Medical imaging in radiation oncology and beyond

The College of Science Physics Department cordially invited the academic community to a lecture entitled Medical Imaging in Radiation Oncology and Beyond by Mr. Delmar R. Arzabal. Abstract: Modern medical diagnosis and treatment heavily rely on the imaging modality. In the field of medical physics, different imaging modalities, particularly those that utilize electromagnetic waves, are thoroughly studied. X-rays are commonly used, and its applications vary extensively based on the complexity of the target volume to give 2D and 3D images. 3- dimensional images are easily rendered using Computed Tomography (CT) scan. The data of which can be integrated with Positron Emission Tomography (PET) and Single-Photon Emission Computed Tomography (SPECT) for better tumor localization and cancer prognosis. Advancements in radiotherapy allow the medical physicists to target and treat the tumor volume more accurately. However, contouring the actual body part still highly depends on the image quality. Various image quality enhancements can be done through the modification virtual and physical parameters of data acquisition. Image reconstruction can be analytic or iterative. Both methods utilize algorithms, commonly the Fourier Transform in 1 and 2 dimensions. Mathematical computation and strategic estimation have considerable effects on the reconstructed image. The CT information can be further differentiated to isolate a chosen part and to export data for 3D printing. This permits customized treatment accessories which can improve radiation dose delivery to patients. The utilization of the image data to 3D print a treatment accessory or replicate an anatomical part is not only useful for radiation oncology, but extends to biomedical engineering and other allied sciences

Phantom and clinical evaluation of combined image reconstruction parameters of Philips Gemini TF 64 PET/CT imaging system

PURPOSE: Positron emission tomography-computed tomograhy imaging provides information on glucose metabolism uptake of the FDG. This may also indicate the functional status of cancer lesions. Erroneous image details may lead to false results. Reconstruction is used to enhance the data by minimizing the noise, adjusting the contrast, and modifying the features of the image mathematically. METHODS: The study used the NEMA phantom for quantitative analysis and retrospective data for clinical evaluation. The data were reconstructed using defined Time-of-Flight (ToF) protocols ofvarying relaxation parameter, iteration subsetsand kernelwidth. For the phantom study, the data were analyzed to determine the percent contrast (PC), background variability (BV), and contrast­ to-noise ratio (CNR) for each reconstruction protocol. Two experienced nuclear medicine physicians, who were blinded by the reconstruction methods used, evaluated the retrospective data. The reconstructed images are rated on a scale of 1-4 and scored by rank according to 6 parameters. RESULTS: In the quantitative analysis, the smallest radioactive sphere obtained the highest PC of 18.3% on Protocol 5, while the largest radioactive sphere had the highest value of 55.1% on Protocol 6. The BV was least at 4.37% for the smallest radioactive sphere, and at 5.55% for the largest radioactive sphere, both on Protocol 1. The CNR was highest on Protocol 1 across all sphere sizes. The clinical evaluation of the retrospective data subjectively preferred Protocol 1 and Protocol 5. CONCLUSION: The findings showed that lower values of image reconstruction parameters produced improved CNR and were more favored by nuclear medicine physicians