Near "real" time magnetic resonance images as a monitoring system for interstitial laser therapy: experimental protocols

1992 ◽  
Author(s):  
Dan J. Castro ◽  
Keyvan Farahani ◽  
Jacques Soudant ◽  
Andrew A. Zwarun ◽  
Robert B. Lufkin
Keyword(s):  
Magnetic Resonance ◽  
Real Time ◽  
Laser Therapy ◽  
Monitoring System ◽  
Magnetic Resonance Images ◽  
Interstitial Laser Therapy ◽  
Experimental Protocols ◽  
Interstitial Laser

Magnetic resonance-guided laparoscopic interstitial laser therapy of the liver

1997 ◽  
Vol 174 (4) ◽  
pp. 448-451 ◽  
Author(s):  
Hans Peter Klotz ◽  
Renata Flury ◽  
Peter Erhart ◽  
Paul Steiner ◽  
Jörg Felix Debatin ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Laser Therapy ◽  
Interstitial Laser Therapy ◽  
Interstitial Laser

Pathology of Interstitial Laser Therapy of the Breast

2003 ◽  
Vol 8 (3) ◽  
pp. 111-114
Author(s):  
Kenneth J. Bloom ◽  
Kambiz Dowlat ◽  
Lina Assad
Keyword(s):  
Laser Therapy ◽  
Interstitial Laser Therapy ◽  
Interstitial Laser

scholarly journals Implementation of Iterative bilateral filtering for removal of Rician noise in MR images using FPGA

2020 ◽  
Vol 9 (3) ◽  
pp. 279-284
Keyword(s):  
Magnetic Resonance ◽  
Real Time ◽  
Visual Analysis ◽  
Similarity Index ◽  
Structural Similarity ◽  
Bilateral Filter ◽  
Magnetic Resonance Images ◽  
Rician Noise ◽  
Diagnostic Quality ◽  
Index Matrix

Magnetic resonance image noise reduction is important to process further and visual analysis. Bilateral filter is denoises image and also preserves edge. It proposes Iterative bilateral filter which reduces Rician noise in the magnitude magnetic resonance images and retains the fine structures, edges and it also reduces the bias caused by Rician noise. The visual and diagnostic quality of the image is retained. The quantitative analysis is based on analysis of standard quality metrics parameters like peak signal-to-noise ratio and mean structural similarity index matrix reveals that these methods yields better results than the other proposed denoising methods for MRI. Problem associated with the method is that it is computationally complex hence time consuming. It is not recommended for real time applications. To use in real time application a parallel implantation of the same using FPGA is proposed.

scholarly journals Semi-automatic method for analysing vocal tract morphology from real-time magnetic resonance images

2021 ◽  
Author(s):  
Michel Belyk ◽  
Christopher Carignan ◽  
Carolyn McGettigan
Keyword(s):  
Magnetic Resonance ◽  
Real Time ◽  
Vocal Tract ◽  
Structural Features ◽  
Magnetic Resonance Images ◽  
Training Data ◽  
Surrounding Tissue ◽  
User Input ◽  
Internal Structures ◽  
Automated Processing

Real-time magnetic resonance imaging is a technique that provides high contrast videographic data of the vocal tract that allow researchers to observe the internal structures that shape the sounds of speech. However, structural features need to be extracted from these vocal tract images to make them useful to researchers. We have developed a semi-automated processing pipeline that produces outlines of the vocal tract to quantify vocal tract morphology. Our approach uses simple tissue classification constrained to pixels that analysts have identified as likely to contain the vocal tract and surrounding tissue. This approach is supplemented with multiple opportunities for the analyst to intervene in order to ensure that outputs are robust to errors. Although this approach is more labour intensive than more fully automated alternatives, these costs are offset by the benefits of improving the quality of measurements. We demonstrate that this pipeline can be generalised to a range of datasets and that it remains reliable across analysts, particularly among analysts with vocal tract expertise. The pipeline’s reliance on user input presents a challenge to scalability if applied to very large. Measurements produced by this pipeline could be provide a broader scope of training data for fully automated methods in an effort to improve their generalisability.

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