Quantitative pulmonary single photon emission computed tomography for radiotherapy applications.
Pulmonary imaging using single photon emission computed tomography (SPECT) is the focus of current radiotherapy research, including dose-response analysis and three-dimensional (3D) radiation treatment planning. Improvement in the quantitative capability of SPECT may help establish its potential role in this application as well as others requiring accurate knowledge of pulmonary blood flow. The purposes of this study were to quantitatively evaluate SPECT filtered backprojection (FBP) and ordered subset-expectation maximization (OS-EM) reconstruction implementations for measuring absolute activity concentration in lung phantom experiments, and to incorporate quantitative SPECT techniques in 3D-RTP for lung cancer. Quantitative FBP (nonuniform iterative Chang attenuation compensation, scatter correction, and 3D postreconstruction Metz filtering) and OS-EM implementations were compared with a "clinical" implementation of FBP (uniform multiplicative Chang attenuation compensation and post-reconstruction von Hann filtering), for their ability to improve quantification of inactive and active spherical defects in the lungs of an anthropomorphic torso phantom. Activity concentration estimates were found to depend on many factors, such as region of interest size, scatter subtraction constant (k), postreconstruction deconvolution filtering and, in the case of OS-EM, total number of iterations. In general, reconstruction implementations incorporating compensation for nonuniform attenuation and scatter provided reduced bias relative to the clinical implementation. Potential applications to lung radiotherapy, including dose-functional histograms and treatment planning are also discussed. SPECT has the potential to provide accurate estimates of lung activity distributions that, together with improved image quality, may be useful for the study and prediction of therapeutic response.
Scarfone
C
C
Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710, USA. christopher.scarfone@vanderbilt.edu
Jaszczak
R J
RJ
Gilland
D R
DR
Greer
K L
KL
Munley
M T
MT
Marks
L B
LB
Coleman
R E
RE
D000465:Algorithms; D055592:Biophysical Phenomena; D001703:Biophysics; D005069:Evaluation Studies as Topic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008168:Lung / Q000000981:diagnostic imaging*; D008175:Lung Neoplasms / Q000532:radiotherapy; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D012542:Scattering, Radiation; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods* / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000295:instrumentation