Haider, Clifton R.

Optoacoustic, or photoacoustic, imaging combines the penetration capabilities of ultrasound imaging with the contrast mechanism of optical absorption related to the photoacoustic effect. To enable modeling of photoacoustic measurements and imaging applications, the problem can be divided into modeling of the optical photon propagation and the resulting acoustic wave propagation. In highly scattering media such as soft tissues, Monte Carlo (MC) methods are used to evaluate photon propagation, absorption and the subsequent generation of a heat source that produces the photoacoustic effect. Acoustic wave equations are then used to model the propagation of the sound in the medium. However, if the appropriate physics could be modeled with a single tool, there would be advantages of consistency in spatial and temporal domains as well as easier integration of the optical and acoustic simulations. We propose using MC methods for optical photon propagation and for the acoustic wave propagation. The simulations are split into two stages for the optical photon propagation and the acoustic phonon propagation. We describe how the same MC framework is used to model photon propagation and acoustic phonon propagation. We will then demonstrate this new combined MC framework in models of photoacoustic problems in homogeneous and heterogeneous cases. These results will be compared with results from using k-Wave models for the acoustic wave propagation. The correspondence between k-Wave the acoustic MC model are shown to be in good agreement.

Compressive sensing (CS) describes how sparse signals can be accurately reconstructed from many fewer samples than required by the Nyquist criterion. Since MRI scan duration is proportional to the number of acquired samples, CS has been gaining significant attention in MRI. However, the computationally intensive nature of CS reconstructions has precluded their use in routine clinical practice. In this work, we investigate how different throughput-oriented architectures can benefit one CS algorithm and what levels of acceleration are feasible on different modern platforms. We demonstrate that a CUDA-based code running on an NVIDIA Tesla C2050 GPU can reconstruct a 256 × 160 × 80 volume from an 8-channel acquisition in 19 seconds, which is in itself a significant improvement over the state of the art. We then show that Intel's Knights Ferry can perform the same 3D MRI reconstruction in only 12 seconds, bringing CS methods even closer to clinical viability.