Implicit neural representation for free-breathing MR fingerprinting (INR-MRF): co-registered 3D whole-liver water T1, water T2, proton density fat fraction, and R2* mapping

Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by interleaving inversion recovery and T2 magnetization preparation. We propose a neural network based on a 4D and a 3D implicit neural representation (INR) which simultaneously learns the motion deformation fields and the static reference frame MRI subspace images respectively. Water and fat singular images were separated during network training, with no need of performing retrospective water-fat separation. T1, T2, R2* and proton density fat fraction (PDFF) produced by the proposed method were validated in vivo on 10 healthy subjects, using quantitative maps generated from conventional scans as reference. Results: Our results showed minimal bias and narrow 95% limits of agreement on T1, T2, R2* and PDFF values in the liver compared to conventional breath-holding scans. Conclusions: INR-MRF enabled co-registered 3D whole liver T1, T2, R2* and PDFF mapping in a single free-breathing scan.

Unique MS Lesion Identification from MRI

Unique identification of multiple sclerosis (MS) white matter lesions (WMLs) is important to help characterize MS progression. WMLs are routinely identified from magnetic resonance images (MRIs) but the resultant total lesion load does not correlate well with EDSS; whereas mean unique lesion volume has been shown to correlate with EDSS. Our approach builds on prior work by incorporating Hessian matrix computation from lesion probability maps before using the random walker algorithm to estimate the volume of each unique lesion. Synthetic images demonstrate our ability to accurately count the number of lesions present. The takeaways, are: 1) that our method correctly identifies all lesions including many that are missed by previous methods; 2) we can better separate confluent lesions; and 3) we can accurately capture the total volume of WMLs in a given probability map. This work will allow new more meaningful statistics to be computed from WMLs in brain MRIs

Bi-Directional MS Lesion Filling and Synthesis Using Denoising Diffusion Implicit Model-based Lesion Repainting

Automatic magnetic resonance (MR) image processing pipelines are widely used to study people with multiple sclerosis (PwMS), encompassing tasks such as lesion segmentation and brain parcellation. However, the presence of lesion often complicates these analysis, particularly in brain parcellation. Lesion filling is commonly used to mitigate this issue, but existing lesion filling algorithms often fall short in accurately reconstructing realistic lesion-free images, which are vital for consistent downstream analysis. Additionally, the performance of lesion segmentation algorithms is often limited by insufficient data with lesion delineation as training labels. In this paper, we propose a novel approach leveraging Denoising Diffusion Implicit Models (DDIMs) for both MS lesion filling and synthesis based on image inpainting. Our modified DDIM architecture, once trained, enables both MS lesion filing and synthesis. Specifically, it can generate lesion-free T1-weighted or FLAIR images from those containing lesions; Or it can add lesions to T1-weighted or FLAIR images of healthy subjects. The former is essential for downstream analyses that require lesion-free images, while the latter is valuable for augmenting training datasets for lesion segmentation tasks. We validate our approach through initial experiments in this paper and demonstrate promising results in both lesion filling and synthesis, paving the way for future work.

MRI quantification of liver fibrosis using diamagnetic susceptibility: An ex-vivo feasibility study

In chronic liver disease, liver fibrosis develops as excessive deposition of extracellular matrix macromolecules, predominantly collagens, progressively form fibrous scars that disrupt the hepatic architecture, and fibrosis, iron, and fat are interrelated. Fibrosis is the best predictor of morbidity and mortality in chronic liver disease but liver biopsy, the reference method for diagnosis and staging, is invasive and limited by sampling and interobserver variability and risks of complications. The overall objective of this study was to develop a new non-invasive method to quantify fibrosis using diamagnetic susceptibility sources with histology validation in ex vivo liver explants.

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000-30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.

Deep learning improved autofocus for motion artifact reduction and its application in quantitative susceptibility mapping

Purpose: To develop a pipeline for motion artifact correction in mGRE and quantitative susceptibility mapping (QSM). Methods: Deep learning is integrated with autofocus to improve motion artifact suppression, which is applied QSM of patients with Parkinson's disease (PD). The estimation of affine motion parameters in the autofocus method depends on signal-to-noise ratio and lacks accuracy when data sampling occurs outside the k-space center. A deep learning strategy is employed to remove the residual motion artifacts in autofocus. Results: Results obtained in simulated brain data (n =15) with reference truth show that the proposed autofocus deep learning method significantly improves the image quality of mGRE and QSM (p = 0.001 for SSIM, p < 0.0001 for PSNR and RMSE). Results from 10 PD patients with real motion artifacts in QSM have also been corrected using the proposed method and sent to an experienced radiologist for image quality evaluation, and the average image quality score has increased (p=0.0039). Conclusions: The proposed method enables substantial suppression of motion artifacts in mGRE and QSM.

RimSet: Quantitatively Identifying and Characterizing Chronic Active Multiple Sclerosis Lesion on Quantitative Susceptibility Maps

Background: Rim+ lesions in multiple sclerosis (MS), detectable via Quantitative Susceptibility Mapping (QSM), correlate with increased disability. Existing literature lacks quantitative analysis of these lesions. We introduce RimSet for quantitative identification and characterization of rim+ lesions on QSM. Methods: RimSet combines RimSeg, an unsupervised segmentation method using level-set methodology, and radiomic measurements with Local Binary Pattern texture descriptors. We validated RimSet using simulated QSM images and an in vivo dataset of 172 MS subjects with 177 rim+ and 3986 rim-lesions. Results: RimSeg achieved a 78.7% Dice score against the ground truth, with challenges in partial rim lesions. RimSet detected rim+ lesions with a partial ROC AUC of 0.808 and PR AUC of 0.737, surpassing existing methods. QSMRim-Net showed the lowest mean square error (0.85) and high correlation (0.91; 95% CI: 0.88, 0.93) with expert annotations at the subject level.

Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion

Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation. Current state-of-the-art automatic MS lesion segmentation methods utilize modified U-Net-like architectures. However, in the literature, dedicated architecture modifications were always required to maximize their performance. In addition, the best-performing methods have not proven to be generalizable to diverse test datasets with contrast variations and image artifacts. In this work, we developed an accurate and generalizable MS lesion segmentation model using the well-known U-Net architecture without further modification. A novel test-time self-ensembled lesion fusion strategy is proposed that not only achieved the best performance using the ISBI 2015 MS segmentation challenge data but also demonstrated robustness across various self-ensemble parameter choices. Moreover, equipped with instance normalization rather than batch normalization widely used in literature, the model trained on the ISBI challenge data generalized well on clinical test datasets from different scanners.

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Deep learning algorithms utilizing magnetic resonance (MR) images have demonstrated cutting-edge proficiency in autonomously segmenting multiple sclerosis (MS) lesions. Despite their achievements, these algorithms may struggle to extend their performance across various sites or scanners, leading to domain generalization errors. While few-shot or one-shot domain adaptation emerges as a potential solution to mitigate generalization errors, its efficacy might be hindered by the scarcity of labeled data in the target domain. This paper seeks to tackle this challenge by integrating one-shot adaptation data with harmonized training data that incorporates labels. Our approach involves synthesizing new training data with a contrast akin to that of the test domain, a process we refer to as "contrast harmonization" in MRI. Our experiments illustrate that the amalgamation of one-shot adaptation data with harmonized training data surpasses the performance of utilizing either data source in isolation. Notably, domain adaptation using exclusively harmonized training data achieved comparable or even superior performance compared to one-shot adaptation. Moreover, all adaptations required only minimal fine-tuning, ranging from 2 to 5 epochs for convergence.

DAGrid: Directed Accumulator Grid

Recent research highlights that the Directed Accumulator (DA), through its parametrization of geometric priors into neural networks, has notably improved the performance of medical image recognition, particularly with small and imbalanced datasets. However, DA's potential in pixel-wise dense predictions is unexplored. To bridge this gap, we present the Directed Accumulator Grid (DAGrid), which allows geometric-preserving filtering in neural networks, thus broadening the scope of DA's applications to include pixel-level dense prediction tasks. DAGrid utilizes homogeneous data types in conjunction with designed sampling grids to construct geometrically transformed representations, retaining intricate geometric information and promoting long-range information propagation within the neural networks. Contrary to its symmetric counterpart, grid sampling, which might lose information in the sampling process, DAGrid aggregates all pixels, ensuring a comprehensive representation in the transformed space. The parallelization of DAGrid on modern GPUs is facilitated using CUDA programming, and also back propagation is enabled for deep neural network training. Empirical results show DAGrid-enhanced neural networks excel in supervised skin lesion segmentation and unsupervised cardiac image registration. Specifically, the network incorporating DAGrid has realized a 70.8% reduction in network parameter size and a 96.8% decrease in FLOPs, while concurrently improving the Dice score for skin lesion segmentation by 1.0% compared to state-of-the-art transformers. Furthermore, it has achieved improvements of 4.4% and 8.2% in the average Dice score and Dice score of the left ventricular mass, respectively, indicating an increase in registration accuracy for cardiac images. The source code is available at https://github.com/tinymilky/DeDA.