TY - JOUR
T1 - Objective assessment of image quality VI
T2 - Imaging in radiation therapy
AU - Barrett, Harrison H.
AU - Kupinski, Matthew A.
AU - Müeller, Stefan
AU - Halpern, Howard J.
AU - Morris, John C.
AU - Dwyer, Roisin
PY - 2013/11/21
Y1 - 2013/11/21
N2 - Earlier work on objective assessment of image quality (OAIQ) focused largely on estimation or classification tasks in which the desired outcome of imaging is accurate diagnosis. This paper develops a general framework for assessing imaging quality on the basis of therapeutic outcomes rather than diagnostic performance. By analogy to receiver operating characteristic (ROC) curves and their variants as used in diagnostic OAIQ, the method proposed here utilizes the therapy operating characteristic or TOC curves, which are plots of the probability of tumor control versus the probability of normal-tissue complications as the overall dose level of a radiotherapy treatment is varied. The proposed figure of merit is the area under the TOC curve, denoted AUTOC. This paper reviews an earlier exposition of the theory of TOC and AUTOC, which was specific to the assessment of image-segmentation algorithms, and extends it to other applications of imaging in external-beam radiation treatment as well as in treatment with internal radioactive sources. For each application, a methodology for computing the TOC is presented. A key difference between ROC and TOC is that the latter can be defined for a single patient rather than a population of patients.
AB - Earlier work on objective assessment of image quality (OAIQ) focused largely on estimation or classification tasks in which the desired outcome of imaging is accurate diagnosis. This paper develops a general framework for assessing imaging quality on the basis of therapeutic outcomes rather than diagnostic performance. By analogy to receiver operating characteristic (ROC) curves and their variants as used in diagnostic OAIQ, the method proposed here utilizes the therapy operating characteristic or TOC curves, which are plots of the probability of tumor control versus the probability of normal-tissue complications as the overall dose level of a radiotherapy treatment is varied. The proposed figure of merit is the area under the TOC curve, denoted AUTOC. This paper reviews an earlier exposition of the theory of TOC and AUTOC, which was specific to the assessment of image-segmentation algorithms, and extends it to other applications of imaging in external-beam radiation treatment as well as in treatment with internal radioactive sources. For each application, a methodology for computing the TOC is presented. A key difference between ROC and TOC is that the latter can be defined for a single patient rather than a population of patients.
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U2 - 10.1088/0031-9155/58/22/8197
DO - 10.1088/0031-9155/58/22/8197
M3 - Article
C2 - 24200954
AN - SCOPUS:84896501804
SN - 0031-9155
VL - 58
SP - 8197
EP - 8213
JO - Physics in medicine and biology
JF - Physics in medicine and biology
IS - 22
ER -