Table 2.
A summary of semisolid MT/CEST MRF methods
| Method | Advantages | Limitations |
|---|---|---|
| Dictionary-correlation matched CEST MRF59 | Rapid acquisition | Prolonged reconstruction for big dictionaries, discrete output parameters |
| CEST-MRF for exchange rate quantification60 | Removal of MT effects prior to dictionary matching | Potential bias from NOE effects in-vivo due to the use of the upfield spectrum signal, discrete output parameters |
| Sub-grouping proton exchange models61 | Least-square fitting is used instead of dot-product matching for continous quantification of amide and MT parameters, circumvents the need for lengthy dictionary generation | Long reconstruction time |
| Quantitative CEST using artificial NN and partial Z-spectrum acquisition (ANNCEST)70 | Rapid CEST acquisition and reconstruction, B0 and B1 mapping | Unsuitable for brain applications where the MT parameters vary |
| Deep semisolid MT MRF with synthetic signal validation73 | Quantification of MT parameters and water T1, allows the removal of MT effects from CEST signals | Amide and NOE reconstruction is semi-quantitative and requires separate water T2 mapping |
| Sequential and deep semisolid MT/CEST MRF15 | Quantitation of both MT and amide parameters, rapid reconstruction | Two acquisition schedules required as well as T1, T2, and B0 mapping |
| Unsupervised semisolid MT MRF75 | No need for dictionary generation, noise robustness | limited generalization ability for unseen pathologies |
| Acquisition schedule optimization using discrimination ability / SNR efficiency based metrics77,78 | Faster than Monte Carlo simulations, enables predicting the encoding capability of different schedules | Time consuming for complicated in-vivo scenarios |
| Learning-based optimization of acquisition schedule (LOAS) for semisolid MT MRF85 | Directly computs quantitative tissue parameter errors, outperforms Cramer–Rao lower bound based optimization, quantitate MT parameters and water T1 | Based on the analytical solution of the BM equations, which might be less accurate than the numerical solution |
| Semisolid MT/CEST MRF acquisition protocols discovery and deep parameter quantification (AutoCEST)82 | An end-to-end fully automatic procedure, yiedling short acuisition schedules and trained NNs for quantitative semisolid MT and CEST reconstruction | Based on the analytical solution of the BM equations, which might be less accurate than the numerical solution |