Hallucination Index: An Image Quality Metric for Generative Reconstruction Models

Generative image reconstruction algorithms such as measurement conditioned diffusion models are increasingly popular in the field of medical imaging. These powerful models can transform low signal-to-noise ratio (SNR) inputs into outputs with the appearance of high SNR. However, the outputs can have a new type of error called hallucinations. In medical imaging, these hallucinations may not be obvious to a Radiologist but could cause diagnostic errors. Generally, hallucination refers to error in estimation of object structure caused by a machine learning model, but there is no widely accepted method to evaluate hallucination magnitude. In this work, we propose a new image quality metric called the hallucination index. Our approach is to compute the Hellinger distance from the distribution of reconstructed images to a zero hallucination reference distribution. To evaluate our approach, we conducted a numerical experiment with electron microscopy images, simulated noisy measurements, and applied diffusion based reconstructions. We sampled the measurements and the generative reconstructions repeatedly to compute the sample mean and covariance. For the zero hallucination reference, we used the forward diffusion process applied to ground truth. Our results show that higher measurement SNR leads to lower hallucination index for the same apparent image quality. We also evaluated the impact of early stopping in the reverse diffusion process and found that more modest denoising strengths can reduce hallucination. We believe this metric could be useful for evaluation of generative image reconstructions or as a warning label to inform radiologists about the degree of hallucinations in medical images.

Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction

Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals, with changes in cortical thickness (CTh) closely linked to its progression. Accurately forecasting CTh trajectories can significantly enhance early diagnosis and intervention strategies, providing timely care. However, the longitudinal data essential for these studies often suffer from temporal sparsity and incompleteness, presenting substantial challenges in modeling the disease's progression accurately. Existing methods are limited, focusing primarily on datasets without missing entries or requiring predefined assumptions about CTh progression. To overcome these obstacles, we propose a conditional score-based diffusion model specifically designed to generate CTh trajectories with the given baseline information, such as age, sex, and initial diagnosis. Our conditional diffusion model utilizes all available data during the training phase to make predictions based solely on baseline information during inference without needing prior history about CTh progression. The prediction accuracy of the proposed CTh prediction pipeline using a conditional score-based model was compared for sub-groups consisting of cognitively normal, mild cognitive impairment, and AD subjects. The Bland-Altman analysis shows our diffusion-based prediction model has a near-zero bias with narrow 95% confidential interval compared to the ground-truth CTh in 6-36 months. In addition, our conditional diffusion model has a stochastic generative nature, therefore, we demonstrated an uncertainty analysis of patient-specific CTh prediction through multiple realizations.

Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and Denoising

Conditional diffusion models have gained recognition for their effectiveness in image restoration tasks, yet their iterative denoising process, starting from Gaussian noise, often leads to slow inference speeds. As a promising alternative, the Image-to-Image Schr\"odinger Bridge (I2SB) initializes the generative process from corrupted images and integrates training techniques from conditional diffusion models. In this study, we extended the I2SB method by introducing the Implicit Image-to-Image Schrodinger Bridge (I3SB), transitioning its generative process to a non-Markovian process by incorporating corrupted images in each generative step. This enhancement empowers I3SB to generate images with better texture restoration using a small number of generative steps. The proposed method was validated on CT super-resolution and denoising tasks and outperformed existing methods, including the conditional denoising diffusion probabilistic model (cDDPM) and I2SB, in both visual quality and quantitative metrics. These findings underscore the potential of I3SB in improving medical image restoration by providing fast and accurate generative modeling.