Technology Development

This system has undergone substantial technological advancement from the early work to the current state of the system. However, advancements can still be made to improve the quality of the system to optimize performance. This includes not only image fidelity, but also data acquisition and reconstruction time. This section discusses these ideas in the context of improvements to be made to the PAI system.

5.1.1 Calibration protocol

Improvements made to the calibration procedure developed in Chapter 2 could vastly improve system performance as related to image quality. The motivation for using a liquid-based photoacoustic point source stemmed from the inherent behaviour of the liquid to produce a uniform PA wave in the azimuth direction. Earlier PA sources

developed displayed significant deviation in PA wave magnitude over the azimuth due to polishing imperfections in the fibre. That said, the variability in PA wave magnitude in the zenith direction is still a concern, despite a marked improvement from previous design iterations developed in our group’s earlier work. The development of a source that emits PA waves of greater uniformity will improve the fidelity to the linear model when constructing the imaging operator. This will be especially important for sources detected by the transducers in the upper rows of the hemisphere, as they are most impacted by the reduction in signal strength at increasing zenith elevations.

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The calibration procedure is performed in a solution with acoustic properties similar to water. This approach could be modified such that the calibration is done in a defined object space with properties more closely mimicking tissue. The PA waves recorded on transducers are not adapted to the changes that would be seen true of imaging tasks performed in real tissue. That is, the amplitude, FWHM, and time-of-flight of the waves emanating from a particular voxel may change if the imaging task is done in a medium with different acoustic properties. While these changes are expected to be minimal, it could become significant as imaging depths are increased. To this point, the temperature at which the calibration procedure is performed should be regulated to match the expected imaging task. Currently, the scan is performed with both the source and water tank at room temperature. The speed of sound will vary if the source is expected to be in vivo applications (mouse, human, etc.).

Beam inhomogeneity during the procedure has also produced inaccuracies in the results. In the current approach, the fibre collects light directly from the beam as it exits the laser aperture, which is considered relatively inhomogeneous in energy distribution. Therefore, the pulse-to-pulse variation in energy delivered to the point source can be influential on the recorded PA waves. While the photodiode was implemented to sample this variation, the fibre itself is bifurcated such that the light collected that is directed to the photodiode is separate from the light that is directed to the PA source. A beam

homogenizing system has recently been implemented using a micro lens array to produce a beam with greater uniformity that is collected by the fibre.

The approach to acquiring an experimental imaging operator itself can be modified if a simulated imaging operator can be modeled in a more accurate and simple technique. If analytical modeling of transducer behaviour is performed, these data could be used in producing a simulated imaging operator. In this context, it would be useful to address not only for potential accuracy improvement in the reconstruction approach but also reconstruction simulations could be made in order to evaluate potential system designs before physical realizations of the system are constructed. At this time, however,

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it is unknown whether the experimental or simulated approach to PAI will be more accurate.

5.1.2 System improvements

As described in Chapter 4, the current achievable frame rate is approximately 0.7 fps. This is limited largely by the rate of data acquisition and to a lesser extent, the image reconstruction computation time. In order to advance the frame rate, improvements in both data acquisition rate and image reconstruction time must be made.

Currently, the data acquisition electronics typically record 5120 data points to sample the entire object space. However, many of these data points do not contain useful information because they originate from a position outside the defined object space. This is due to the distance between each of the transducers and the centre of the hemisphere. Therefore, a time delay could be introduced such that the data acquisition system only acquires data from the region defined by the object space. Currently, a maximum of 2000 out of the possible 5120 data points are used in the construction of the imaging operator. Even in the largest object space used, that would reduce the data acquisition time by a factor of approximately 2.5. Of course, in object spaces of smaller dimension (possible for some imaging tasks), the number of data points used could be even smaller, resulting in an even greater reduction in data acquisition time.

After the data has been transferred to the PC for image reconstruction, there is an intermediate step, which requires the processing of the data set such that it is in the same format as the data that composes the imaging operator. This is done using custom-built software that is called during the Labview™ sequence. Presumably, this same processing could be done with built-in Labview™ techniques. While the increase may be relatively minor in comparison to the data acquisition and image reconstruction times, it is nevertheless an improvement that could reduce the overall sequence time by tens of milliseconds.

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Data acquisition is performed in a serial manner from each of the 4 electronic boards (containing 8 channels). If parallel communication was taken advantage, an increase in transfer rate by a factor of 4 could be expected. As well, image reconstruction can take up to 100 ms with the existing imaging operators by utilizing MATLAB® to perform the matrix multiplication. Utilizing higher speed processing (GPUs), this could be reduced significantly as well. It is difficult to comment on the degree to which this could improve reconstruction time as many factors influence the matrix multiplication. This includes the number of non-zero entries in the imaging operator, H, and data set, g, making it difficult to predict any precise reduction in computation time. However, other operations performed in MATLAB® utilizing GPU processing techniques have reduced the computation time by a factor of anywhere between approximately 2 and 40.

System noise can contribute significantly to image reconstruction problems as it propagates from sampled data to the imaging operator. In order to understand system limitations and the consequence of noise on image reconstruction, objective assessment of these features will need to be performed. With quantified understanding of noise on system performance, improvements can be made as well as limitations more

comprehensively understood.