Are Current Technical Exclusion Criteria for Clinical Trials of Magnetic Resonance–Guided High‐Intensity Focused Ultrasound Too Restrictive?

2020 ◽  
Vol 39 (9) ◽  
pp. 1849-1855
Author(s):  
Lung W. Lau ◽  
Avinash Eranki ◽  
Haydar Celik ◽  
AeRang Kim ◽  
Peter C. W. Kim ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Magnetic Resonance ◽  
High Intensity ◽  
Focused Ultrasound ◽  
High Intensity Focused Ultrasound ◽  
Exclusion Criteria

scholarly journals Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
A. Vargas-Olivares ◽  
O. Navarro-Hinojosa ◽  
M. Maqueo-Vicencio ◽  
L. Curiel ◽  
M. Alencastre-Miranda ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Intensity ◽  
Focused Ultrasound ◽  
High Intensity Focused Ultrasound ◽  
Segmentation Algorithm ◽  
Focal Region ◽  
Therapy Planning ◽  
Mr Images ◽  
Monitoring Method ◽  
Data Set

High-intensity focused ultrasound (HIFU) is a minimally invasive therapy modality in which ultrasound beams are concentrated at a focal region, producing a rise of temperature and selective ablation within the focal volume and leaving surrounding tissues intact. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery. Magnetic resonance imaging (MRI) has been proposed as guidance and monitoring method for the therapy. The identification of regions of interest is a crucial procedure in HIFU therapy planning. This procedure is performed in the MR images. The purpose of the present research work is to implement a time-efficient and functional segmentation scheme, based on the watershed segmentation algorithm, for the MR images used for the HIFU therapy planning. The achievement of a segmentation process with functional results is feasible, but preliminary image processing steps are required in order to define the markers for the segmentation algorithm. Moreover, the segmentation scheme is applied in parallel to an MR image data set through the use of a thread pool, achieving a near real-time execution and making a contribution to solve the time-consuming problem of the HIFU therapy planning.

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