X-ray exposure during image guided interventions can be important for the patient as well as for the medical staff.
Therefore dose reduction is a major concern. Nevertheless, decreasing the dose per image affects significantly the image
quality. As a matter of fact, this tends to increase the noise and reduce the contrast. Hence, we propose a new and
efficient method to reduce the noise in low dose fluoroscopic sequences. Many studies in that domain have been proposed
implementing either multi-scale approaches using wavelet with its derivatives or using filters in the direct space. Our work
is based on a spatio-temporal denoising filter using the curvelet transform. Indeed, this sparse transform represents well
smooth images with edges and can be applied to fluoroscopic images in order to achieve robust denoising performances.
Therefore, we propose to combine a temporal recursive filter with a spatial curvelet filter. Our work is focused on the use of
the statistical dependencies between the curvelet coefficients in order to optimize the threshold function. Determining the
correlation among coefficients allows to detect which coefficients represent the relevant signal. Thus, our method allows
to diminish or even to erase curvelet-like artefacts. The performances and robustness of the proposed method are assessed
both on synthetic and real low dose sequences (ie: 20 nGy/frame).
Forward and Backward projections are two computational costly steps in tomography image reconstruction such
as Positron Emission Tomography (PET). To speed-up reconstruction time, a hardware projection/backprojection
pair has been built following algorithm architecture adequacy principles. Thanks to an original memory access
strategy based on an 3D adaptive and predictive memory cache, the external memory wall has been overcome.
Thus, for both projector architectures several units run efficiently. Each unit reaches a computational throughput
close to 1 operation per cycle.
In this paper, we present how from our hardware projection/backprojection pair, an analytic (3D-RP) and an
iterative (3D-EM) reconstruction algorithms can be implemented on a System on Programmable Chip (SoPC).
First, an hardware/software partitioning is done based on the different steps of each algorithm. Then the
reconstruction system is composed of two hardware configurations of the programmable logic resources (FPGA).
Each one corresponds mainly to the projection and backprojection step.
Our projector/backprojector has been validated with a software 3D-RP and 3D-EM reconstruction on simulated
PET-SORTEO data. A reconstruction time evaluation of these reconstruction systems are done based
on the measured performances of our projectors IPs and the estimated performances of the additional simple
hardware IPs. The expected reconstruction time is compared with the software tomography distribution STIR.
A speed-up of 7 can be expected for the 3D-RP algorithm and a speed-up of 3.5 for the 3D-EM algorithm. For
both algorithms, the architecture cycle efficiency expected is largely greater than the software implementation: 120 times for 3D-RP and 60 times for 3D-EM.