Basis pursuit deconvolution for improving model-based reconstructed images in photoacoustic tomography

Abstract

The model-based image reconstruction approaches in photoacoustic tomography have a distinct advantage compared to traditional analytical methods for cases where limited data is available. These methods typically deploy Tikhonov based regularization scheme to reconstruct the initial pressure from the boundary acoustic data. The model-resolution for these cases represents the blur induced by the regularization scheme. A method that utilizes this blurring model and performs the basis pursuit deconvolution to improve the quantitative accuracy of the reconstructed photoacoustic image is proposed and shown to be superior compared to other traditional methods via three numerical experiments. Moreover, this deconvolution including the building of an approximate blur matrix is achieved via the Lanczos bidagonalization (least-squares QR) making this approach attractive in real-time.

Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography

“…Moreover, the regularization parameter always filters some of the natural characteristics of the expected image. There have been attempts earlier to mitigate the effects of regularization via applying a deconvolution on top of the reconstruction step . Several methods were proposed in the past, for determination of regularization parameter such as the Morozov discrepancy principle, the Generalized Cross Validation (GCV), and the L‐curve method .…”

“…Subsequent to this work, an error estimate‐based method was proposed and shown to be computationally efficient compared to the least‐squares method in determining this regularization parameter automatically . Despite all these developments, automated evaluation of regularization parameter is still computationally demanding and the metric for an automated choice of regularization parameter may not provide the desired reconstructed image characteristics as regularization inherently blurs the reconstructed image . The ideal case will be to compute the regularization‐free solution that does not have the blur (filtering of high frequencies) in the reconstructed image and is computationally efficient to deploy in real time.…”

High frame rate photoacoustic imaging at 7000 frames per second using clinical ultrasound system

“…Ultrasound detectors (single or multiple or array detectors) acquire these PA waves outside the sample boundary. Reconstruction techniques are used to form the optical absorption map of the inside of the object [16][17][18][19][20]. PAT has several advantages including deeper penetration depth, good spatial resolution, and high soft tissue contrast in comparison to other optical imaging modality like optical microscopy or optical coherence tomography.…”

“…For the light excitation source, usually, a Nd:YAG pump laser either pumps a dye laser or an Optical parametric oscillator (OPO) laser to generate light in the NIR wavelengths. But, these types of lasers are bulky (often requiring an optical table to house the laser), expensive, and slow (typical pulse repetition rate for such lasers is [10][11][12][13][14][15][16][17][18][19][20] Hz with ~100 mJ of energy per pulse), making the PAT system difficult to translate into clinical setup. Therefore, there is a need to develop PAT system which is compact, affordable, portable and have a higher frame rates [26,27].…”

High speed photoacoustic tomography system with low cost portable pulsed diode laser

“…Reconstruction techniques are used to map the initial pressure rise in the object which is in turn correlated to the absorption coefficient of the object. [8][9][10][11][12][13] The applications of PAT are varied and include blood vessel imaging, Sentinel lymph node imaging, breast cancer detection and various others. 6,[14][15][16][17][18] The functional aspect of the PAT allows for the identification of the oxygenation level in the blood, and total blood volume.…”