arXiv subjects

Sandeep Kumar Kalva

Publications and source records attributed to Sandeep Kumar Kalva.

3 recordsLinked to original sources

arXiv2023

Multi-scale volumetric dynamic optoacoustic and laser ultrasound (OPLUS) imaging enabled by semi-transparent optical guidance

Major biological discoveries have been made by interrogating living organisms with light. However, the limited penetration of unscattered photons within biological tissues severely limits the depth range covered by optical methods. Deep-tissue imaging has been achieved by combining light and ultrasound. Optoacoustic imaging uniquely exploits optical generation of ultrasound to render high-resolution images at depths unattainable with optical microscopy. Recently, laser ultrasound has further been suggested as a means of generating broadband acoustic waves for high-resolution pulse-echo ultrasound imaging. Herein, we propose an approach to simultaneously interrogate biological tissues with light and ultrasound based on layer-by-layer coating of silica optical fibers with a controlled degree of transparency. We exploit the time separation between optoacoustic signals and ultrasound echoes collected with a custom-made spherical array transducer for simultaneous three-dimensional optoacoustic and laser ultrasound (OPLUS) imaging with a single laser pulse. OPLUS is shown to enable large-scale comprehensive anatomical characterization of tissues along with functional multi-spectral imaging of spectrally-distinctive chromophores and assessment of cardiac dynamics at ultrafast rates only limited by the pulse repetition frequency of the laser. The suggested approach provides a flexible and scalable means for developing a new generation of systems synergistically combining the powerful capabilities of optoacoustics and ultrasound imaging in biology and medicine.

physics.optics↗

arXiv2020

Photo-acoustic tomographic image reconstruction from reduced data using physically inspired regularization

We propose a model-based image reconstruction method for photoacoustic tomography(PAT) involving a novel form of regularization and demonstrate its ability to recover good quality images from significantly reduced size datasets. The regularization is constructed to suit the physical structure of typical PAT images. We construct it by combining second-order derivatives and intensity into a non-convex form to exploit a structural property of PAT images that we observe: in PAT images, high intensities and high second-order derivatives are jointly sparse. The specific form of regularization constructed here is a modification of the form proposed for fluorescence image restoration. This regularization is combined with a data fidelity cost, and the required image is obtained as the minimizer of this cost. As this regularization is non-convex, the efficiency of the minimization method is crucial in obtaining artifact-free reconstructions. We develop a custom minimization method for efficiently handling this non-convex minimization problem. Further, as non-convex minimization requires a large number of iterations and the PAT forward model in the data-fidelity term has to be applied in the iterations, we propose a computational structure for efficient implementation of the forward model with reduced memory requirements. We evaluate the proposed method on both simulated and real measured data sets and compare them with a recent reconstruction method that is based on a well-known fast iterative shrinkage threshold algorithm (FISTA).

eess.IV↗

arXiv2020

Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography

The quality of the reconstructed photoacoustic image largely depends on the amount of photoacoustic (PA) boundary data available, which in turn is proportional to the number of detectors employed. In case of limited data (owing to less number of detectors due to cost/instrumentation constraints), the reconstructed PA images suffer from artifacts and are often noisy. In this work, for the first time, a deep learning based model was developed to super resolve and denoise the photoacoustic sinogram data. The proposed method was compared with existing nearest neighbor interpolation and wavelet based denoising techniques and was shown to outperform them both in numerical and in-vivo cases. The improvement obtained in Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) for the reconstructed PA image using the sinogram data that was super-resolved and denoised using proposed neural network based method was as high as 41.70 % and 6.93 dB respectively compared to utilizing limited sinogram data.

eess.IV↗