Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Ultrasound localization microscopy (ULM) breaks the diffraction limit and allows imaging microvasculature at micrometric resolution while preserving the penetration depth. Frame rate plays an important role for high-quality ULM imaging, but there is still a lack of review and investigation of the frame rate effects on ULM. This work aims to clarify how frame rate influences the performance of ULM, including the effects of microbubble detection, localization and tracking. The performance of ULM was evaluated using an in vivo rat brain dataset (15.6 MHz, 3 tilted plane waves (-5°, 0°, +5°), at a compounded frame rate of 1000 Hz) with different frame rates. Quantification methods, including Fourier ring correlation and saturation parameter, were applied to analyze the spatial resolution and reconstruction efficiency, respectively. In addition, effects on each crucial step in ULM processing were further analyzed. Results showed that when frame rates dropped from 1000 Hz to 250 Hz, the spatial resolution deteriorated from 9.9 μm to 15.0 μm. Applying a velocity constraint was able to improve the ULM performance, but inappropriate constraint may artificially result in high apparent resolution. For the dataset, compared with the results of 1000 Hz frame rate, the velocity was underestimated at 100 Hz with 47.18% difference and the saturation was reduced from 55.00% at 1000 Hz to 43.34% at 100 Hz. Analysis showed that inadequate frame rate generated unreliable microbubble detection, localization and tracking as well as incomplete track reconstruction, resulting in the deterioration in spatial resolution, the underestimation in velocity measurement and the decrease in saturation. Finally, a guidance of determining the frame rate requirement was discussed by considering the required spatial sampling points based on vessel morphology, clutter filtering method, tracking algorithm and acquisition time, which provides indications for future clinical application of ULM method.
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http://dx.doi.org/10.1016/j.ultras.2023.107009 | DOI Listing |