Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.
Yang LeiHui-Kuo ShuSibo TianJiwoong Jason JeongTian LiuHyunsuk ShimHui MaoTonghe WangAshesh B JaniWalter J CurranXiaofeng YangPublished in: Journal of medical imaging (Bellingham, Wash.) (2018)
Magnetic resonance imaging (MRI) provides a number of advantages over computed tomography (CT) for radiation therapy treatment planning; however, MRI lacks the key electron density information necessary for accurate dose calculation. We propose a dictionary-learning-based method to derive electron density information from MRIs. Specifically, we first partition a given MR image into a set of patches, for which we used a joint dictionary learning method to directly predict a CT patch as a structured output. Then a feature selection method is used to ensure prediction robustness. Finally, we combine all the predicted CT patches to obtain the final prediction for the given MR image. This prediction technique was validated for a clinical application using 14 patients with brain MR and CT images. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), normalized cross-correlation (NCC) indices and similarity index (SI) for air, soft-tissue and bone region were used to quantify the prediction accuracy. The mean ± std of PSNR, MAE, and NCC were: 22.4±1.9 dB , 82.6±26.1 HU, and 0.91±0.03 for the 14 patients. The SIs for air, soft-tissue, and bone regions are 0.98±0.01 , 0.88±0.03 , and 0.69±0.08 . These indices demonstrate the CT prediction accuracy of the proposed learning-based method. This CT image prediction technique could be used as a tool for MRI-based radiation treatment planning, or for PET attenuation correction in a PET/MRI scanner.
Keyphrases
- contrast enhanced
- computed tomography
- magnetic resonance imaging
- dual energy
- positron emission tomography
- soft tissue
- image quality
- magnetic resonance
- diffusion weighted imaging
- deep learning
- radiation therapy
- newly diagnosed
- air pollution
- pet ct
- machine learning
- ejection fraction
- health information
- chronic kidney disease
- pet imaging
- brain injury
- ionic liquid
- radiation induced
- bone loss
- squamous cell carcinoma