In 2011, we developed a software tool to analysis of the echogenicity level in ultrasound B-MODE images. This software is based on binary thresholding in a predefined Region of Interest (ROI). The goal of this paper is to observe if the echogenicity grade in B-MODE images corresponds with brightness level in MR images using the echogenicity index. Achieved results obtained by two, non-experienced observers in radiology, shows the software can be used also for MRI images. The reproducibility of the measurement evinces the high level of agreement. We use three ROI areas for which the exact position in MR image is not important at this moment. Totally of 52 images were analyzed. Achieved results show the error between measurements by two non-experienced observers does not exceed 5 %; calculated based on the range of the measurements and computed average difference for each image set. Thus, the echogenicity index can be considered as reproducible marker; a small shift of the ROI does not evince significant change. Average range of the index is computed from 28.17 up to 67.95; minimal index value was < 20 and the highest value was 101.2 due to different brightness level in the examined ROI. The range for the same ROI are almost equal, the difference does not exceed 2 %.
Our software has been developed for ultrasound
Bimaging [
In modern neurology and neurosurgery, MRI is one of
the most progressive medical imaging for all perioperative
phases [
Copyright ©2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
In this study, we have three sets of T1 and T2 MR images
(two basic types of MR images) [
We analyze three ROI areas with different size and shape, see Fig. 2. For each image, all three ROI areas are placed in the same position in the image. In other words, each image is analyzed thrice using three different ROI in the same position.
The size and shape of the ROI were defined in the past
for B-MODE images. Originally, ROI1 was used for ncl.
raphe analysis and ROI2 has been defined for substantia
nigra area; both in B-MODE images. Square-shaped ROI3
has been used to analyze medial temporal lobe (MTL) in
different case; in measurement of the black/white pixel
ratio in the ROI 20 20 mm to judge a probability of MTL
atrophy as a marker for the dementia [
In the case of B-MODE images, the echogenicity index should corresponds with echogenicity grade of the tissue. We can use the same index for MR images, in which the index should corresponds with brightness level of the examined part inside the ROI. The index is one numerical value computed using our software.
More information about the methodology, see [
ECHOINDEX = (1) å2H5=50 AH 100
Due to the principle of binary thresholding, for lower echogenicity grade, the Echo-Index should be lower and for higher echogenicity the Echo-Index should be higher. This is an assumption which proceeds from the principle of binary thresholding. We have used the index in MR images to judge general reproducibility between two nonexperienced observers.
An example of achieved results for a selected image set of 14 images, is stated in Table 1.
From achieved results we can judge the echogenicity index could be well-applicable in general as a feature in MRI analysis.
In Table 2 you can see the average differences for each ROI in four image sets. It is closely related to judge level of agreement which is almost perfect. The data in Table 2 shows that the differences between observers are minimal in the case of the same position of the ROI including a small shift. It seems that small ROI position changes which are not recognizable visually, have no significant influence on resulted echogenicity index. From achieved results, the range of the index is from 28.17 up to 38.89; very similar for each image set. According to the range and computed average differences between the observers, the difference between observers is smaller than 5 %. In the case of US imaging, the image can be adjusted dynamically during examination by ultrasound probe settings; we can increase or decrease the brightness level according to examined tissue density. The echogenicity grade displayed on acquired digitized image can be different visually for the same tissue density. Due to this fact we need to analyze the image sets with same probe (image) settings to avoid incorrect echogenicity evaluation. See Fig. 3 in which three TCS B-MODE images with different global brightness level and corresponding histogram profiles are shown.
In MRI, the settings of the MR machine are
determined by the manufacturer. All image enhancements are
set in post-processing phase in digitized MR images, like
histogram equalization using different algorithms (LHE,
GHE) [
The brightness settings should not be affected by settings during examination but there is other limitation in MR images corresponding with ROI selection in MR slices, see the following chapter. 2.2
Although it seems the echogenicity index is well reproducible, there is one important limitation. In Fig. 5, there is the example of using the same ROI size and shape to select a structure (it is not important from medical point of view at this moment). Due to weighted MRI, the examined structure can be smaller, larger, deformed or may not visible. In the case of ultrasound B-MODE images, like the substantia nigra in TCS images, the position and size is determined; only echogenicity grade is different corresponding the gain settings, angle, etc.
In this case, totally different echogenicity grade can be obtained for the same patient but in different MR image. Thus, another ROI types will be defined in the future which should be better adapted for different MR images to examine structures. This limitation is also a barrier for automatic ROI selection discussed in the following chapter. 3
In our previous study dedicated to atherosclerotic plaques
analysis in B-MODE images, we also have discussed the
possibility to automatic learning of the plaque detection
using ANN [
Thus, it could be hard to apply an automatic recognition a ROI described by shape or size when is changing in the weighted MR imaging.
The principle could be based on iterative learning using
a convolutional neural network (CNN) which uses
filtering to extract some features to recognize the region. CNN
are designed to work with grid-structured inputs, like 2D
images. There are many advanced techniques using CNN
in medical imaging like in [
In 2020, we presented an idea to automatic segmentation
based on boundary recognition of atherosclerotic plaques
in B-MODE images [
In the case of MRI, there is a different task. There are no exact borders to find the ROI. In Fig. 6, the weighted MR images are displayed; it could be hard to learn what to consider as a feature. Let to have a structure in MR image which is probably located equally based on radiologist’s experience. There is difficult to learn any shape and size of the ROI due to changing weighted MR images.
In this field, there is an interesting inspiration how to
develop an automatic segmentation using deep learning
approach in T1 and T2 weighted images [
To CNN training, the back-propagation algorithm is used; similarly as in linear feed-forward ANN architecture. Input image is represented as a single vector w h d where w and h represent image resolution and d to be color depth, in this case d = 1 (for RGB channels d = 3). Each pixel is represented as intensity value in the range of 0 to 255. CNN uses ReLU (Rectified Linear Unit) activation function instead of sigmoid or hyperbolic tangent in traditional multi-layer backpropagation networks. In general, CNN has the following layers and functions: 1. input layer (as a single vector w h d) 2. convolutional layer (3 3 or 5 5 convolutional masks are commonly used) to extract feature map
4. pooling (sub-sampling) layer (to reduce dimensionality of feature maps using MaxPooling algorithm)
Convolutional layer with ReLU and pooling layer are designed for feature extraction and the fully-connected layer with softmax function is used to classification. In our case, we need to recognize a structure in MR image which is defined by a radiologist, e.g. in Fig. 5 and/or in Fig. 10. The process is illustrated in Fig. 9.
Deep learning paradigm is based on learning rules from
inputs and desired outputs. This is main difference from
traditional programming when we have inputs, rules and
we need to create outputs. Deep learning requires large
data amount to efficiency. In comparison with
traditional neural networks and learning, deep learning should
achieve better accuracy related to increasing data amount
[
In a critical point, depending on data complexity and its structure, conventional paradigms could be inefficient due to overfitting so the learning rate is low or stopped.
In MR images, we can use deep learning approach to learn the rules to recognize the ROI. In general, deep learning is focused on training with pairs input-output from large datasets, e.g. thousands of images. Thus, when we need to learn a specific structure in MR images, the training is based on input-output training set to learn rules, i.e. features, to find an appropriate structure to place a predefined ROI. The idea of deep learning using CNN is illustrated in Fig. 9.
Consider the following six MR images in Fig. 10. Consider a task to find the highlighted anatomic structure (square-shaped ROI). It seems, there is really hard to learn the features of the structure because it is from small to bigger area in which the structure is located.
To effective training and learning the network, a large
set of images is needed to learn how to recognize the
structure from input-output training set. For example, we can
learn the edge, the brightness difference, the shape, e.g.
roundness, height/width ratio, etc. and another feature. In
this task, deep learning could be applied to help to extract
the features to recognize the structure. The background of
the principle of the image convolution algorithm in CNN
you can find in [
There are many ways for practical implementation. One of the most known to be Keras, a high-level modular API developed for Python programming language using GPU acceleration. More information, code samples (including using for CT scans) are available on Keras.io website. 4
The goal of the paper is to show how to use echogenicity index, computed with ultrasound B-MODE images, in MR images. To this purpose, we have analyzed sets of T1 and T2 MR images. The principle of the analysis is equal as for B-MODE images. The core of the algorithm is based on binary thresholding of the images in grayscale. Within this MR images analysis, the main idea is also applicable in MR images; the higher index value should correlate with higher brightness intensity and vice versa.
Achieved results show the principle of the echogenicity index could be applied for B-MODE images and MR images independently. It seems, the echogenicity index is well applicable to observe different brightness in MRI equally as in the case of B-MODE images. The obtained differences are not significant, but the software is more sensitive than visual assessment in general.
Finally, we can recommend using this methodology in future clinical studies focused on the analysis of MRI using different ROI shapes and sizes according to examined structure in MR image. In future, we will use a new ROI areas, like a circle-shaped and/or free-hand closed area, defined by an experienced sonographer. It is related to examined structures in MR images, see Fig. 8 as the example.
In parallel, we are working on analysis of the echogenicity index differences between a light area and a dark area within the same ROI.
This work was supported by European Union under European Structural and Investment Funds Operational Programme Research, Development and Education project "Zvýšení kvality vzdeˇlávání na Slezské univerziteˇ v Opaveˇ ve vazbeˇ na potrˇeby Moravskoslezského kraje" CZ.02.2.69/0.0/0.0/18-058/0010238 and project CZ.02.2.69/0.0/0.0/18-054/0014696 "Rozvoj VaV kapacit Slezské univerzity v Opaveˇ, "Rozvoj metod teoretické a aplikované informatiky" SGS/11/2019 and the image use from grant No.16-28628A.