Contrast-detail analysis for detection and characterization with near-infrared diffuse tomography.
Near-infrared (NIR) diffuse tomography is emerging as a medical imaging modality for obtaining information related to tissue hemoglobin concentration and oxygen saturation and may be used for characterizing diseased tissues such as breast cancer. The optimal methodology for NIR image reconstruction remains an ongoing research problem with several new approaches being demonstrated in recent years. However, a comparison of reconstruction methods is problematic because tools for the objective assessment of image quality have yet to be clearly defined for this type of nonlinear reconstruction problem. Contrast-detail analysis has become an accepted assessment tool to quantify x-ray mammography image quality, and in this study it has been applied to a prototype NIR diffuse tomography system that is being evaluated for breast cancer characterization. The minimum detectable levels of contrast have been defined for different sizes of objects, and the minimum contrasts which can be accurately reconstructed have also been determined for the same object sizes. In general, objects 8 mm and larger in diameter can be accurately reconstructed and detected for most absorption contrasts which are observed in human tissues (i.e., greater than 1% contrast in absorption). Objects as small as 2 mm can be detected with high contrast (i.e., near 100%), but cannot be accurately reconstructed. Within the size range of 2 mm to 8 mm, there is an inverse correlation between contrast and detail size which is characteristic of the total noise in the system. This analysis provides an objective method for assessing detection and characterization limits and can be applied to future improvements in hardware system architecture as well as reconstruction algorithms.
Pogue
B W
BW
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755, USA. brian.pogue@dartmouth.edu
Willscher
C
C
McBride
T O
TO
Osterberg
U L
UL
Paulsen
K D
KD
D000465:Algorithms; D001943:Breast Neoplasms / Q000175:diagnosis; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007259:Infrared Rays; D015233:Models, Statistical; D019047:Phantoms, Imaging; D012680:Sensitivity and Specificity; D014054:Tomography / Q000295:instrumentation* / Q000379:methods*; D014965:X-Rays