Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Diffusion models

Motivation: Diffusion models have shown state-of-the-art performance in solving inverse problems. However, current solutions typically consider cases only when the forward operator is fully known, which limits their applicability to the wide variety of MRI inverse problems.

Goal(s): Develop a general method for blind MRI inverse problems with unknown forward operator parameters.

Approach: We extend the RED-diff framework, which has the key strength of not requiring training or fine–tuning for each specific task. We test our method for image reconstruction with off-resonance and motion correction.

Results: Our blind RED-diff framework can successfully approximate the unknown forward model parameters and produce accurate reconstructions.

Impact: We demonstrate the potential of current diffusion models to readily tackle a wide range of blind inverse problems in MRI without application-specific re-training or fine-tuning. Image reconstruction with motion and off-resonance correction are the first demonstration applications.

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, image reconstruction; diffusion models

Motivation: Diffusion probabilistic methods synthesize realistic images via a denoising transformation from Gaussian noise onto MRI data, but this normality assumption can yield suboptimal performance in accelerated MRI reconstruction tasks.

Goal(s): Our goal was to devise a new diffusion-based method that generates high-quality images by capturing a task-relevant transformation for accelerated MRI.

Approach: We introduced a novel reconstruction method based on a diffusion Schrodinger bridge (FDB) that learns to directly transform between undersampled and fully-sampled MRI data via a multi-step process.

Results: Higher reconstruction performance was obtained with FDB over previous state-of-the-art at up-to 8-fold acceleration.

Impact: The improvement in image quality and acquisition speed in accelerated MRI enabled through FDB may facilitate comprehensive MRI exams in many applications, particularly in assessments of pediatric and elderly individuals in need of fast exams due to limited motor control.

Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence

Motivation: MR-based “real-time” imaging of dynamic processes, as the beating heart, often depends on fast (undersampled) scans, which are subsequently reconstructed by algorithms exploiting prior knowledge. Spatio-temporal models describing the data in suboptimal manner can thereby lead to residual artifacts.   

Goal(s): A high-quality model to regularize the reconstruction of real-time cardiac MRI based on undersampled spiral data acquisitions.

Approach: A video diffusion model was trained using cine videos in magnitude reconstruction and subsequently applied as a prior in a plug-and-play FISTA approach.

Results: Reconstructions of undersampled real-time frames with higher image quality than a low rank plus sparse approach.

Impact: We show the potential of probabilistic video diffusion models as a promising prior in iterative reconstructions of undersampled dynamic MR data. In our example, the approach enabled high quality real-time cardiac functional MRI in patients with arrhythmia.  

Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, Rapid MRI, Quantitative MRI, knee, brain

Motivation: Quantitative MRI (qMRI) is time-consuming and requires substantial efforts for acceleration to cut down the acquisition time.

Goal(s): This paper proposes a novel generative AI approach for image reconstruction based on diffusion modeling conditioned on the native data domain. 

Approach: Our method is applied to multi-coil quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the tissue parameter domain.

Results: The proposed method demonstrates a significant promise for reconstructing quantitative maps at high acceleration factors. Notably, it maintains excellent reconstruction accuracy and efficiency for MR parameter maps across diverse anatomical structures.

Impact: This work demonstrates the feasibility of a new generative AI method for rapid qMRI. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.

Keywords: AI Diffusion Models, Image Reconstruction, Heart

Motivation: Current deep learning reconstruction for accelerated cardiac cine MRI suffers from spatial and temporal blurring.

Goal(s): To improve image sharpness and motion delineation for cine MRI under high undersampling rates.

Approach: A combined non-generative reconstruction and diffusion enhancement model along with a novel paired sampling strategy was developed.

Results: The proposed combined method provided sharper tissue boundaries and clearer motion than the original reconstruction in experts’ evaluation on clinical data. The innovative paired sampling strategy substantially reduced artificial noises in the generative results.

Impact: The approach has the potential to improve reconstruction quality in highly accelerated cardiac cine imaging. The novel paired sampling for diffusion generation may be applied to other conditional tasks to reduce the artificial noises stemming from noisy training data.

Keywords: AI Diffusion Models, Machine Learning/Artificial Intelligence, fMRI, xAI, diffusion, transformers

Motivation: Deep-learning classifiers for functional MRI (fMRI) offer state-of-the-art performance in detection of cognitive states from BOLD responses, but their black-box nature hinders interpretation of results.

Goal(s): Our goal was to devise a reliable method to infer the important BOLD-response attributes that drive the decisions of deep fMRI classifiers.

Approach: We introduced a novel counterfactual explanation method (DreaMR) based on a new fractional, distilled diffusion prior for efficient generation of high-fidelity counterfactual samples.

Results: DreaMR generated more specific and plausible explanations of deep fMRI classifiers trained for resting-state and task-based fMRI analysis than previous state-of-the-art explanation methods.

Impact: The improvement in sensitivity, plausability and efficiency in explanation of deep classifiers through DreaMR may facilitate adoption of AI-based analyses in fMRI studies, thereby benefiting assessment of cognitive processes in both normal and neurological disease states.

Keywords: AI Diffusion Models, fMRI, brain decoding, fMRI

Motivation: The study addresses the challenge of decoding and reconstructing visual experiences from fMRI data, an area yet to be mastered in neuroscience.

Goal(s): We propose a methodology that deciphers brain activity patterns and renders these into visual and textual representations.

Approach: We trained a linear model to map brain activity to image latent represenations. This informed a generative image-to-text transformer and a visual attribute-focused regression model, culminating in the creation of photorealistic images using a text-to-image diffusion model.

Results: The model effectively combined high-level semantic understanding and low-level visual details, producing plausible reconstruction images from fMRI data.

Impact: Our findings enhance our understanding of visual processing in the brain, with significant implications for integrating artificial intelligence (AI) with neuroscience.

Keywords: AI Diffusion Models, Cardiovascular

Motivation: The synthesis of multi-sequence cardiac magnetic resonance (CMR) images is of great significance to shorten the scan durations and expand the beneficiary population from CMR examination.

Goal(s): Achieving accurate synthesis is particularly challenging due to the inherent suboptimal image quality and the persistent interference from noise.

Approach: We first propose a novel method based on diffusion model, CMRDiff, for multi-sequence CMR synthesis.

Results: We evaluated the proposed CMRDiff on the MICCAI2020 MyoPS Challenge dataset. Our experiments demonstrate that CMRDiff outperforms other state of-the-art multi-modal MRI synthesis methods.

Impact: We design the first denoising diffusion probabilistic modelin the literature for multi-sequence CMR synthesis, promising to serve as an effective tool for multi-sequence CMR synthesis.

Keywords: Acquisition Methods, Brain

Motivation: Scanning for multi-contrast MR images is time-consuming. To reduce scan time, it is beneficial to explore methods for efficiently synthesizing target contrast images from existing contrast scans.

Goal(s): To address the stability issues encountered when dealing with multi-contrast MR image domains individually, we propose a methodology for effectively synthesizing images while incorporating multi-contrast domains.

Approach: Our model is a novel unified diffusion model (UDM) that improves the synthesis of detailed anatomical structures in target contrast images through an ensemble method.

Results: UDM demonstrates effectiveness across multiple domains, outperforming existing methodologies in synthesizing images for each contrast domain.

Impact: By reducing scan times and costs for multi-contrast imaging, UDM facilitates prognosis prediction and treatment planning. This method is not only usable for image synthesis but also extendable to various applications such as reconstruction.