TY - JOUR
T1 - 3-D H-Scan Ultrasound Imaging and Use of a Convolutional Neural Network for Scatterer Size Estimation
AU - Tai, Haowei
AU - Khairalseed, Mawia
AU - Hoyt, Kenneth
N1 - Funding Information:
This work was supported in part by National Institutes of Health (NIH) Grant R01 EB025841 and Cancer Prevention and Research Institute of Texas (CPRIT) Award RP180670. The authors thank Lokesh Basavarajappa, Aditi Bellary, Katherine Brown, Hassan Jahanandish and Ipek Ozdemir for their insightful suggestions on this project and help with article preparation and review.
Publisher Copyright:
© 2020 World Federation for Ultrasound in Medicine & Biology
PY - 2020/10
Y1 - 2020/10
N2 - H-Scan ultrasound (US) is a new imaging technology that estimates the relative size of acoustic scattering objects and structures. The purpose of this study was to introduce a three-dimensional (3-D) H-scan US imaging approach for scatterer size estimation in volume space. Using a programmable research scanner (Vantage 256, Verasonics Inc, Kirkland, WA, USA) equipped with a custom volumetric imaging transducer (4 DL7, Vermon, Tours, France), raw radiofrequency (RF) data was collected for offline processing to generate H-scan US volumes. A deep convolutional neural network (CNN) was modified and used to achieve voxel mapping from the input H-scan US image to underlying scatterer size. Preliminary studies were conducted using homogeneous gelatin-based tissue-mimicking phantom materials embedded with acoustic scatterers of varying size (15 to 250 μm) and concentrations (0.1 to 1%). Two additional phantoms were embedded with 63 or 125 µm-sized microspheres and used to test CNN estimation accuracy. In vitro results indicate that 3-D H-scan US imaging can visualize the spatial distribution of acoustic scatterers of varying size at different concentrations (R2 > 0.85, p < 0.03). The result of scatterer size estimation reveals that a CNN can achieve an average mapping accuracy of 93.3%. Overall, our preliminary in vitro findings reveal that 3-D H-scan US imaging allows the visualization of tissue scatterer patterns and incorporation of a CNN can be used to help estimate size of the acoustic scattering objects.
AB - H-Scan ultrasound (US) is a new imaging technology that estimates the relative size of acoustic scattering objects and structures. The purpose of this study was to introduce a three-dimensional (3-D) H-scan US imaging approach for scatterer size estimation in volume space. Using a programmable research scanner (Vantage 256, Verasonics Inc, Kirkland, WA, USA) equipped with a custom volumetric imaging transducer (4 DL7, Vermon, Tours, France), raw radiofrequency (RF) data was collected for offline processing to generate H-scan US volumes. A deep convolutional neural network (CNN) was modified and used to achieve voxel mapping from the input H-scan US image to underlying scatterer size. Preliminary studies were conducted using homogeneous gelatin-based tissue-mimicking phantom materials embedded with acoustic scatterers of varying size (15 to 250 μm) and concentrations (0.1 to 1%). Two additional phantoms were embedded with 63 or 125 µm-sized microspheres and used to test CNN estimation accuracy. In vitro results indicate that 3-D H-scan US imaging can visualize the spatial distribution of acoustic scatterers of varying size at different concentrations (R2 > 0.85, p < 0.03). The result of scatterer size estimation reveals that a CNN can achieve an average mapping accuracy of 93.3%. Overall, our preliminary in vitro findings reveal that 3-D H-scan US imaging allows the visualization of tissue scatterer patterns and incorporation of a CNN can be used to help estimate size of the acoustic scattering objects.
KW - Acoustic scatterer size
KW - Convolutional neural network
KW - H-Scan ultrasound
KW - Tissue characterization
KW - Volumetric imaging
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U2 - 10.1016/j.ultrasmedbio.2020.06.001
DO - 10.1016/j.ultrasmedbio.2020.06.001
M3 - Article
C2 - 32653207
AN - SCOPUS:85087732894
VL - 46
SP - 2810
EP - 2818
JO - Ultrasound in Medicine and Biology
JF - Ultrasound in Medicine and Biology
SN - 0301-5629
IS - 10
ER -