Frequently MRI data is characterised by a relatively low signal to noise ratio (SNR) or contrast to noise ratio (CNR). When developing automated Computer Assisted Diagnostic (CAD) techniques the errors introduced by the image noise are not acceptable. Thus, to limit these errors, a solution is to filter the data in order to increase the SNR. More importantly, the image filtering technique should be able to reduce the level of noise, but not at the expense of feature preservation. In this paper we detail the implementation of a number of 3D diffusion-based filtering techniques and we analyse their performance when they are applied to a large collection of MR datasets of varying type and quality.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.compmedimag.2004.12.003 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Real-Time Tractography-Assisted Neuronavigation for Transcranial Magnetic Stimulation.
Hum Brain Mapp
January 2025
Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland.
State-of-the-art navigated transcranial magnetic stimulation (nTMS) systems can display the TMS coil position relative to the structural magnetic resonance image (MRI) of the subject's brain and calculate the induced electric field. However, the local effect of TMS propagates via the white-matter network to different areas of the brain, and currently there is no commercial or research neuronavigation system that can highlight in real time the brain's structural connections during TMS. This lack of real-time visualization may overlook critical inter-individual differences in brain connectivity and does not provide the opportunity to target brain networks.
View Article and Find Full Text PDFActive noise cancellation gets a boost: A novel diffusion-based approach in spline adaptive filters.
ISA Trans
December 2024
Department of Computer Science, Semnan University, Semnan, Iran. Electronic address:
This study addresses the challenge of unwanted noise in signal processing, particularly for applications requiring high-fidelity audio like noise-canceling headphones. Current adaptive filters offer some noise reduction but struggle with specific noise profiles. We propose the enhanced adaptive filter and a distributed learning utilizing a novel diffusion-based framework that leverages spline adaptation.
View Article and Find Full Text PDFSuppressing the HIFU interference in ultrasound guiding images with a diffusion-based deep learning model.
Comput Methods Programs Biomed
September 2024
State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China. Electronic address:
Background And Objectives: In ultrasound guided high-intensity focused ultrasound (HIFU) surgery, it is necessary to transmit sound waves at different frequencies simultaneously using two transducers: one for the HIFU therapy and another for the ultrasound imaging guidance. In this specific setting, real-time monitoring of non-invasive surgery is challenging due to severe contamination of the ultrasound guiding images by strong acoustic interference from the HIFU sonication.
Methods: This paper proposed the use of a deep learning (DL) solution, specifically a diffusion implicit model, to suppress the HIFU interference.
DTAN: Diffusion-based Text Attention Network for medical image segmentation.
Comput Biol Med
January 2024
School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China. Electronic address:
In the current era, diffusion models have emerged as a groundbreaking force in the realm of medical image segmentation. Against this backdrop, we introduce the Diffusion Text-Attention Network (DTAN), a pioneering segmentation framework that amalgamates the principles of text attention with diffusion models to enhance the precision and integrity of medical image segmentation. Our proposed DTAN architecture is designed to steer the segmentation process towards areas of interest by leveraging a text attention mechanism.
View Article and Find Full Text PDFZero-Shot Medical Image Translation via Frequency-Guided Diffusion Models.
IEEE Trans Med Imaging
March 2024
Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!