TY - GEN
T1 - Compressed quantitative acoustic microscopy
AU - Kim, J. H.
AU - Hill, P. R.
AU - Canagarajah, N.
AU - Rohrbach, D.
AU - Kouame, D.
AU - Mamou, J.
AU - Achim, A.
AU - Basarab, A.
PY - 2017/11/2
Y1 - 2017/11/2
N2 - Scanning acoustic microscopy is a well-accepted modality for forming quantitative 2D maps of acoustic properties of soft tissues at microscopic scales. In our studies, the sample is raster-scanned with a spatial step size of 2 μm using a 250 MHz transducer resulting in 3D RF data cubes. Each RF signal is processed to obtain, for each spatial location, acoustic parameters, e.g., the speed of sound. The scanning time is directly dependent on the sample size and can range from few minutes to hours. In order to maintain constant experimental conditions for the sensitive thin sectioned samples, the scanning time is an important practical issue. Hence, the main objective of this work is to reduce the scanning time by reconstructing acoustic microscopy images from spatially under sampled measurements, based on the theory of compressive sampling. A recently proposed approximate message passing method using a Cauchy maximum a posteriori image denoising algorithm is thus employed to account for the non-Gaussianity of quantitative acoustic microscopy wavelet coefficients.
AB - Scanning acoustic microscopy is a well-accepted modality for forming quantitative 2D maps of acoustic properties of soft tissues at microscopic scales. In our studies, the sample is raster-scanned with a spatial step size of 2 μm using a 250 MHz transducer resulting in 3D RF data cubes. Each RF signal is processed to obtain, for each spatial location, acoustic parameters, e.g., the speed of sound. The scanning time is directly dependent on the sample size and can range from few minutes to hours. In order to maintain constant experimental conditions for the sensitive thin sectioned samples, the scanning time is an important practical issue. Hence, the main objective of this work is to reduce the scanning time by reconstructing acoustic microscopy images from spatially under sampled measurements, based on the theory of compressive sampling. A recently proposed approximate message passing method using a Cauchy maximum a posteriori image denoising algorithm is thus employed to account for the non-Gaussianity of quantitative acoustic microscopy wavelet coefficients.
KW - Approximate message passing
KW - Cauchy distribution
KW - Compressive sampling
KW - Scanning acoustic microscopy
UR - http://www.scopus.com/inward/record.url?scp=85039416872&partnerID=8YFLogxK
U2 - 10.1109/ULTSYM.2017.8092328
DO - 10.1109/ULTSYM.2017.8092328
M3 - Conference Contribution (Conference Proceeding)
AN - SCOPUS:85039416872
SN - 9781538633847
BT - 2017 IEEE International Ultrasonics Symposium (IUS 2017)
PB - Institute of Electrical and Electronics Engineers (IEEE)
T2 - 2017 IEEE International Ultrasonics Symposium, IUS 2017
Y2 - 6 September 2017 through 9 September 2017
ER -