While this work is great, this will not directly lead to "sharper MRI scans". This is about better modelling of NMR signals, which may eventually lead to better MRI, but it is still pretty far away from imaging. If you want how we use simpler signal models in physics-based reconstruction to improve MR images, you can look at our paper: https://doi.org/10.1098/rsta.2020.0196

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azalemeth 6 hours ago | parent [-]

Indeed. Martin's a great name in the field -- the thing that has actually made most clinical proton MRI substantially better over the last twenty years has been parallel imaging (acquiring the magnetic resonance signal from different spatially separated devices known as RF coils) and associated reconstruction techniques such as compressed sensing.

Given the fact that macrocyclic gadolinium complexes accumulate in the brain and the linear ones dechelate I think very few companies are pursuing new agents. I've done some work with different ions (like Dy, which has Curie paramagnetism) but a lot of focus in the field is trying to find alternatives to gad and reduce its use. There are plenty of great ways of getting more info out of a machine that spans quantum mechanics to medicine, from the established but now actually useful and routine (like advanced diffusion models) to the sort of utterly mad techniques I work on... [0]

[0] https://www.science.org/doi/10.1126/sciadv.adz4334

	▲	olsondv an hour ago \| parent [-]
		Advanced diffusion certainly benefited from the acquisition speed ups. That is its biggest challenge in my opinion preventing it from wider clinical adoption. It takes too long to get enough images for the models. Hyperpolarized MR will run into issue of lack of expertise in clinical imaging centers. There is already a shortage of good techs and MR companies are working to further automate the workflows. Unless there is a major benefit of the advanced techniques, people will stick to the bread and butter FSE and DWI.