4 research outputs found
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution
Motion estimation across low-resolution frames and the reconstruction of
high-resolution images are two coupled subproblems of multi-frame
super-resolution. This paper introduces a new joint optimization approach for
motion estimation and image reconstruction to address this interdependence. Our
method is formulated via non-linear least squares optimization and combines two
principles of robust super-resolution. First, to enhance the robustness of the
joint estimation, we propose a confidence-aware energy minimization framework
augmented with sparse regularization. Second, we develop a tailor-made
Levenberg-Marquardt iteration scheme to jointly estimate motion parameters and
the high-resolution image along with the corresponding model confidence
parameters. Our experiments on simulated and real images confirm that the
proposed approach outperforms decoupled motion estimation and image
reconstruction as well as related state-of-the-art joint estimation algorithms.Comment: accepted for ICIP 201
Attribute Regularized Soft Introspective VAE: Towards Cardiac Attribute Regularization Through MRI Domains
Deep generative models have emerged as influential instruments for data
generation and manipulation. Enhancing the controllability of these models by
selectively modifying data attributes has been a recent focus. Variational
Autoencoders (VAEs) have shown promise in capturing hidden attributes but often
produce blurry reconstructions. Controlling these attributes through different
imaging domains is difficult in medical imaging. Recently, Soft Introspective
VAE leverage the benefits of both VAEs and Generative Adversarial Networks
(GANs), which have demonstrated impressive image synthesis capabilities, by
incorporating an adversarial loss into VAE training. In this work, we propose
the Attributed Soft Introspective VAE (Attri-SIVAE) by incorporating an
attribute regularized loss, into the Soft-Intro VAE framework. We evaluate
experimentally the proposed method on cardiac MRI data from different domains,
such as various scanner vendors and acquisition centers. The proposed method
achieves similar performance in terms of reconstruction and regularization
compared to the state-of-the-art Attributed regularized VAE but additionally
also succeeds in keeping the same regularization level when tested on a
different dataset, unlike the compared method
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI
Self-supervised models allow (pre-)training on unlabeled data and therefore
have the potential to overcome the need for large annotated cohorts. One
leading self-supervised model is the masked autoencoder (MAE) which was
developed on natural imaging data. The MAE is masking out a high fraction of
visual transformer (ViT) input patches, to then recover the uncorrupted images
as a pretraining task. In this work, we extend MAE to perform anomaly detection
on breast magnetic resonance imaging (MRI). This new model, coined masked
autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced
MRI sequences, aiming at lesion detection without the need for intravenous
injection of contrast media and temporal image acquisition. During training,
only non-cancerous images are presented to the model, with the purpose of
localizing anomalous tumor regions during test time. We use a public dataset
for model development. Performance of the architecture is evaluated in
reference to subtraction images created from dynamic contrast enhanced
(DCE)-MRI