Abstract:

Objective. 3D-localization of gamma sources has the potential to improve the outcome of radio-guided surgery. The goal of this paper is to analyze the localization accuracy for point-like sources with a single coded aperture camera. Approach. We both simulated and measured a point-like 241Am source at 17 positions distributed within the field of view of an experimental gamma camera. The setup includes a 0.11mm thick Tungsten sheet with a MURA mask of rank 31 and pinholes of 0.08 mm in diameter and a detector based on the photon counting readout circuit Timepix3. Two methods, namely an iterative search including either a symmetric Gaussian fitting or an exponentially modified Gaussian fitting (EMG) and a center of mass method were compared to estimate the 3D source position. Main results. Considering the decreasing axial resolution with source-to-mask distance, the EMG improved the results by a factor of 4 compared to the Gaussian fitting based on the simulated data. Overall, we obtained a mean localization error of 0.77 mm on the simulated and 2.64 mm on the experimental data in the imaging range of 20−100 mm. Significance. This paper shows that despite the low axial resolution, point-like sources in the nearfield can be localized as well as with more sophisticated imaging devices such as stereo cameras. The influence of the source size and the photon count on the imaging and localization accuracy remains an important issue for further research.

Abstract:

Purpose: Handheld gamma cameras with coded aperture collimators are under inves- tigation for intraoperative imaging in nuclear medicine. Coded apertures are a promis- ing collimation technique for applications such as lymph node localization due to their high sensitivity and the possibility of 3D imaging. We evaluated the axial resolutionand computational performance of two reconstruction methods.Methods: An experimental gamma camera was set up consisting of the pixelated semiconductor detector Timepix3 and MURA mask of rank 31 with round holesof 0.08 mm in diameter in a 0.11 mm thick Tungsten sheet. A set of measurements was taken where a point-like gamma source was placed centrally at 21 different positions within the range of 12–100 mm. For each source position, the detector image was reconstructed in 0.5 mm steps around the true source position, resulting in an image stack. The axial resolution was assessed by the full width at half maximum (FWHM) of the contrast-to-noise ratio (CNR) profile along the z-axis of the stack. Two reconstruction methods were compared: MURA Decoding and a 3D maximum likeli- hood expectation maximization algorithm (3D-MLEM).Results: While taking 4400 times longer in computation, 3D-MLEM yielded a smaller axial FWHM and a higher CNR. The axial resolution degraded from 5.3 mm and 1.8 mm at 12 mm to 42.2 mm and 13.5 mm at 100 mm for MURA Decoding and 3D-MLEM respectively.Conclusion: Our results show that the coded aperture enables the depth estimation of single point-like sources in the near field. Here, 3D-MLEM offered a better axial reso- lution but was computationally much slower than MURA Decoding, whose reconstruc- tion time is compatible with real-time imaging.

Abstract:

The overall aim of this thesis is to improve the outcome of Radioguided Surg- eries (RGSs) by developing and evaluating reconstruction methods to exploit the advantages of Coded Aperture Imaging (CAI) for Intraoperative Gamma Cameras (IGCs).Motivation: Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer. The biopsy and examination of lymph nodes that receive lymphatic drainage from the primary tumor are guided by IGCs. CAI has been proposed as an alternative to parallel-hole or pinhole collimators to produce an image. It offers a better trade-off between sensitivity and spatial resolution, but requires image reconstruction.Methods: First, planar reconstruction was investigated where the source’s depth is assumed to be known. A Convolutional Encoder-Decoder (CED) was developed and trained on synthetic source distributions and a low-fidelity sim- ulation. It was quantitatively compared to reconstruction methods from the literature, such as MURA Decoding and a Maximum Likelihood Expectation Maximization (MLEM) algorithm. The computing time and the Contrast-to- Noise Ratio (CNR) served as key metrics throughout this thesis. Moreover, the ability of super-resolution was investigated by reconstructing bilinear interpo- lations of simulated low-resolution detector images. In the second part of this thesis, the assumption of a known source-to-mask distance was loosened and the axial resolution quantified using the Full Width at Half Maximum (FWHM) of the axial CNR profile of a point-like source. Lastly, an Iterative Source Localization (ISL) algorithm incorporating an Exponentially Modified Gaussian (EMG) fitting was developed to localize sources in all three dimensions.Results: While MURA Decoding quickly provides robust reconstructions with good quality, MLEM takes around 170 times longer with a 1.2 times higher CNR. The CED performed the best with an on average 2.7 times higher CNR and a runtime comparable to MURA Decoding, despite the low-fidelity simulation of the training data. The simulation study indicated that super- resolution is feasible. Regarding 3D reconstruction, it was found that, for MURA Decoding, the axial resolution degraded from 5.3 mm FWHM at 12 mm mask-to-source distance to 42.2mm at 100mm. The ISL combined with an EMG fit achieved a mean localization error of 0.8mm on the simulated and 2.6mm on the experimental data in the imaging range of 20−100mm.Conclusion: The MLEM algorithm yields a higher CNR and better axial resolution, but is not suitable for RGS in its current form, due to its compu- tational complexity. MURA Decoding provides robust reconstructions. Its fast computation enables 3D reconstruction which allows the localization of point-like sources with an accuracy comparable to that of stereoscopic cameras.