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
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]
I have one planned soon. Of course the prescribing doctor didn't mention any of this, but I guess the research is still too fresh. Thanks for raising awareness.
not only vitamin c but fruits containing oxalic acid if I read that right. But I'm far more interested in when such contrast agents are warranted, because I'm not aware that in Europe that contrast agent would be used that much for MRI
For your anecdata I'm in Sweden and definitely had a contrast agent (presumably gadolinium based) for a recent MRI of my gallbladder/pancreas/liver area.
The term is "physics based model" it has somewhat specific meaning in context of mathemtical/physics modelling. It has nothing to do with all physics required to make MRI work. A model doesnt have to be based on physics to be usefull. You can often get some recognizable image by dumb stronger signal=> brighter pixel logic without fully modelling how why it changes. As long as change in material correlates with change in signal (doesn't even have to happen uniformly) you can get some picture and leave the interpretation to human. A simpler example would be temperature control. You can have simple hysteresis based approch of temperature under threshold turn on heater, above threshold turn off. Or you can have physics based model of what the heating power is, what's the heat capacity of chamber, what's the heat capacity of object, how the temperature diffuses within object, what's the thermal resistance of interfaces between heater, target and temperature sensor. Many everyday systems systems are controlled by generic PID controllers without physically modelling how exactly the process reacts to input, PID can be be considered a mathematical control model with sufficient parameterization to approximate various physical systems. You could also make an AI based model and create a signal processing function that way. For many drones PID coefficients are tuned by hand, it was quite a surprise when one of controller manufacurers had made a physics based model to calculate suitable defaults based on drone mass, momment of inertia and maximum thrust.
Technically the tittle isn't lying. Researchers created new physics based model which is more detailed and makes less simplifications compared to old physics based model. The qualification also clarifies that potentially sharper image won't be achieved by new device model or a picture of 3d model printed on marketing materials.
Here's the paper.[1] No paywall this way; U.S. Government funded research.
The paper claims an associated Github repository but there is no obvious link.
There's no imagery in the paper, just the development of the math. So this
may or may not help much.
Weird that I couldn't find the paper on arXiv: in my field I just google the title prefixed with "arXiv" and it pops up.
Some earlier articles by the same authors are there though (e.g. https://arxiv.org/abs/2310.06106), does the journal of chemical physics prohibit arXiv posting or is the norm just different in that field?
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
https://www.ashospital.net/blog/why-do-i-need-contrast-for-m...
Thanks for making me aware!
Technically the tittle isn't lying. Researchers created new physics based model which is more detailed and makes less simplifications compared to old physics based model. The qualification also clarifies that potentially sharper image won't be achieved by new device model or a picture of 3d model printed on marketing materials.
[1] https://www.osti.gov/pages/servlets/purl/3001752
Some earlier articles by the same authors are there though (e.g. https://arxiv.org/abs/2310.06106), does the journal of chemical physics prohibit arXiv posting or is the norm just different in that field?