We Claim:
1. A method for medical imaging, the method being characterized
in that it comprises the implementation, by a data processor (11b) of a second server
5 (1b), of steps of:
(a) Obtaining (i) at least one candidate pre-contrast image and a candidate
contrast image respectively depicting a body part prior to and after an
injection of a first dose of contrast agent, wherein the first dose of contrast
agent is higher than a low dose of contrast agent and the low dose is lower
10 than a standard dose of contrast agent; and (ii) a convolutional neural
network, CNN, being trained to reconstruct, from at least one pre-contrast
input image and a low dose contrast input image respectively depicting
the body part prior to and after an injection of the low dose of contrast
agent, a standard dose contrast image depicting the body part after an
15 injection of the standard dose of contrast agent;
(b) Generating a synthetic contrast image by applying the CNN to the at least
one candidate pre-contrast image and the candidate contrast image.

2. The method according to claim 1, wherein the low dose of
contrast agent is between 10% and 50% of the standard dose of contrast agent.

3. The method according to one of claims 1 and 2, wherein the
first dose of contrast agent is at least 50% of the standard dose of contrast agent.

4. The method according to claims 2 and 3 in combination,
wherein the low dose of contrast agent is between 1/5 and 1/3 of the standard dose of
contrast agent; and the first dose of contrast agent is at least 80% of the standard dose
of contrast agent.

5. The method according to one of claims 1 to
4, wherein the at
least one candidate pre-contrast image and the candidate contrast image are acquired
by a medical imaging device (10) connected to the second server (1b), in particular
an MRI scanner.

6. The method according to one of claims 1 to 5, wherein the at
least one candidate pre-contrast image includes a T1-weighted pre-contrast image and
the candidate contrast image is a T1-weighted image.

7. The method according to claim 6, wherein:
- the at least one candidate pre-contrast image includes the T1-weighted
pre-contrast image, a T2-flair-weighted pre-contrast image and an ADC pre-contrast
map;
- the at least one pre-contrast input image includes a T1-weighted precontrast input image, a T2-flair-weighted pre-contrast input image and a training
ADC pre-contrast input map;
- the low dose contrast input image is a T1-weighted contrast image;
- the standard dose contrast image is a T1-weighted contrast image;
- the synthetic contrast image is a synthetic T1-weighted contrast image.

8. The method according to one of claims 1 to 7, wherein said
CNN comprises an encoder branch followed by a decoder branch, with skip
connections between the encoder branch and decoder branch.

9. The method according to one of claims 1 to 8, comprising a
previous step of training, by a data processor (11a) of a first server (1a), said CNN
from a base of sequences of at least one training pre-contrast image, a first training
contrast image and a second training image respectively depicting a body part prior
to, after an injection of the low dose of contrast agent, and after an injection of the
standard dose of contrast agent.

10. Computer program product comprising code instructions to
execute a method for medical imaging according to one of claims 1 to 9, when said
computer program is executed on a computer.