Penalized maximum-likelihood image reconstruction for lesion detection
| Autor: | Ronald H. Huesman, Jinyi Qi |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Point spread function
Computer science Image quality Quantitative Biology::Tissues and Organs Physics::Medical Physics Monte Carlo method ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Information Storage and Retrieval Iterative reconstruction Sensitivity and Specificity Regularization (mathematics) Lesion Neoplasms Image Interpretation Computer-Assisted medicine Humans Radiology Nuclear Medicine and imaging Computer vision Penalty method Likelihood Functions Radiological and Ultrasound Technology Lesion detection Phantoms Imaging business.industry Isotropy Reproducibility of Results Image Enhancement Tomography Artificial intelligence medicine.symptom business Algorithm Algorithms Tomography Emission-Computed |
| Zdroj: | Physics in Medicine and Biology. 51:4017-4029 |
| ISSN: | 1361-6560 0031-9155 |
| DOI: | 10.1088/0031-9155/51/16/009 |
| Popis: | Detecting cancerous lesions is one major application in emission tomography. In this paper, we study penalized maximum-likelihood image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modelling the photon detection process and measurement noise in imaging systems. To explore the full potential of penalized maximum-likelihood image reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters to improve lesion detectability. We conducted computer-based Monte Carlo simulations to compare the proposed penalty function, conventional penalty function, and a penalty function for isotropic point spread function. The lesion detectability is measured by a channelized Hotelling observer. The results show that the proposed penalty function outperforms the other penalty functions for lesion detection. The relative improvement is dependent on the size of the lesion. However, we found that the penalty function optimized for a 5 mm lesion still outperforms the other two penalty functions for detecting a 14 mm lesion. Therefore, it is feasible to use the penalty function designed for small lesions in image reconstruction, because detection of large lesions is relatively easy. |
| Databáze: | OpenAIRE |
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