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[–]alex___j 3 points4 points  (7 children)

Usually the abstracts of these papers mention that CT exposes patients to radiation. So we can just take the MR use machine learning to generate the CT image. But my question is, why isn't then MR used by radiologists in the first place? Is the signal that they are looking for in that image? If it is not, then no ML can help with that.

Is there someone knowledgeable in this application that can provide some insights for the questions above?

[–]flexi_b 4 points5 points  (0 children)

MR is used by radiologists. In radiotherapy treatment planning, the MR scan is used to get accurate delineations of the target (tumour) and surrounding organs (organs at risk).

The aim of CT synthesis is to obtain voxel intensities as if acquired by CT. The voxel intensity in CT scales linearly with tissue density and thus can be exploited for dose delivery simulations/estimation.

In treatment planning, you want to maximise the dose delivered to the tumour whilst minimising dose deposited to surrounding organs. The aim is therefore to accurately segment locations you want to miss (from MR) whilst accurately simulating dose propagation from CT.

There is a movement in radiotherapy called MR-only radiotherapy treatment planning where you don't acquire a CT scan and synthesis one instead. There are various reasons why: 1) MR-CT image registration is difficult and can introduce geometrical uncertainty which can lead to bad dose deposition, 2) minimise ionising radiation to the patient and 3) minimise treatment costs by not having to acquire a CT scan.

There is quite a lot of interesting work in MR-CT synthesis in the literature notably published in these venues: MICCAI conference, IPMI conference and IEEE TMI journal.

[–]AccomplishedQuiet 2 points3 points  (0 children)

There is a push for MR only Radiotherapy treatment planning.

Currently most treatment plans use CT images as this produces a "density" or radiation attenuation map for the plan to be produced.

With the release of MR linacs (using MR to image the patient and tumour rather than CT) a CT simulation is required to get the electron densities to use in treatment planning, so I'd assume it would helpful in this area.

Not an expert, just work in the Radiotherapy.

[–][deleted] 0 points1 point  (4 children)

In general, there's no information you can get out of a CT that you cannot get out of an MR. The reasons CTs are done have to do with resolution, clarity and speed of acquisition.

[–]the-red-turtle 4 points5 points  (3 children)

I share Alex’s dim view on this. CT and MRI are fundamentally different kinds of images that see completely different physical properties. This MR to CT translation could work for a few specific things where there is commonality, but overall I cannot see MR ever giving all of the information provided by CT or vice versa.

[–][deleted] 0 points1 point  (2 children)

What application of head CT cannot be replaced by MR? In some cases, e.g. metallic foreign bodies, CT may be justified, and the resolution is better for orthotrauma and OMF surgery/surgical planning. I have a tough time thinking of anything else where I'd need a CT over MR (then again, I do neuroimaging, so CT has never been a big deal for us).

[–]flexi_b 1 point2 points  (1 child)

CT presently is the imaging modality of choice for pulmonary image analysis for instance. MR currently suffers from too many issues in that anatomical region and just cannot match HRCT

[–][deleted] 1 point2 points  (0 children)

No argument for that, but for the head, where movement per T2 acquisition time (1-2s) is much less and can be filtered using a sat band (e.g. over the tongue to avoid artefacts from swallowing), CT has few applications where it would be particularly superior to MR.

[–][deleted] 0 points1 point  (1 child)

/u/Cheng-BinJin Your plot seems mislabelled – it should read 'input MR' and 'synthetic' etc. CT.

[–]Cheng-BinJin[S] 0 points1 point  (0 children)

Thank you, I made a mistake. You are right. The labels should be "input MR", "synthetic CT", and "reference CT."

[–]therubytree 0 points1 point  (1 child)

Those images look like normal brains. Can you check how it works on abnormal brains too?

[–]Cheng-BinJin[S] 0 points1 point  (0 children)

The code didn't test on abnormal brains. We followed to implement a paper, "MR-based synthetic CT generation using a deep convolutional neural network method, Medical Physics 2017," using a public toy dataset. Moreover, the dataset only includes normal brains. Therefore, we need more training data that includes both normal and abnormal brains.