- Brief description of current acidoCEST method and its limitations
- Previous work in ML and why it could solve some of these issues. Previous work by our lab
- Objective of this paper.
- Short description of ML approach (learning and testing)
- Describe that our current methodology does not account for variable experimental conditions, but it could do if we use an ML approach.
- Description of using the entire Z spectra.
-ML and analysis
- Short description of ML approach (learning and testing)
- Lorentzian fitting
- Describe that our current methodology does not account for variable experimental conditions, but it could do if we use an ML approach.
- Description of using the entire Z spectra.
-Experimental
- Samples
- MRI methods
- Regression
- standard method when applied to out-of-training data
- standard method when built using a ML approach
- Linear regression using entire spectra
- Classification
- standard method when applied to out-of-training data
- standard method when built using a ML approach
- Linear regression using entire spectra
- how does the method building method affect the accessible pH range
- limitation of current study
- conclusions
- Figure 01: Principles of ML. Simlar to Joey's thesis
- Figure 02: Effect of pH on the "shape" of a Z spectra for Iopamidol - Include other molecules in supplemental info.
- Figure 03: Iopamidol pH regression
- Figure 04: polymer pH regression
- Figure 05: monomer-2 pH regression
- Figure 06: Iopamidol pH classification (two cut offs)
- Figure 07: polymer pH classification (two cut offs)
- Figure 08: monomer-2 pH classification (two cut offs)