Journal
IEEE TRANSACTIONS ON MEDICAL IMAGING
Volume 28, Issue 11, Pages 1781-1790Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2009.2024082
Keywords
Iterative image reconstruction; optoacoustic tomography; photoacoustic tomography; thermoacoustic tomography
Categories
Funding
- Pritzker Institute of Biomedical Science and Engineering [4-52681]
- National Science Foundation under CAREER Award [0546113]
- American Cancer Society Research Scholar
- Div Of Chem, Bioeng, Env, & Transp Sys
- Directorate For Engineering [0546113] Funding Source: National Science Foundation
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In the classic formulation of photoacoustic tomography (PAT), two distinct descriptions of the imaging model have been employed for developing reconstruction algorithms. We demonstrate that the numerical and statistical properties of un-weighted least-squares reconstruction algorithms associated with each imaging model are generally very different. Specifically, some PAT reconstruction algorithms, including many of the iterative algorithms previously explored, do not work directly with the raw measured pressure wavefields, but rather with an integrated data function that is obtained by temporally integrating the photoacoustic wavefield. The integration modifies the statistical distribution of the data, introducing statistical correlations among samples. This change is highly significant for iterative algorithms, many of which explicitly or implicitly seek to minimize a statistical cost function. In this work, we demonstrate that iterative reconstruction by least-squares minimization yields better resolution-noise tradeoffs when working with the raw pressure data than with the integrated data commonly employed. In addition, we demonstrate that the raw-data based approach is less sensitive to certain deterministic errors, such as dc offset errors.
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