Improving the detectability of overactive glands in dual-phase parathyroid SPECT/CT: a Monte Carlo simulation study.
Aysegul OralAlbert GuvenisPublished in: Biomedical physics & engineering express (2021)
Objective. SPECT-CT is a standard procedure conducted before minimally invasive surgery for the treatment of primary hyperthyroidism. In order to improve image quality, it is important to know how defect detectability is influenced by acquisition and processing parameters. The objective of this study is to continue prior physical phantom optimization studies by performing Monte Carlo simulations for the dual phase parathyroid SPECT-CT protocol using a digital anthropomorphic phantom.Methods. The dual phase parathyroid SPECT-CT imaging procedure with 99mTc-Sestamibi was simulated using the previously extensively validated SIMIND software for the first time. An anthropomorphic ZUBAL based phantom was built to represent an adenoma. Its diameter was set to 0.76 cm which corresponded to more than three times the pixel size and the target-to-background ratio was set to 16:1 based on previous studies. Four different collimators were tested. Contrast-to-noise (CNR) values were determined for different scatter correction options and processing parameter values. The OSEM algorithm was used for image reconstruction.Results. CNR values were improved from about zero (LEGP collimator, 16 iterations, attenuation correction: on, scatter correction: off) up to 3.7 (LEUHR collimator, 16 iterations, attenuation correction: on, scatter correction: off). The subjective visual assessment of detectability on simulated images agreed with the quantitative CNR values.Conclusion. Higher resolution collimators gave better CNR as confirmed by similar studies. The effect of scatter correction was found beneficial only if both the resolution and sensitivity of the collimator were relatively high. This is a significant finding since there is a shortage of definitive guideline on the use of scatter correction for parathyroid SPECT imaging.
Keyphrases
- monte carlo
- image quality
- dual energy
- computed tomography
- contrast enhanced
- pet ct
- high resolution
- deep learning
- positron emission tomography
- magnetic resonance
- physical activity
- machine learning
- magnetic resonance imaging
- case control
- single molecule
- squamous cell carcinoma
- mental health
- combination therapy
- radiation therapy
- convolutional neural network
- air pollution
- optical coherence tomography
- smoking cessation
- rectal cancer
- fluorescence imaging
- replacement therapy