interpretation of real-time MRI results.

Impact: The proposed framework enables measurement of spatiotemporal resolution, providing new information that is important for the interpretation of real-time MRI results, and can also be useful for the development/tuning of acquisition and reconstruction methods.

Keywords: Image Reconstruction, Image Reconstruction

Motivation: To improve image quality and spatial-temporal resolution of T₂^*-weighted dynamic MRI in applications such as MRI-guided focused ultrasound surgery, resulting in more effective diagnostic and treatment outcomes.

Goal(s): Our goal is to present an innovative complex-valued spatial-temporal multi-band super-resolution technique, enhancing spatial-temporal resolution, signal-to-noise ratio (SNR) and minimizing susceptibility artifacts in T₂^*-weighted dynamic MRI.

Approach: We highlight the susceptibility artifact through a hybrid simulation and design a multi-band-super-resolution sequence. We perform an experiment on phantom and the acquired data is reconstructed using our proposed method.

Results: Hybrid simulation and reconstruction on phantom data demonstrate improvements in SNR, spatial-temporal-resolution, and reduction of susceptibility artifacts.

Impact: Conventional magnitude-based super-resolution reconstruction exhibits signal loss due to susceptibility effects. Our proposed method integrating phase in reconstruction shows an improvement in SNR, spatial-temporal resolution, and a reduction of susceptibility artifacts.

Keywords: Image Reconstruction, Image Reconstruction

Motivation: Visualizing the movement of laryngeal muscles during voicing and breathing is important for understanding the mechanics of speech production.

Goal(s): The goal is to improve our understanding of speech production by visualizing gross-vocal fold movements due to laryngeal muscle contractions.

Approach: We proposed a multi-slice non-gated spiral sparse sampling strategy, and variational manifold based reconstruction scheme for dynamic laryngeal MR imaging at high spatio-temporal resolution.

Results: Our proposed method can capture gross vocal fold motions (e.g., adduction, abduction, elongation, shortening) during various voicing and breathing at both high spatial (1.5 mm²) and high temporal resolutions (36 ms/frame).

Impact: Otolaryngologists, voice and neuroscientists interested in larynx physiology and disorders pertaining to breathing and phonation.

Keywords: Image Reconstruction, Perfusion, Dynamic Contrast Enhancement, Real-Time MRI

Motivation: Dynamic contrast-enhanced (DCE) MRI requires both high spatial and high temporal resolution for accurate quantification and delineation. In practice, temporal resolution is often traded for spatial resolution and volume coverage.

Goal(s): To develop a reconstruction method that offers both high spatial and temporal resolution.

Approach: We describe a two-stage reconstruction technique that consistently produces high-temporal, high-spatial resolution estimates of the ground truth data and is more accurate than current state-of-the-art methods.

Results: The proposed method achieves high quality reconstruction with as few as one acquired spoke per frame when radial sampling is used.

Impact: The theoretical and practical success of the spatial subspace method over temporal subspace methods encourages further research on the topic. Furthermore, reconstruction at 1 spoke per frame enables larger imaging volumes, thus more capable imaging tools.

Keywords: Image Reconstruction, Cardiovascular

Motivation: Real-time MRI requires efficient data acquisition and low latency image reconstruction along high temporal resolution. The pMRI method known as GRAPPA, offers advantage in terms of fast data acquisition.

Goal(s):  However, large computational requirements of GRAPPA limit its performance in real-time clinical settings.

Approach: This paper presents a novel MPSoC based hardware accelerator which combines 32-bit FPGA based accelerator module with multiple DSP engines and on-chip ARM processor, to provide sufficient computational resources for GRAPPA.

Results: The results using in-vivo cardiac datasets i.e. 18-receiver coils, show that the proposed accelerator reconstructs cardiac images at ∼35 frames-per-second without degrading the image quality.

Impact: The proposed accelerator is capable to reconstruct 35 frames in one second as compared to the CPU-based counterparts which can only reconstruct 2 frames/second for a given GRAPPA reconstruction setting in our experiments.   

Keywords: Image Reconstruction, Heart, Reconstruction

Motivation: Try to obtaining high spatial and temporal resolution image in dynamic MRI.

Goal(s): The plug and play truncated low rank optimization combined iterative soft thresholding(PNPT-) embedded method was proposed to  reconstructe data and tested to accurately estimate the rank function for dMRI reconstruction.

Approach: The PNPT- method uses truncated nuclear norm combined iterative soft thresholding to assign different shrink values to different singular vectors, enabling the preservation of essential image information while effectively eliminating noise.

Results: The embedded method was proposed to  reconstructe data and tested to accurately estimate the rank function for dMRI reconstruction.

Impact: The proposed method demonstrated reduced aliasing artifacts compared to other methods and yielded better reconstruction images in cine and perfusion data. Consequently, it provided more accurate and efficient image information, benefiting the clinical diagnosis of heart-related diseases.

Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, MR Multitasking, DCE MRI, self-supervised learning, zero-shot learning

Motivation: Deep learning (DL) MR multitasking reconstruction can reduce the reconstruction time, but previous methods are supervised learning, which may learn artifacts from the reference images.

Goal(s): Our goal was to develop a DL reconstruction method that can improve image quality beyond supervised DL and conventional iterative reconstruction.

Approach: We developed a zero-shot self-supervised deep learning method for DCE MR multitasking reconstruction.

Results: With shorter reconstruction time than conventional iterative reconstruction, the proposed method obtained better image quality than both supervised DL and conventional iterative reconstruction methods.

Impact: With the proposed method, DCE MR multitasking can have better image quality with shorter reconstruction time than previous iterative reconstruction, which is essential for the potential clinical application of the motion-resolved and high spatial-temporal-resolution abdominal DCE MR multitasking.

Keywords: Image Reconstruction, Radiotherapy

Motivation: Motion resolution of the original Multitasking 4D RTP MR sequence is not robust enough.

Goal(s): To enhance motion robustness and introduce T1/T2 mappings of 4D abdominal RTP MR protocol.

Approach: Estimation of explicit motion deformable vector fields to correct. 

Results: The proposed approach dramatically enhance motion robustness of MR multitasking in 4D MRI.  

Impact: The straight-forward explicit motion vector field estimation and application provides effective and robust motion correction .

Keywords: Image Reconstruction, Radiotherapy, real-time, MR-Linac

Motivation: Most standard MR sequences are too slow for real-time applications. Accelerated acquisitions are one method to achieve the required frame rates. Neural-network reconstruction and parallel imaging are two methods that can achieve the necessary frame rates but are computationally expensive or require extensive coil arrays. 

Goal(s): To develop an accelerated reconstruction method suitable for real-time applications which is computationally inexpensive and simple to implement.

Approach: Our method was tested retrospectively on lung, liver, and prostate patients for image quality and auto-contourability using a range of metrics.

Results: Image quality and contourability improved over similar methods while maintaining good reconstruction times for real-time applications.

Impact: An accelerated PCA-based reconstruction method was developed suitable for real-time applications, and in particular, target tracking. It has improved image quality and auto-contourability compared to similar methods while still maintaining simplicity in its implementation (low-cost computing, single coil arrays).

Keywords: Image Reconstruction, Motion Correction

Motivation: Dynamic contrast-enhanced (DCE) MRI faces challenges from respiratory motion and sub-optimal DCE contrast timing. Free-breathing DCE with high temporal resolution is desirable. 

Goal(s): We aim to reconstruct respiratory motion-free and high temporal resolution DCE-MRI.

Approach: We proposed a Motion Integrated Forward model using motion vector fields and jointly estimated coil sensitivity to reconstruct severely under-sampled DCE data. Furthermore, we utilized a model-based deep learning framework to amalgamate the knowledge of the measurement model and the denoising prior.

Results: The proposed method provided deformable motion vector fields, coil-sensitivity maps, and sharp motion-free DCE images without artifacts using highly under-sampled data.

Impact: This method provides good quality free-breathing liver DCE MR images with high temporal resolution. It will eliminate the need for breath-holding. Moreover, continuous acquisition and high temporal resolution reconstruction mitigate the problem of sub-optimal DCE contrast in clinical diagnosis.