A boundary element approach for image-guided near-infrared absorption and scatter estimation.
Multimodality NIR spectroscopy systems offer the possibility of region-based vascular and molecular characterization of tissue in vivo. However, computationally efficient 3D image reconstruction algorithms specific to these image-guided systems currently do not exist. Image reconstruction is often based on finite-element methods (FEMs), which require volume discretization. Here, a boundary element method (BEM) is presented using only surface discretization to recover the optical properties in an image-guided setting. The reconstruction of optical properties using BEM was evaluated in a domain containing a 30 mm inclusion embedded in two layer media with different noise levels and initial estimates. For 5% noise in measurements, and background starting values for reconstruction, the optical properties were recovered to within a mean error of 6.8%. When compared with FEM for this case, BEM showed a 28% improvement in computational time. BEM was also applied to experimental data collected from a gelatin phantom with a 25 mm inclusion and could recover the true absorption to within 6% of expected values using less time for computation compared with FEM. When applied to a patient-specific breast mesh generated using MRI, with a 2 cm ductal carcinoma, BEM showed successful recovery of optical properties with less than 5% error in absorption and 1% error in scattering, using measurements with 1% noise. With simpler and faster meshing schemes required for surface grids as compared with volume grids, BEM offers a powerful and potentially more feasible alternative for high-resolution 3D image-guided NIR spectroscopy.
Srinivasan
Subhadra
S
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755, USA. subha@dartmouth.edu
Pogue
Brian W
BW
Carpenter
Colin
C
Yalavarthy
Phaneendra K
PK
Paulsen
Keith
K
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging / Q000473:pathology; D004058:Diffusion; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging*; D019047:Phantoms, Imaging; D011859:Radiography; D012542:Scattering, Radiation; D012984:Software; D013053:Spectrophotometry / Q000379:methods; D019265:Spectroscopy, Near-Infrared