Investigating Data Memorization in 3D Latent Diffusion Models for Medical Image Synthesis [PDF]
arXiv, 2023 Generative latent diffusion models have been established as state-of-the-art in data generation. One promising application is generation of realistic synthetic medical imaging data for open data sharing without compromising patient privacy. Despite the promise, the capacity of such models to memorize sensitive patient training data and synthesize ...
arxiv
Multi-delay arterial spin-labeled perfusion estimation with biophysics simulation and deep learning [PDF]
arXiv, 2023 Purpose: To develop biophysics-based method for estimating perfusion Q from arterial spin labeling (ASL) images using deep learning. Methods: A 3D U-Net (QTMnet) was trained to estimate perfusion from 4D tracer propagation images. The network was trained and tested on simulated 4D tracer concentration data based on artificial vasculature structure ...
arxiv
Variable density sampling based on physically plausible gradient waveform. Application to 3D MRI angiography [PDF]
arXiv, 2015 Performing k-space variable density sampling is a popular way of reducing scanning time in Magnetic Resonance Imaging (MRI). Unfortunately, given a sampling trajectory, it is not clear how to traverse it using gradient waveforms. In this paper, we actually show that existing methods [1, 2] can yield large traversal time if the trajectory contains high ...
arxiv
Deep Residual Network for Off-Resonance Artifact Correction with Application to Pediatric Body Magnetic Resonance Angiography with 3D Cones [PDF]
arXiv, 2018 Purpose: Off-resonance artifact correction by deep-learning, to facilitate rapid pediatric body imaging with a scan time efficient 3D cones trajectory. Methods: A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of 30 pediatric magnetic resonance angiography exams.
arxiv
Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging [PDF]
arXiv, 2019 Magnetic resonance imaging (MRI) is a remarkably powerful diagnostic technique: it generates wide-ranging information for the non-invasive study of tissue anatomy and physiology. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps,
arxiv
A Two-step Surface-based 3D Deep Learning Pipeline for Segmentation of Intracranial Aneurysms [PDF]
arXiv, 2020 The exact shape of intracranial aneurysms is critical in medical diagnosis and surgical planning. While voxel-based deep learning frameworks have been proposed for this segmentation task, their performance remains limited. In this study, we offer a two-step surface-based deep learning pipeline that achieves significantly higher performance.
arxiv
Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario [PDF]
We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to efficiently detect Intra-Cranial ...
arxiv +1 more source
Vessel Segmentation in Medical Imaging Using a Tight-Frame Based Algorithm [PDF]
arXiv, 2011 Tight-frame, a generalization of orthogonal wavelets, has been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolution image restoration, etc. Segmentation is the process of identifying object outlines within images.
arxiv
Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models [PDF]
arXivCerebrovascular disease often requires multiple imaging modalities for accurate diagnosis, treatment, and monitoring. Computed Tomography Angiography (CTA) and Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) are two common non-invasive angiography techniques, each with distinct strengths in accessibility, safety, and diagnostic accuracy.
arxiv
Y-net: 3D intracranial artery segmentation using a convolutional autoencoder [PDF]
arXiv, 2017 Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system.
arxiv