Speaker
Description
The super-resolution (SR) techniques are often used in the up-scaling process to add-in details that are not present in the original low-resolution image. In radiation therapy the SR can be applied to enhance the quality of medical images used in treatment planning. The Dose3D detector measuring spatial dose distribution [1][2], the dedicated set of ML algorithms for SR has been proposed to perform final dose distribution up-scaling. Despite the significant advancements in image processing, the task of three-dimensional (3D) image upscaling remains a formidable challenge and has not gained widespread popularity due to the inherent complexities associated with preserving spatial consistency and accurately interpolating volumetric pixel intensities.
In our project, as the SR technique, the SRCNN [3] architecture has been adjusted. The training and validation data being produced with Geant4 MC simulation with in-house developed application and with two different scoring resolutions. Extra features related to the beam shape have been defined. The input data resolution is the one coming from the measurement (1cc) and the target data resolution is defined at the level of the CT image. Our research's latest breakthroughs and advancements will feature at the conference.
References:
[1] https://dose3d.fis.agh.edu.pl,
[2] M. Kopeć, et al. A reconfigurable detector for measuring the spatial distribution of radiation dose for applications in the preparation of individual patient treatment plans. Nuclear Inst. and Methods in Physics Research, A 1048 (2023) 167937
[3] Dong, C., Loy, C.C., He, K., Tang, X. (2014). Learning a Deep Convolutional Network for Image Super-Resolution. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8692. Springer, Cham. https://doi.org/10.1007/978-3-319-10593-2_13
