Howard Aizenstein

Jinghang Li, Tales Santini, Courtney Clark et al.

Hippocampal subfield segmentation requires high-resolution T2w turbo spin echo (TSE) MRI, yet this sequence is susceptible to motion artifacts, leading to substantial data loss. We developed a conditional generative model (MRecover) that synthesizes routinely acquired T1w images to create TSE images with autoregressive slice conditioning for volumetric consistency. Trained on 7T MRI data (n=577), the model achieved high in-domain fidelity (n=148, SSIM=0.84, FSIM=0.94) and generalized well to out-of-domain 3T data: subfield volumes from synthesized and the as-acquired images closely matched: (n=416, r=0.87-0.97) and yielded 31.8% more analyzable subjects in the motion-affected ADNI3 dataset after quality control (593 vs 450). The synthesized images also achieved larger effect sizes due to increasing the sample size for diagnostic group differences in hippocampal subfield atrophy (whole hippocampus $ε^2$= 0.121-0.100 vs. 0.086-0.062, left-right hemispheres). Project page: https://jinghangli98.github.io/MRecover/

Jinghang Li, Tales Santini, Yuanzhe Huang et al.

White matter hyperintensity (WMH) remains the top imaging biomarker for neurodegenerative diseases. Robust and accurate segmentation of WMH holds paramount significance for neuroimaging studies. The growing shift from 3T to 7T MRI necessitates robust tools for harmonized segmentation across field strengths and artifacts. Recent deep learning models exhibit promise in WMH segmentation but still face challenges, including diverse training data representation and limited analysis of MRI artifacts' impact. To address these, we introduce wmh_seg, a novel deep learning model leveraging a transformer-based encoder from SegFormer. wmh_seg is trained on an unmatched dataset, including 1.5T, 3T, and 7T FLAIR images from various sources, alongside with artificially added MR artifacts. Our approach bridges gaps in training diversity and artifact analysis. Our model demonstrated stable performance across magnetic field strengths, scanner manufacturers, and common MR imaging artifacts. Despite the unique inhomogeneity artifacts on ultra-high field MR images, our model still offers robust and stable segmentation on 7T FLAIR images. Our model, to date, is the first that offers quality white matter lesion segmentation on 7T FLAIR images.

Yuanzhe Huang, Saurab Faruque, Minjie Wu et al.

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression.

Anthony Sicilia, Xingchen Zhao, Davneet Minhas et al.

We consider a model-agnostic solution to the problem of Multi-Domain Learning (MDL) for multi-modal applications. Many existing MDL techniques are model-dependent solutions which explicitly require nontrivial architectural changes to construct domain-specific modules. Thus, properly applying these MDL techniques for new problems with well-established models, e.g. U-Net for semantic segmentation, may demand various low-level implementation efforts. In this paper, given emerging multi-modal data (e.g., various structural neuroimaging modalities), we aim to enable MDL purely algorithmically so that widely used neural networks can trivially achieve MDL in a model-independent manner. To this end, we consider a weighted loss function and extend it to an effective procedure by employing techniques from the recently active area of learning-to-learn (meta-learning). Specifically, we take inner-loop gradient steps to dynamically estimate posterior distributions over the hyperparameters of our loss function. Thus, our method is model-agnostic, requiring no additional model parameters and no network architecture changes; instead, only a few efficient algorithmic modifications are needed to improve performance in MDL. We demonstrate our solution to a fitting problem in medical imaging, specifically, in the automatic segmentation of white matter hyperintensity (WMH). We look at two neuroimaging modalities (T1-MR and FLAIR) with complementary information fitting for our problem.

Xingchen Zhao, Anthony Sicilia, Davneet Minhas et al.

Typical machine learning frameworks heavily rely on an underlying assumption that training and test data follow the same distribution. In medical imaging which increasingly begun acquiring datasets from multiple sites or scanners, this identical distribution assumption often fails to hold due to systematic variability induced by site or scanner dependent factors. Therefore, we cannot simply expect a model trained on a given dataset to consistently work well, or generalize, on a dataset from another distribution. In this work, we address this problem, investigating the application of machine learning models to unseen medical imaging data. Specifically, we consider the challenging case of Domain Generalization (DG) where we train a model without any knowledge about the testing distribution. That is, we train on samples from a set of distributions (sources) and test on samples from a new, unseen distribution (target). We focus on the task of white matter hyperintensity (WMH) prediction using the multi-site WMH Segmentation Challenge dataset and our local in-house dataset. We identify how two mechanically distinct DG approaches, namely domain adversarial learning and mix-up, have theoretical synergy. Then, we show drastic improvements of WMH prediction on an unseen target domain.