If people's first impression of what you want your UI to mean is different often enough to cause problems (for example see the amount of debate here), that is the bug, not the people misinterpreting it. The use of these terms in Jira is one of my pet peeves, since different teams often use them very differently and can be real fun when sharing Jira boards with multiple opinionated companies.
The colloquial usage of 'severity' and 'priority' just has too much overlap. Something like pairing 'likelihood' and 'severity' assessments, as is standard in safety, would still be general and make it immediately clear why something could be severe yet low priority, especially since people commonly mean both likely and severe when ranking a problem as severe. Keeping with the author's definition of severe, renaming severity as 'System Impact' at least immediately narrows down what is severe, but still carries the possible implication of 'frequent'.
I think fighting for a given interpretation here without using different terms is akin to insisting that people simply understand that a 'significant difference' reported in science means 'statistically different' as opposed to an 'important difference'.
I find how the Fourier Transform works so simply in multiple dimensions as long as you have cartesian sampling is where the transform starts to shine. Just apply along one dimension then the others and because it's a linear unitary operator they are incredibly robust and it makes no difference which order you apply them. Then you can combine them with other measurement types in higher dimensional measurements (e.g. a relaxation period) and still just mindlessly apply the Fourier Transform along the frequency dimensions (first since it's so robust) and get the desired result.
There isn't any difference in the Fourier analysis between spectroscopy and imaging. In imaging you're just encoding position as a frequency and if you enforce Cartesian sampling the analysis remains the same, including combining extra dimensions. Spectroscopy and flow imaging experiments can get pretty crazy with 5-6 encoding dimensions limited only by your patience and instrument drift over longer periods (weeks).
The differences between spectroscopy and MRI are primarily application driven and then because it is tricker to apply precise magnetic field gradient pulses vs precisely timing RF pulses. Combined with the fact people are a lot bigger, more impatient and more delicate than test tubes while imaging can also be far less quantitative drives the design of very different NMR/MRI pulse sequences. In imaging, the speed gains and human placed limits on gradient slew rates (Audio frequency dB/dt induces currents in neurons) often justify the trouble of non-Cartesian and/or incomplete sampling.
If you want a next step for MRI signal processing, look into multiple-coil reconstruction techniques and how they not only combine the spacial sensitivity profiles of the coils with gradient imaging, without knowing the actual sensitivity profiles a-priori. Pretty much every medical MRI machine uses multiple receive coils to reduce imaging times.
Note: My user name is proton gamma though I left the field mid-career to do sw development. (A far better career though not as exciting if you love physics like I do.)
(edits: grammar, probably still missed a few typos)
The colloquial usage of 'severity' and 'priority' just has too much overlap. Something like pairing 'likelihood' and 'severity' assessments, as is standard in safety, would still be general and make it immediately clear why something could be severe yet low priority, especially since people commonly mean both likely and severe when ranking a problem as severe. Keeping with the author's definition of severe, renaming severity as 'System Impact' at least immediately narrows down what is severe, but still carries the possible implication of 'frequent'.
I think fighting for a given interpretation here without using different terms is akin to insisting that people simply understand that a 'significant difference' reported in science means 'statistically different' as opposed to an 'important difference'.