Name
Title | ||
|---|---|---|
Eigensensing and deconvolution for the reconstruction of heat absorption profiles from photoacoustic tomography data |
Abstract | ||
|---|---|---|
Photoacoustic tomography (PAT) is a relatively recent imaging modality that is promising for breast cancer detection and breast screening. It combines the high intrinsic contrast of optical radiation with acoustic imaging at submillimeter spatial resolution through the photoacoustic effect of absorption and thermal expansion. However, image reconstruction from boundary measurements of the propagating wave field is still a challenging inverse problem. Here we propose a new theoretical framework, for which we coin the term eigensensing, to recover the heat absorption profile of the tissue. One of the main features of our method is that there is no explicit forward model that needs to be used within a (usually) slow iterative scheme. Instead, the eigensensing principle allow us to computationally obtain several intermediate images that are blurred by known convolution kernels which are chosen as the eigenfunctions of the spatial Laplace operator. The source image can then be reconstructed by a joint deconvolution algorithm that uses the intermediate images as input. Moreover, total variation regularization is added to make the inverse problem well-posed and to favor piecewise-smooth images. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ISBI.2013.6556684 | Biomedical Imaging |
Keywords | Field | DocType |
Laplace equations,acoustic tomography,biothermics,cancer,deconvolution,eigenvalues and eigenfunctions,image resolution,image restoration,inverse problems,medical image processing,optical tomography,thermal expansion,tumours,acoustic imaging,boundary measurements,breast cancer detection,breast screening,convolution kernels,eigenfunctions,eigensensing,heat absorption profile reconstruction,high-intrinsic contrast,image deblurring,image reconstruction,imaging modality,inverse problem,joint deconvolution algorithm,optical radiation,photoacoustic effect,photoacoustic tomography data,piecewise-smooth images,propagating wave field,source image,spatial Laplace operator,submillimeter spatial resolution,thermal expansion,tissue,total variation regularization,Photoacoustic Tomography,Wave equation,deconvolution,joint sparsity,source imaging,total variation | Iterative reconstruction,Computer vision,Photoacoustic effect,Computer science,Deconvolution,Optics,Total variation denoising,Inverse problem,Artificial intelligence,Optical tomography,Image restoration,Image resolution | Conference |
ISSN | ISBN | Citations |
1945-7928 | 978-1-4673-6456-0 | 0 |
PageRank | References | Authors |
0.34 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zafer Dogan | 1 | 12 | 1.93 |
| T Blu | 2 | 2574 | 259.70 |
| Dimitri Van De Ville | 3 | 1656 | 118.48 |