LAMA: Stable Dual-Domain Deep Reconstruction For Sparse-View CT

Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block optimization by synergizing data-driven and classical techniques with proven convergence. LAMA is naturally induced by a variational model with learnable regularizers in both data and image domains, parameterized as composite functions of neural networks trained with domain-specific data. We allow these regularizers to be nonconvex and nonsmooth to extract features from data effectively. We minimize the overall objective function using Nesterov's smoothing technique and residual learning architecture. It is demonstrated that LAMA reduces network complexity, improves memory efficiency, and enhances reconstruction accuracy, stability, and interpretability. Extensive experiments show that LAMA significantly outperforms state-of-the-art methods on popular benchmark datasets for Computed Tomography.

Cross-Institutional Structured Radiology Reporting for Lung Cancer Screening Using a Dynamic Template-Constrained Large Language Model

Structured radiology reporting is advantageous for optimizing clinical workflows and patient outcomes. Current LLMs in creating structured reports face the challenges of formatting errors, content hallucinations, and privacy leakage concerns when uploaded to external servers. We aim to develop an enhanced open-source LLM for creating structured and standardized LCS reports from free-text descriptions. After institutional IRB approvals, 5,442 de-identified LCS reports from two institutions were retrospectively analyzed. 500 reports were randomly selected from the two institutions evenly and then manually labeled for evaluation. Two radiologists from the two institutions developed a standardized template including 29 features for lung nodule reporting. We proposed template-constrained decoding to enhance state-of-the-art open-source LLMs, including LLAMA, Qwen, and Mistral. The LLM performance was extensively evaluated in terms of F1 score, confidence interval, McNemar test, and z-test. Based on the structured reports created from the large-scale dataset, a nodule-level retrieval system was prototyped and an automatic statistical analysis was performed. Our software, vLLM-structure, is publicly available for local deployment with enhanced LLMs. Our template-constrained decoding approach consistently enhanced the LLM performance on multi-institutional datasets, with neither formatting errors nor content hallucinations. Our method improved the best open-source LLAMA-3.1 405B by up to 10.42%, and outperformed GPT-4o by 17.19%. A novel nodule retrieval system was successfully prototyped and demonstrated on a large-scale multimodal database using our enhanced LLM technologies. The automatically derived statistical distributions were closely consistent with the prior findings in terms of nodule type, location, size, status, and Lung-RADS.

Noise-aware Dynamic Image Denoising and Positron Range Correction for Rubidium-82 Cardiac PET Imaging via Self-supervision

Rb-82 is a radioactive isotope widely used for cardiac PET imaging. Despite numerous benefits of 82-Rb, there are several factors that limits its image quality and quantitative accuracy. First, the short half-life of 82-Rb results in noisy dynamic frames. Low signal-to-noise ratio would result in inaccurate and biased image quantification. Noisy dynamic frames also lead to highly noisy parametric images. The noise levels also vary substantially in different dynamic frames due to radiotracer decay and short half-life. Existing denoising methods are not applicable for this task due to the lack of paired training inputs/labels and inability to generalize across varying noise levels. Second, 82-Rb emits high-energy positrons. Compared with other tracers such as 18-F, 82-Rb travels a longer distance before annihilation, which negatively affect image spatial resolution. Here, the goal of this study is to propose a self-supervised method for simultaneous (1) noise-aware dynamic image denoising and (2) positron range correction for 82-Rb cardiac PET imaging. Tested on a series of PET scans from a cohort of normal volunteers, the proposed method produced images with superior visual quality. To demonstrate the improvement in image quantification, we compared image-derived input functions (IDIFs) with arterial input functions (AIFs) from continuous arterial blood samples. The IDIF derived from the proposed method led to lower AUC differences, decreasing from 11.09% to 7.58% on average, compared to the original dynamic frames. The proposed method also improved the quantification of myocardium blood flow (MBF), as validated against 15-O-water scans, with mean MBF differences decreased from 0.43 to 0.09, compared to the original dynamic frames. We also conducted a generalizability experiment on 37 patient scans obtained from a different country using a different scanner.

Multi-Conditioned Denoising Diffusion Probabilistic Model (mDDPM) for Medical Image Synthesis

Medical imaging applications are highly specialized in terms of human anatomy, pathology, and imaging domains. Therefore, annotated training datasets for training deep learning applications in medical imaging not only need to be highly accurate but also diverse and large enough to encompass almost all plausible examples with respect to those specifications. We argue that achieving this goal can be facilitated through a controlled generation framework for synthetic images with annotations, requiring multiple conditional specifications as input to provide control. We employ a Denoising Diffusion Probabilistic Model (DDPM) to train a large-scale generative model in the lung CT domain and expand upon a classifier-free sampling strategy to showcase one such generation framework. We show that our approach can produce annotated lung CT images that can faithfully represent anatomy, convincingly fooling experts into perceiving them as real. Our experiments demonstrate that controlled generative frameworks of this nature can surpass nearly every state-of-the-art image generative model in achieving anatomical consistency in generated medical images when trained on comparable large medical datasets.

Hyper-Compression: Model Compression via Hyperfunction

The rapid growth of large models' size has far outpaced that of GPU memory. To bridge this gap, inspired by the succinct relationship between genotype and phenotype, we turn the model compression problem into the issue of parameter representation to propose the so-called hyper-compression. The hyper-compression uses a hyperfunction to represent the parameters of the target network, and notably, here the hyperfunction is designed per ergodic theory that relates to a problem: if a low-dimensional dynamic system can fill the high-dimensional space eventually. Empirically, the proposed hyper-compression enjoys the following merits: 1) \textbf{P}referable compression ratio; 2) \textbf{N}o post-hoc retraining; 3) \textbf{A}ffordable inference time; and 4) \textbf{S}hort compression time. It compresses LLaMA2-7B in an hour and achieves close-to-int4-quantization performance, without retraining and with a performance drop of less than 1\%. Our work has the potential to invigorate the field of model compression, towards a harmony between the scaling law and the stagnation of hardware upgradation.

LLM-based MOFs Synthesis Condition Extraction using Few-Shot Demonstrations

The extraction of Metal-Organic Frameworks (MOFs) synthesis conditions from literature text has been challenging but crucial for the logical design of new MOFs with desirable functionality. The recent advent of large language models (LLMs) provides disruptively new solution to this long-standing problem and latest researches have reported over 90% F1 in extracting correct conditions from MOFs literature. We argue in this paper that most existing synthesis extraction practices with LLMs stay with the primitive zero-shot learning, which could lead to downgraded extraction and application performance due to the lack of specialized knowledge. This work pioneers and optimizes the few-shot in-context learning paradigm for LLM extraction of material synthesis conditions. First, we propose a human-AI joint data curation process to secure high-quality ground-truth demonstrations for few-shot learning. Second, we apply a BM25 algorithm based on the retrieval-augmented generation (RAG) technique to adaptively select few-shot demonstrations for each MOF's extraction. Over a dataset randomly sampled from 84,898 well-defined MOFs, the proposed few-shot method achieves much higher average F1 performance (0.93 vs. 0.81, +14.8%) than the native zero-shot LLM using the same GPT-4 model, under fully automatic evaluation that are more objective than the previous human evaluation. The proposed method is further validated through real-world material experiments: compared with the baseline zero-shot LLM, the proposed few-shot approach increases the MOFs structural inference performance (R^2) by 29.4% in average.

Physics-Inspired Generative Models in Medical Imaging: A Review

Physics-inspired generative models, in particular diffusion and Poisson flow models, enhance Bayesian methods and promise great utilities in medical imaging. This review examines the transformative role of such generative methods. First, a variety of physics-inspired generative models, including Denoising Diffusion Probabilistic Models (DDPM), Score-based Diffusion Models, and Poisson Flow Generative Models (PFGM and PFGM++), are revisited, with an emphasis on their accuracy, robustness as well as acceleration. Then, major applications of physics-inspired generative models in medical imaging are presented, comprising image reconstruction, image generation, and image analysis. Finally, future research directions are brainstormed, including unification of physics-inspired generative models, integration with vision-language models (VLMs),and potential novel applications of generative models. Since the development of generative methods has been rapid, this review will hopefully give peers and learners a timely snapshot of this new family of physics-driven generative models and help capitalize their enormous potential for medical imaging.

Integrating AI in College Education: Positive yet Mixed Experiences with ChatGPT

The integration of artificial intelligence (AI) chatbots into higher education marks a shift towards a new generation of pedagogical tools, mirroring the arrival of milestones like the internet. With the launch of ChatGPT-4 Turbo in November 2023, we developed a ChatGPT-based teaching application (https://chat.openai.com/g/g-1imx1py4K-chatge-medical-imaging) and integrated it into our undergraduate medical imaging course in the Spring 2024 semester. This study investigates the use of ChatGPT throughout a semester-long trial, providing insights into students' engagement, perception, and the overall educational effectiveness of the technology. We systematically collected and analyzed data concerning students' interaction with ChatGPT, focusing on their attitudes, concerns, and usage patterns. The findings indicate that ChatGPT offers significant advantages such as improved information access and increased interactivity, but its adoption is accompanied by concerns about the accuracy of the information provided and the necessity for well-defined guidelines to optimize its use.

Data-driven imaging geometric recovery of ultrahigh resolution robotic micro-CT for in-vivo and other applications

We introduce an ultrahigh-resolution (50\mu m\) robotic micro-CT design for localized imaging of carotid plaques using robotic arms, cutting-edge detector, and machine learning technologies. To combat geometric error-induced artifacts in interior CT scans, we propose a data-driven geometry estimation method that maximizes the consistency between projection data and the reprojection counterparts of a reconstructed volume. Particularly, we use a normalized cross correlation metric to overcome the projection truncation effect. Our approach is validated on a robotic CT scan of a sacrificed mouse and a micro-CT phantom scan, both producing sharper images with finer details than that prior correction.

Cardiovascular Disease Detection from Multi-View Chest X-rays with BI-Mamba

Accurate prediction of Cardiovascular disease (CVD) risk in medical imaging is central to effective patient health management. Previous studies have demonstrated that imaging features in computed tomography (CT) can help predict CVD risk. However, CT entails notable radiation exposure, which may result in adverse health effects for patients. In contrast, chest X-ray emits significantly lower levels of radiation, offering a safer option. This rationale motivates our investigation into the feasibility of using chest X-ray for predicting CVD risk. Convolutional Neural Networks (CNNs) and Transformers are two established network architectures for computer-aided diagnosis. However, they struggle to model very high resolution chest X-ray due to the lack of large context modeling power or quadratic time complexity. Inspired by state space sequence models (SSMs), a new class of network architectures with competitive sequence modeling power as Transfomers and linear time complexity, we propose Bidirectional Image Mamba (BI-Mamba) to complement the unidirectional SSMs with opposite directional information. BI-Mamba utilizes parallel forward and backwark blocks to encode longe-range dependencies of multi-view chest X-rays. We conduct extensive experiments on images from 10,395 subjects in National Lung Screening Trail (NLST). Results show that BI-Mamba outperforms ResNet-50 and ViT-S with comparable parameter size, and saves significant amount of GPU memory during training. Besides, BI-Mamba achieves promising performance compared with previous state of the art in CT, unraveling the potential of chest X-ray for CVD risk prediction.