Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
Chemical exchange saturation transfer (CEST) imaging is an emerging MRI technique relying on the use of endogenous or exogenous molecules containing exchangeable proton pools. The heterogeneity of the water resonance frequency offset plays a key role in the occurrence of artifacts in CEST-MR images. To limit this drawback, a new smoothing-splines-based method for fitting and correcting Z-spectra in order to compensate for low signal-to-noise ratio (SNR) without any a priori model was developed. Global and local voxel-by-voxel Z-spectra were interpolated by smoothing splines with smoothing terms aimed at suppressing noise. Thus, a map of the water frequency offset ('zero' map) was used to correctly calculate the saturation transfer (ST) for each voxel. Simulations were performed to compare the method to polynomials and zero-only-corrected splines on the basis of SNR improvement. In vitro acquisitions of capillaries containing solutions of LIPOCEST agents at different concentrations were performed to experimentally validate the results from simulations. Additionally, ex vivo investigations of bovine muscle mass injected with LIPOCEST agents were performed as a function of increasing pulse power. The results from simulations and experiments highlighted the importance of a proper 'zero' correction (15% decrease of fictitious CEST signal in phantoms and ex vivo preparations) and proved the method to be more accurate compared with the previously published ones, often providing a SNR higher than 5 in different simulated and experimentally noisy conditions. In conclusion, the proposed method offers an accurate tool in CEST investigation.
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