Method and system for determining the BPE in a contrast medium-enhanced X-ray examination of a breast
One or more example embodiments of the present invention relates to a method for determining a Background Parenchymal Enhancement (BPE) in a contrast medium-enhanced X-ray examination of a breast comprising providing images of the X-ray examination, the images having been taken after administration of the contrast medium, the images comprising at least one low energy (LE) image taken at a predetermined low X-ray energy and a high energy (HE) image taken at a predetermined high X-ray energy, creating an iodine image from the LE image and the HE image and calculating a volumetric BPE as a sum of all pixel values in the iodine image for which the following two conditions apply i) iodine enrichment is present in the iodine image, and ii) fibroglandular tissue (FGT) is present. The method further includes obtaining BPE result data based on the iodine image and volumetric BPE and outputting the BPE result data.
1 . A method for determining a Background Parenchymal Enhancement (BPE) in a contrast medium-enhanced X-ray examination of a breast comprising:
providing images of the contrast medium-enhanced X-ray examination, the images having been taken after administration of a contrast medium, the images comprising at least one low energy (LE) image taken at a predetermined low X-ray energy and a high energy (HE) image taken at a predetermined high X-ray energy;
creating an iodine image from the at least one LE image and the HE image;
calculating a volumetric BPE as a sum of all pixel values in the iodine image for which iodine enrichment is present in the iodine image and fibroglandular tissue (FGT) is present;
obtaining BPE result data based on the iodine image and the volumetric BPE; and
outputting the BPE result data.
2 . The method of claim 1 , wherein the calculating includes:
creating an FGT mask in which a classification is performed for each picture element in the at least one LE image or a weighted linear combination of the at least one LE image and a number of further spectral images as to whether a picture element represents FGT, a corresponding image position in the FGT mask is marked with an FGT marker for each picture element that represents FGT;
creating an FGT enrichment (FGT-E) mask, in which a classification is performed at image positions in the iodine image whose correspondences in the FGT mask are marked with an FGT marker as to whether iodine enrichment is present, wherein in a positive case a corresponding image position in the FGT-E mask is provided with an FGTE marker for the image positions in the iodine image that are classified with iodine enrichment being present; and
calculating the volumetric BPE as the sum of all pixel values in the iodine image for which an FGTE marker is present at the corresponding image position of the FGT-E mask.
3 . The method of claim 2 , further comprising:
counting a number of FGT markers of the FGT mask as X and a number of FGTE markers of the FGT-E mask as XE; and
calculating a relative BPE rBPE as rBPE=XE/X.
4 . The method of claim 1 , further comprising:
classifying BPE result data in the iodine image by applying an assignment function to image elements of the iodine image.
5 . The method of claim 1 , wherein the outputting includes at least one of:
graphically displaying the iodine image or of an image derived from the iodine image, or
displaying at least one of the volumetric BPE or a value rBPE is performed.
6 . The method of claim 1 , further comprising:
detecting a deviation between a local mammographic breast density and the BPE result data based on the at least one LE image and the iodine image, wherein if a pattern of the BPE and the local mammographic breast density do not match, an indicator of a relevant clinical condition is generated.
7 . The method of claim 1 , further comprising:
performing an automated analysis of a spatial distribution, a morphology and a texture of the BPE in the iodine image.
8 . The method of claim 1 , further comprising:
performing an automated longitudinal section assessment of the BPE based on the iodine image using LE images and HE images from multiple X-ray examinations of a same individual.
9 . The method of claim 1 , wherein based on LE images and HE images of a bilateral mammographic examination, an automated assessment of symmetry is performed based on the iodine image of an imaged right breast and an imaged left breast.
10 . The method of claim 2 , wherein at least one of
the contrast medium-enhanced X-ray examination uses a spectral multi-energy imaging method and at least one of the iodine image, the FGT-E mask or the FGT mask is generated via at least one further image which was taken at a different energy than the at least one LE image and the HE image,
the BPE result data is determined using combined images, or
the contrast medium-enhanced X-ray examination is a tomographic examination.
11 . A system for determining a Background Parenchymal Enhancement (BPE) in a contrast medium-enhanced X-ray examination of a breast, the system comprising:
a first data interface configured to receive images of the contrast medium-enhanced X-ray examination, the images having been taken after administration of a contrast medium, the images comprising at least one low energy (LE) image taken at a predetermined relatively low X-ray energy and high energy (HE) image which has been taken at a predetermined relatively high X-ray energy;
an iodine image unit configured to create an iodine image from the at least one LE image and the HE image;
a BPE unit configured to calculate a volumetric BPE as a sum of all pixel values in the iodine image for which iodine enrichment is present in the iodine image and fibroglandular tissue (FGT) is present and obtain BPE result data based on the iodine image and the volumetric BPE; and
a second data interface configured to output the BPE result data.
12 . A control facility configure to control a mammography system comprising the system of claim 11 .
13 . A mammography system comprising the control facility of claim 12 .
14 . A non-transitory computer program product comprising commands that, when executed by a computer, cause the computer to perform the method of claim 1 .
15 . A non-transitory computer-readable storage medium comprising commands that, when executed by a computer, cause the computer to perform the method of claim 1 .
16 . The method of claim 4 , wherein the assignment function assigns specific designations to intervals of a continuous range of values.
17 . The method of claim 16 , wherein the specific designations indicate four different degrees of strength or another range of values.
18 . The method of claim 6 , wherein the indicator is generated by:
calculating a breast density map from the at least one LE image or a linear combination of the at least one LE image and the HE image;
comparing intensity values of image elements of the breast density map with corresponding image elements in the iodine image; and
determining whether deviation values lie locally or globally outside a predefined range of values based on the comparing the intensity values of the image elements of the breast density map with the corresponding image elements in the iodine image.
19 . The method of claim 7 , wherein the performing the automated analysis includes at least one of,
determining whether the BPE is localized or homogeneously distributed in the iodine image, wherein morphological operators are applied sequentially to the iodine image or to an FGT enrichment (FGT-E) mask, or
determining the morphology and the texture of the BPE using Haralick texture functions applied to at least one of the FGT-E mask or the iodine image.
20 . The method of claim 8 , wherein the performing the automated longitudinal section assessment includes at least one of, calculating a relative change in volumetric BPE across the multiple X-ray examinations, or
calculating a change in at least one of a spatial distribution, a morphology or a texture of the BPE.