| dc.contributor.advisor | fadlullah, zubair md. | |
| dc.contributor.advisor | reznik, alla | |
| dc.contributor.author | komorov, borys | |
| dc.date.accessioned | 2022-09-28t19:37:38z | |
| dc.date.available | 2022-09-28t19:37:38z | |
| dc.date.created | 2022 | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://knowledgecommons.lakeheadu.ca/handle/2453/5025 | |
| dc.description.abstract | functional medical imaging is unique in its ability to visualize molecular interactions and
pathways in the body. organ-targeted positron emission tomography (pet) is a
functional imaging technique that has emerged to meet the demands of precision
medicine and has shown advantages in terms of sensitivity and image quality compared
to whole-body (wb) pet. a common application for organ-targeted pet is oncology,
particular breast cancer imaging. in this work we present the application of graphics
processing unit (gpu) to significantly accelerate reconstruction of clinical breast images
acquired with an organ-targeted pet camera and reconstructed using the maximum
likelihood estimation maximization (mlem) algorithm. the pet camera is configured
with two planar detector heads with a sensing area of 232mm×174mm. acquired raw
image data are converted into list mode format and reconstructed by a gpu-based 3d
mlem algorithm that was developed specifically for the limited-angle capabilities of the
planar pet geometry. the algorithm applies corrections including attenuation and scatter
to provide clinical grade image quality. we demonstrate that a transition from originally
developed central processing unit (cpu) to newly developed gpu-based algorithm
improves single iteration speed by more than 400 times, while preserving image quality.
the latter has been assessed on clinical data and through phantom tests performed
according to the national electrical manufacturers association (nema) nu-4 standards.
the gain in reconstruction speed is expected to result in improved patient throughput
capabilities of the clinical organ-targeted pet. indeed, gpu-based image reconstruction
reduces time needed for a typical breast image reconstruction to less than 5 minutes thus
making it shorter than the image acquisition time. this is of particular importance in
improving patient throughput and clinical adaptation of the pet system. | en_us |
| dc.language.iso | en_us | en_us |
| dc.subject | positron emission tomography (pet) | en_us |
| dc.subject | graphics processing unit (gpu) | en_us |
| dc.subject | breast imaging | en_us |
| dc.title | 3d gpu-based image reconstruction algorithm for the application in a clinical organ-targeted pet camera | en_us |
| dc.type | thesis | en_us |
| etd.degree.name | master of science | en_us |
| etd.degree.level | master | en_us |
| etd.degree.discipline | computer science | en_us |
| etd.degree.grantor | 阿根廷vs墨西哥竞猜
| en_us |
