Investigating Effects of Participant Variation on Performance of Visual Stimuli Reconstruction From fMRI Signals Using Machine Learning

Image reconstruction from neural activation data is a field that has been growing in popularity with developments such as neuralink in the brain-machine interface space. To make better decisions when collecting data for this purpose, it is important to know what qualities to optimize for. The present paper investigates the relation between...

Intravascular Ultrasound and Photoacoustic Image Reconstruction: For Improved Lipid Imaging in Atherosclerotic Coronary Arteries

Introduction: Coronary atherosclerosis can form large lipid-rich plaques inside the arteries, which are prone to rupture. Rupture can lead to thrombus formation and subsequent obstruction of the blood flow, which is the most common cause of acute coronary events like myocardial infarction. Current imaging modalities lack the ability to image...

Image Reconstruction for a Handheld Low-Field MRI Scanner via Deep Learning

MRI has been used worldwide as an imaging modality for decades due to its ability to distinguish between soft tissues. However, MRIs cannot be used in all situations due to their extensive hard- and software. For this reason, research in the field of low-field MRI has been conducted. Low-field MRI systems do not use superconducting magnets but...

Recurrent Inference Machines for accelerated MRI acquisition trained using simulated undersampled k-space data

Recently, many advancements have been made in accelerated MRI reconstruction with the use of neural networks. Such deep learning methods learn a suitable MRI prior distribution from large sets of training data. For MRI images acquired with an uncommon scanning sequence, large datasets required for training are not available. Additionally, deep...

MR reconstruction of FLAIR weighted images with simulated lesions: A comparison between Compressed Sensing and a Recurrent Inference Machine

Background: For both hospitals and patients it would be beneficial if the scan time of MR images could be reduced. At the moment, Compressed Sensing (CS) is introduced to reduce the scan time, however, new methods are developed such as a deep learning method, called the Recurrent Inference Machine (RIM). In this study the effect of...

Verbeterde beeldreconstructie bij CT-scanners

De CT-scanner heeft een belangrijke rol in de medische wereld. Helaas gebruikt de CT-scanner voor het opstellen van een scan schadelijke straling. In dit onderzoek is nagegaan of er een betere beeldreconstructietechniek te vinden is voor het opstellen van een CT-afbeelding met gebruik van minder straling.<br/>De standaard voor het opstellen van...