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The authors develop a matrix-free operator framework for 3D photoacoustic tomography that analytically models transducer spatial impulse responses by decomposing acoustic measurements into temporal convolutions of radial basis functions and continuous geometric surface intersections. Standard iterative reconstructions either ignore finite transducer geometry—severely blurring resolution—or rely on surface point discretizations that scale poorly to full 3D volumes. Accelerated by lookup-table approximations of elliptic integrals on GPUs, the resulting implementation matches exact continuous evaluation accuracy 10x faster than standard quadrature baselines and enables full-scale vascular reconstruction with superior resolution and PSNR.
Full-scale 3D photoacoustic imaging no longer requires trading transducer geometry for computational feasibility: replacing surface discretization with analytical geometric intersections delivers exact forward-operator modeling at 10x the speed.
Iterative model-based reconstruction in photoacoustic tomography repeatedly applies the forward operator mapping the initial pressure to the transducer signals, and its adjoint. At the scale of current three-dimensional systems, this operator cannot be stored and must be evaluated matrix-free, while accounting for the finite, focused surface of the transducers, whose spatial impulse response degrades the resolution when ignored. Representing the initial pressure by compactly supported radial functions, we show that the measured signal is exactly a temporal convolution between a system kernel gathering the radial function and the electrical impulse response, and a purely geometric quantity accounting for the portion of the transducer surface reached by the wave emitted from a voxel during one time step. Two implementations are proposed, differing only in how this quantity is evaluated: a quadrature over points of the surface, as in existing works, or a closed-form area, which never discretizes the surface. We derive closed forms for planar and cylindrically focused transducers and provide, in the latter case, two accelerations of the resulting elliptic integrals, a lookup table and a trapezoidal approximation, together with the piecewise planar approximation customary in the literature. These implementations reduce the per-voxel geometric computations and are released as an open-source Python package for graphics processing units. The performance of these operators is first demonstrated on a synthetic phantom, where the lookup-table-based operator reaches the accuracy of the exact evaluation ten times faster and outperforms the point discretization on both accuracy and runtime. A second experiment shows that they enable the processing of a realistic vascular phantom at full scale, with a higher peak signalto-noise ratio and a better resolution than the back-projection counterpart. The released implementations are an important step towards the adoption of three-dimensional modelbased photoacoustic reconstructions with finite and focused transducers.
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