Name: guidelines and experience using imaging biomarker explorer (ibex) for radiomics
Uploaded: 2018-01-08T15:00:00
Description: Watch this Scientific Journal Video about Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics at JoVE.com

Rachel Ger is funded by the Rosalie B. Hite Graduate Fellowship and American Legion Auxiliary Fellowship. Carlos Cardenas has been funded by the George M. Stancel PhD Fellowship in the Biomedical Sciences. The development of IBEX was funded by the NCI (R03 CA178495).

1. Install IBEX
NOTE: To install a source-code version go to step 1.1. Alternatively, to install a stand-alone version go to step 1.2.
<ol>
	<li>Source-code version
	<ol>
		<li>Go to the IBEX source-code version website3. Download the &#34;IBEX_Source.zip&#34; and &#34;How_to_use.pdf&#34; files. Look in the &#34;How_to_use.pdf&#34; file to find the pre-requisites to use the latest IBEX version. 
		NOTE: IBEX works only on 32 bit and 64 bit Matlab...

<ol>
	<li>Lambin, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Radiomics:+extracting+more+information+from+medical+images+using+advanced+feature+analysis.">Radiomics: extracting more information from medical images using advanced feature analysis.</a> EJC. 48, 441-446 (2012).</li><li>Zhang, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IBEX:+an+open+infrastructure+software+platform+to+facilitate+collaborative+work+in+radiomics.">IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics.</a> Med Phys. 42, 1341-1353 (2015).</li><li>Haralick, R. M., Shanmugam, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Textural+features+for+image+classification.">Textural features for image classification.</a> IEEE Trans Syst Man Cybern. , 610-621 (1973).</li><li>Galloway, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texture+analysis+using+gray+level+run+lengths.">Texture analysis using gray level run lengths.</a> Comp Graphics and Im Proc. 4, 172-179 (1975).</li><li>Tang, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Texture+information+in+run-length+matrices.">Texture information in run-length matrices.</a> IEEE Trans on Im Proc. 7, 1602-1609 (1998).</li><li>Amadasun, M., King, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Textural+features+corresponding+to+textural+properties.">Textural features corresponding to textural properties.</a> IEEE Trans Syst Man Cybern. 19, 1264-1274 (1989).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Preliminary+investigation+into+sources+of+uncertainty+in+quantitative+imaging+features.">Preliminary investigation into sources of uncertainty in quantitative imaging features.</a> Comp Med Imaging Graph. 44, 54-61 (2015).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+radiomics+features+be+reproducibly+measured+from+CBCT+images+for+patients+with+non-small+cell+lung+cancer.">Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer.</a> Med Phys. 42, 6784-6797 (2015).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+image+preprocessing+on+the+volume+dependence+and+prognostic+potential+of+radiomics+features+in+non-small+cell+lung+cancer.">Impact of image preprocessing on the volume dependence and prognostic potential of radiomics features in non-small cell lung cancer.</a> Trans Cancer Res. 5, 349-363 (2016).</li><li>Fave, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Delta-radiomics+features+for+the+prediction+of+patient+outcomes+in+non-small+cell+lung+cancer.">Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer.</a> Scientific Reports. 7, 588 (2017).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stage+III+Non-Small+Cell+Lung+Cancer:+Prognostic+Value+of+FDG+PET+Quantitative+Imaging+Features+Combined+with+Clinical+Prognostic+Factors.">Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.</a> Radiology. 278, 214-222 (2016).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+Use+of+(18)F-fluorodeoxyglucose+Positron+Emission+Tomography-Based+Quantitative+Imaging+Features+for+Guiding+Dose+Escalation+in+Stage+III+Non-Small+Cell+Lung+Cancer.">Potential Use of (18)F-fluorodeoxyglucose Positron Emission Tomography-Based Quantitative Imaging Features for Guiding Dose Escalation in Stage III Non-Small Cell Lung Cancer.</a> IJROBP. 94, 368-376 (2016).</li><li>Mackin, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+Computed+Tomography+Scanner+Variability+of+Radiomics+Features.">Measuring Computed Tomography Scanner Variability of Radiomics Features.</a> Invest radiol. 50, 757-765 (2015).</li><li>Yang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uncertainty+analysis+of+quantitative+imaging+features+extracted+from+contrast-enhanced+CT+in+lung+tumors.">Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors.</a> Comp Med Imaging Graph. 48, 1-8 (2016).</li><li>van Rossum, P. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+incremental+value+of+subjective+and+quantitative+assessment+of+18F-FDG+PET+for+the+prediction+of+pathologic+complete+response+to+preoperative+chemoradiotherapy+in+esophageal+cancer.">The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer.</a> JNM. 57, 691-700 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NSCLC+tumor+shrinkage+prediction+using+quantitative+image+features.">NSCLC tumor shrinkage prediction using quantitative image features.</a> Comp Med Imaging Graph. 49, 29-36 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+quality+machine-robust+image+features:+identification+in+nonsmall+cell+lung+cancer+computed+tomography+images.">High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images.</a> Med Phys. 40, 121916 (2013).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prognostic+value+and+reproducibility+of+pretreatment+CT+texture+features+in+stage+III+non-small+cell+lung+cancer.">Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.</a> IJROBP. 90, 834-842 (2014).</li><li>Gan, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MO-DE-207B-09:+A+Consistent+Test+for+Radiomics+Softwares.">MO-DE-207B-09: A Consistent Test for Radiomics Softwares.</a> Med Phys. 43, 3706-3706 (2016).</li><li>Klawikowski, S., Christian, J., Schott, D., Zhang, M., Li, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-D-207B-07:+Development+of+a+CT-Radiomics+Based+Early+Response+Prediction+Model+During+Delivery+of+Chemoradiation+Therapy+for+Pancreatic+Cancer.">SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer.</a> Med Phys. 43, 3350-3350 (2016).</li><li>Huang, W., Tu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-F-R-22:+Malignancy+Classification+for+Small+Pulmonary+Nodules+with+Radiomics+and+Logistic+Regression.">SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression.</a> Med Phys. 43, 3377-3378 (2016).</li><li>Hanania, A. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+imaging+to+evaluate+malignant+potential+of+IPMNs.">Quantitative imaging to evaluate malignant potential of IPMNs.</a> Oncotarget. 7, 85776-85784 (2016).</li><li>Court, L. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+resources+for+radiomics.">Computational resources for radiomics.</a> Trans Cancer Res. 5, 340-348 (2016).</li><li>. Matlab Add path Available from: <a target="_blank" href="https://www.mathworks.com/help/matlab/ref/addpath.html">https://www.mathworks.com/help/matlab/ref/addpath.html</a> (2017)</li><li>Zhao, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+of+radiomics+for+deciphering+tumor+phenotype+with+imaging.">Reproducibility of radiomics for deciphering tumor phenotype with imaging.</a> Sci Rep. 6, 23428 (2016).</li><li>Owens, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+and+Robustness+of+Radiomic+Features+Extracted+with+Semi-Automatic+Segmentation+Tools.">Reproducibility and Robustness of Radiomic Features Extracted with Semi-Automatic Segmentation Tools.</a> Med Phys. , (2017).</li><li>Kassner, A., Liu, F., Thornhill, R. E., Tomlinson, G., Mikulis, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+hemorrhagic+transformation+in+acute+ischemic+stroke+using+texture+analysis+of+postcontrast+T1-weighted+MR+images.">Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images.</a> JMRI. 30, 933-941 (2009).</li><li>. IBEX Google Forum Available from: <a target="_blank" href="https://groups.google.com/forum/#!forum/ibex_users">https://groups.google.com/forum/#!forum/ibex_users</a> (2017)</li></ol>

IBEX is a powerful tool for medical imaging radiomics research. It has thus far mostly been used for radiation oncology purposes in studies conducted by the MD Anderson radiomics group. IBEX allows for manipulation of ROIs and calculation of features within 5 main feature categories. The source code version of IBEX allows the user to design applications that are not already part of IBEX, such as gray level zone matrix features.
The main steps involved in IBEX are the import of images, contouri...

In medicine, patient disease diagnosis typically incorporates a large number of diagnostic exams such as x-rays, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans to assist in determining the course of patient care. While physicians use these images to qualitatively assess patient&#39;s diagnosis, there may be additional quantitative features that can be extracted to guide patient care. The rationale is that these features may represent proteomic and genomic patterns expressed on the macroscopic scale1. Combining this quantitative information with the current clinical information, e.g., patient demographics, may allow more individualized patient care. This is the theory behind radiomics: feature analysis of images on a voxel level. The features typically fall into 5 main categories: gray level co-occurrence matrix, gray level run length matrix, neighborhood intensity difference matrix, histogram, and shape.
Imaging Biomarker Explorer (IBEX) is an open-source tool for radiomics work2. The graphical user interface (GUI) was developed at MD Anderson Cancer Center with the goal of facilitating the extraction and calculation of quantitative features to assist in decision making in cancer care. A source code3 and a stand-alone4 version are available online. IBEX calculates the 5 most common categories of features used in medical radiomics with parameters that can be set for each feature category. The categories are: gray level co-occurrence matrix5, gray level run length matrix6,7, intensity, neighborhood intensity difference matrix8, and shape. Since IBEX is open source, it allows for harmonized feature extraction results across institutions to easily compare different radiomics studies. All features within IBEX are described in the initial paper by Zhang et al.2
The purpose of this manuscript is to provide guidance on how to use IBEX and to demonstrate its applications through peer-reviewed published studies from the MD Anderson radiomics group. Since its release to the public in 2015, IBEX has been used to calculate features from CT, PET, and MRI scan images by the MD Anderson radiomics group, typically investigating features to improve clinical survival models9,10,11,12,13,14,15,16,17,18,19,20 and by outside institutions21,22,23,24. Additional guidance on software tools that can be used for the steps in radiomics research that are not included in IBEX can be found in Court et al.25
A general introduction to the workflow of IBEX will help to organize data properly before starting radiomics projects utilizing IBEX. If importing DICOM images, IBEX requires that each patient have their own folder with their DICOM images. DICOM radiation structure set is optional to include in the patient folder, but is recommended instead of using the contouring platform in IBEX. To assist with importing all patients for a specific study, all patient folders can be placed into one folder together so that all data may be imported into IBEX using only one step. If importing patients from Pinnacle, it is best to have the structure set with the patient plan. As patients may have multiple image sets and plans within Pinnacle, it is best to know which image set and plan are correct before importing. If computation time is a concern, reducing the number of image slices for a patient can drastically reduce time. For example, if only the liver is of interest in a study but the patients have full body CT scans, reducing the DICOM slices to only the extent of the area of interest can shorten computation time (e.g., reducing the DICOM from 300 slices to 50 slices can take 1/6th the time). There are different tools available to perform this slice reduction, from manual to semi-automatic.

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			<td height="42" style="height:42px;width:216px;">Excel</td>
			<td style="width:71px;">Microsoft Office</td>
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			<td style="width:167px;">Any version of excel should work.</td>
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			<td height="42" style="height:42px;width:216px;">Matlab</td>
			<td style="width:71px;">MathWorks</td>
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			<td>Only use IBEX on 32 bit Matlab 2011a or 64 bit Matlab 2014b.</td>
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guidelines and experience using imaging biomarker explorer (ibex) for radiomics

Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's graphical user interface (GUI) and to demonstrate how IBEX calculated features have been used in clinical studies. IBEX allows for the import of DICOM images with DICOM radiation therapy structure files or Pinnacle files. Once the images are imported, IBEX has tools within the Data Selection GUI to manipulate the viewing of the images, measure voxel values and distances, and create and edit contours. IBEX comes with 27 preprocessing and 132 feature choices to design feature sets. Each preprocessing and feature category has parameters that can be altered. The output from IBEX is a spreadsheet that contains: 1) each feature from the feature set calculated for each contour in a data set, 2) image information about each contour in a data set, and 3) a summary of the preprocessing and features used with their selected parameters. Features calculated from IBEX have been used in studies to test the variability of features under different imaging conditions and in survival models to improve current clinical models.

The output from IBEX is a spreadsheet (see Figure 4) that contains 3 tabs. The &#34;Results&#34; tab contains the feature values for each ROI in the data set (Figure 4A). The &#34;Data Info.&#34; tab contains information about the images taken from each ROI in the data set (Figure 4B). The &#34;Feature Info.&#34; tab contains a comprehensive list of features used wit...

Watch this Scientific Journal Video about Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics at JoVE.com

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

We describe IBEX, an open-source tool designed for medical imaging radiomics studies, and how to use this tool. In addition, some published works that have used IBEX for uncertainty analysis and model building are showcased.

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's ...

The overall goal of this software tool is to extract quantitative image features that fall into shape or texture such as gray level co-occurrence matrix categories. This tool can be used to answer some key questions in the field of radiomics. For example, you can extract quantitative image features and look at correlations between them and patient outcomes in radiation therapy.The main advantage of this technique is that it's open source, it's easy to use, and it has a long history of people using it for extracting features for use in prognostic models. Radiomics has potential to provide information about the likely response of a tumor to certain treatments, and therefore can provide additional information that clinical staff can use when determining the appropriateness of alternative treatments. Radiomics can provide insight into the tumor phenotype, it can also be applied to allosistance such as quanitfying changes of more tissue throughout treatment.Generally, individuals new to this method will struggle, because selecting the right settings to the different features such as the bit depth resampling is important, so it is vital to pay attention to these. Visual demonstration is critical as there are many steps and details. Careful attention to detail is important for good outcomes in radiomic studies.To begin, install IBEX described in the accompanying text protocol. Once installed, load IBEX and create a new user in order to import and save your data. Next, click on the import icon, and select either the DICOM or Pinnacle file version nine format by clicking on the name of the format, and then click next.To import all of the patients at once, check the batch folder box in the top right corner, and then click on the more button next to the listed directory. Select the folder that contains all patient folders with their corresponding DICOM images and radiation DICOM structure files. Finally, click the exit button.To begin, load a patient file by first clicking on the data icon. Then in the select a patient section, click on the desired patient file to open. Next, in the select image set section, click on the image set of the desired patient file, and then click the open button.Use the arrows to scroll through the images which are shown in the axial, coronal, and sagittal views. Then, select the zoom button and draw a box on the image plane to zoom. Right click on the image to zoom out.Next, click on the window level button. Another button will pop up, when it does, drag the left and right sides to the desired area, or manually enter the window level in window and level boxes at the bottom of the new window. Select different window levels using the first drop-down menu on the left.There are eight options for CT, and one option for PET. Visualize the regions of interest by clicking on the box next to the region of interest to make it visible on the images, or click on the On All ROIs"button to turn on all regions of interest. Next, prepare the data set in order to view the contours.To accomplish this, first click on the show data set button to view contours in the data set. Once data sets are viewed, close the window. This is a new user with just-imported patients, so we have no data sets created yet.Then, select the contours to add to the data set by clicking the check box next to their name, and click on the add to data set button. Click on the name of the data set to add the contours, or click the new button for a new data set. If new is selected, enter the name of the data set in the new window, and then click the OK button.When finished, click the exit button to return to the list of patients and scans. Click on the feature icon, and add preprocessing if desired, by clicking on the add button and selecting the preprocessing option from the drop-down menu in the new window. Next, click on the i under Parameters to select the preprocessing parameters.Select the number under the value column, type in the new parameter value, and then click the OK button. For description of the preprocessing method and the specific parameters, click on the question mark button in the upper right corner. Once satisfied, click the add button to add the preprocessing step.In order to delete undesired preprocessing steps, select the preprocessing step, and click delete. In feature categories menu, select the gray level coocurrence matrix 25 in the neighbor instensity difference 25 feature categories to compute the gray level co-occurrence matrix and neighborhood intensity difference matrix respectively in 2.5D. This is accomplished by calculating on each slice individually, and then summing all the matrices together.Another option is to select the gray level co-occurrence three in the neighbor intensity difference three feature categories to compute the gray level co-occurrence matrix and neighborhood intensity difference matrix, respectively, in 3D. Click on the I to see the selected parameters for that feature category. To change a parameter, enter the new value and click the OK button.For additional information, click on the question mark in the upper right corner. Once all of the parameters are properly set, click the test button to view a feature category or specific feature by clicking on the button next to the feature or category desired. Select the data set to view the test on, and then click the open button.Check the box next to the patients from the selected data set, and click the test button. Close the window once satisfied with the test. Uncheck any unwanted features for the category selected.To uncheck all features, click on the word features"under set two, features. In order to add all of the selected features with the selected preprocessing, click the add to feature set button. Select the name and the feature set, and click the open button, or, to add these features to a new set, click the new button.Enter the name of the feature set in the new window, and click OK.Next, click the show feature set button to view features with corresponding preprocessing techniques. Select the feature set to view, and then click open. Observe the preprocessing parameters and feature categories in dropdown menus.Edit them by clicking on the I button under parameters. Once finished, close the window. In the main feature window, click on the show data set button to view current data sets.Then, select the data set to view, and then click the open button. When finished, click the exit button. In order to prepare the output, first click on the result icon, then click on the data set to run the features on, under step one, and click on the feature set to run on the selected data under step two.Next, click on the view data button to view the selected data set, followed by the view feature button to view the selected feature set. Finally, click the compute and save result button. Enter the file name for the results and select save.The output from IBEX is a spreadsheet with three sheets. Shown here is a more complicated output with more regions of interest, and features selected than was run in this tutorial video. The first sheet contains the feature values for each region of interest placed in the data set.For the gray level co-occurrence matrix and gray level run length matrix features, a value is output for each direction that the matrix is computed in, as well as an average matrix across all directions that is designated as 333. The fourth row states the feature category, and the fifth row states the header for that column, either designated the index, image, region of interest, MRN, or specific feature. The second sheet contains the data information.This is information about the image that the region of interest was obtained from, such as the X, Y and Z voxyl dimensions. The third sheet contains the feature information. The features are separated by their category.The parameters for the feature category are listed in the second column. All the selected features for that category are listed in the third column. The fifth column shows the preprocessing that was applied to the features, and the sixth column shows the parameters for the preprocessing.Once mastered, this tool can be used to efficiently extract the texture data from medical images. It's important to remember to carefully review all data, including making sure the preprocessing and other feature settings are important. For example, use the IBEX review tool to carefully review the post-process images.After its development, this technique paved the way for researchers in the field of radiomics to explore the use of images to stratify patients for outcomes like local regional control and cancer patients. After watching this video, you should have a good understanding of how to use IBEX to extract image features from medical images. You can then put those image features into statistical or machine learning approaches to learn something about tumor phenotype.

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Research

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Engineering

Spatial Separation of Molecular Conformers and Clusters

Gas-phase molecular physics and physical chemistry experiments commonly use supersonic expansions through pulsed valves for the production of cold molecular beams. However, these beams often contain multiple conformers and clusters, even at low rotational temperatures. We present an experimental methodology that allows the spatial separation of these constituent parts of a molecular beam expansion. Using an electric deflector the beam is separated by its mass-to-dipole moment ratio, analogous to a bender or an electric sector mass spectrometer spatially dispersing charged molecules on the basis of their mass-to-charge ratio. This deflector exploits the Stark effect in an inhomogeneous electric field and allows the separation of individual species of polar neutral molecules and clusters. It furthermore allows the selection of the coldest part of a molecular beam, as low-energy rotational quantum states generally experience the largest deflection. Different structural isomers (conformers) of a species can be separated due to the different arrangement of functional groups, which leads to distinct dipole moments. These are exploited by the electrostatic deflector for the production of a conformationally pure sample from a molecular beam. Similarly, specific cluster stoichiometries can be selected, as the mass and dipole moment of a given cluster depends on the degree of solvation around the parent molecule. This allows experiments on specific cluster sizes and structures, enabling the systematic study of solvation of neutral molecules.

We present a technique that allows the spatial separation of different conformers or clusters present in a molecular beam. An electrostatic deflector is used to separate species by their mass-to-dipole moment ratio, leading to the production of gas-phase ensembles of a single conformer or cluster stoichiometry.

Gas-phase molecular physics and physical chemistry experiments commonly use supersonic expansions through pulsed valves for the production of cold ...

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

X-ray microtomography was used to image, at a resolution of 6.6 &#181;m, the pore-scale arrangement of residual carbon dioxide ganglia in the pore-space of a carbonate rock at pressures and temperatures representative of typical formations used for CO2 storage. Chemical equilibrium between the CO2, brine and rock phases was maintained using a high pressure high temperature reactor, replicating conditions far away from the injection site. Fluid flow was controlled using high pressure high temperature syringe pumps. To maintain representative in-situ conditions within the micro-CT scanner a carbon fiber high pressure micro-CT coreholder was used. Diffusive CO2 exchange across the confining sleeve from the pore-space of the rock to the confining fluid was prevented by surrounding the core with a triple wrap of aluminum foil. Reconstructed brine contrast was modeled using a polychromatic x-ray source, and brine composition was chosen to maximize the three phase contrast between the two fluids and the rock. Flexible flow lines were used to reduce forces on the sample during image acquisition, potentially causing unwanted sample motion, a major shortcoming in previous techniques. An internal thermocouple, placed directly adjacent to the rock core, coupled with an external flexible heating wrap and a PID controller was used to maintain a constant temperature within the flow cell. Substantial amounts of CO2 were trapped, with a residual saturation of 0.203 &#177; 0.013, and the sizes of larger volume ganglia obey power law distributions, consistent with percolation theory.

We present a methodology for the imaging of multiple fluid phases at reservoir conditions by the use of x-ray microtomography. We show some representative results of capillary trapping in a carbonate rock sample.

X-ray microtomography was used to image, at a resolution of 6.6 &#181;m, the pore-scale arrangement of residual carbon dioxide ganglia in the pore-space ...

Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization

Misfit dislocations in heteroepitaxial layers of GaP grown on Si(001) substrates are characterized through use of electron channeling contrast imaging (ECCI) in a scanning electron microscope (SEM). ECCI allows for imaging of defects and crystallographic features under specific diffraction conditions, similar to that possible via plan-view transmission electron microscopy (PV-TEM). A particular advantage of the ECCI technique is that it requires little to no sample preparation, and indeed can use large area, as-produced samples, making it a considerably higher throughput characterization method than TEM. Similar to TEM, different diffraction conditions can be obtained with ECCI by tilting and rotating the sample in the SEM. This capability enables the selective imaging of specific defects, such as misfit dislocations at the GaP/Si interface, with high contrast levels, which are determined by the standard invisibility criteria. An example application of this technique is described wherein ECCI imaging is used to determine the critical thickness for dislocation nucleation for GaP-on-Si by imaging a range of samples with various GaP epilayer thicknesses. Examples of ECCI micrographs of additional defect types, including threading dislocations and a stacking fault, are provided as demonstration of its broad, TEM-like applicability. Ultimately, the combination of TEM-like capabilities &#8211; high spatial resolution and richness of microstructural data &#8211; with the convenience and speed of SEM, position ECCI as a powerful tool for the rapid characterization of crystalline materials.

The use of electron channeling contrast imaging in a scanning electron microscope to characterize defects in III-V/Si heteroexpitaxial thin films is described. This method yields similar results to plan-view transmission electron microscopy, but in significantly less time due to lack of required sample preparation.

Misfit dislocations in heteroepitaxial layers of GaP grown on Si(001) substrates are characterized through use of electron channeling contrast imaging ...

Use of a Multi-compartment Dynamic Single Enzyme Phantom for Studies of Hyperpolarized Magnetic Resonance Agents

Imaging of hyperpolarized substrates by magnetic resonance shows great clinical promise for assessment of critical biochemical processes in real time. Due to fundamental constraints imposed by the hyperpolarized state, exotic imaging and reconstruction techniques are commonly used. A practical system for characterization of dynamic, multi-spectral imaging methods is critically needed. Such a system must reproducibly recapitulate the relevant chemical dynamics of normal and pathological tissues. The most widely utilized substrate to date is hyperpolarized [1-13C]-pyruvate for assessment of cancer metabolism. We describe an enzyme-based phantom system that mediates the conversion of pyruvate to lactate. The reaction is initiated by injection of the hyperpolarized agent into multiple chambers within the phantom, each of which contains varying concentrations of reagents that control the reaction rate. Multiple compartments are necessary to ensure that imaging sequences faithfully capture the spatial and metabolic heterogeneity of tissue. This system will aid the development and validation of advanced imaging strategies by providing chemical dynamics that are not available from conventional phantoms, as well as control and reproducibility that is not possible in vivo.

A multi-compartment dynamic phantom is used to simulate some biology of interest for metabolic studies using hyperpolarized magnet resonance agents.

Imaging of hyperpolarized substrates by magnetic resonance shows great clinical promise for assessment of critical biochemical processes in real time. Due ...

Dynamic Pore-scale Reservoir-condition Imaging of Reaction in Carbonates Using Synchrotron Fast Tomography

Underground storage permanence is a major concern for carbon capture and storage. Pumping CO2 into carbonate reservoirs has the potential to dissolve geologic seals and allow CO2 to escape. However, the dissolution processes at reservoir conditions are poorly understood. Thus, time-resolved experiments are needed to observe and predict the nature and rate of dissolution at the pore scale. Synchrotron fast tomography is a method of taking high-resolution time-resolved images of complex pore structures much more quickly than traditional &#181;-CT. The Diamond Lightsource Pink Beam was used to dynamically image dissolution of limestone in the presence of CO2-saturated brine at reservoir conditions. 100 scans were taken at a 6.1 &#181;m resolution over a period of 2 hours. The images were segmented and the porosity and permeability were measured using image analysis and network extraction. Porosity increased uniformly along the length of the sample; however, the rate of increase of both porosity and permeability slowed at later times.

Synchrotron fast tomography was used to dynamically image dissolution of limestone in the presence of CO2-saturated brine at reservoir conditions. 100 scans were taken at a 6.1 &#181;m resolution over a period of 2 h.

Underground storage permanence is a major concern for carbon capture and storage. Pumping CO2 into carbonate reservoirs has the potential to dissolve ...

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

Micro-electronic devices often undergo significant self-heating when biased to their typical operating conditions. This paper describes a convenient optical micro-imaging technique which can be used to map and quantify such behavior. Europium thenoyltrifluoroacetonate (EuTFC) has a 612 nm luminescence line whose activation efficiency drops strongly with increasing temperature, due to T-dependent interactions between the Eu3+ ion and the organic chelating compound. This material may be readily coated on to a sample surface by thermal sublimation in vacuum. When the coating is excited with ultraviolet light (337 nm) an optical micro-image of the 612 nm luminescent response can be converted directly into a map of the sample surface temperature. This technique offers spatial resolution limited only by the microscope optics (about 1 micron) and time resolution limited by the speed of the camera employed. It offers the additional advantages of only requiring comparatively simple and non-specialized equipment, and giving a quantitative probe of sample temperature.

Europium thenoyltrifluoroacetonate (EuTFC) has an optical luminescence line at 612 nm, whose activation efficiency decreases strongly with temperature. If a sample coated with a thin film of this material is micro-imaged, the 612 nm luminescent response intensity may be converted into a direct map of sample surface temperature.

Micro-electronic devices often undergo significant self-heating when biased to their typical operating conditions. This paper describes a convenient ...

Advanced Compositional Analysis of Nanoparticle-polymer Composites Using Direct Fluorescence Imaging

The fabrication of polymer-nanoparticle composites is extremely important in the development of many functional materials. Identifying the precise composition of these materials is essential, especially in the design of surface catalysts, where the surface concentration of the active component determines the activity of the material. Antimicrobial materials which utilize nanoparticles are a particular focus of this technology. Recently swell encapsulation has emerged as a technique for inserting antimicrobial nanoparticles into a host polymer matrix. Swell encapsulation provides the advantage of localizing the incorporation to the external surfaces of materials, which act as the active sites of these materials. However, quantification of this nanoparticle uptake is challenging. Previous studies explore the link between antimicrobial activity and surface concentration of the active component, but this is not directly visualized. Here we show a reliable method to monitor the incorporation of nanoparticles into a polymer host matrix via swell encapsulation. We show that the surface concentration of CdSe/ZnS nanoparticles can be accurately visualized through cross-sectional fluorescence imaging. Using this method, we can quantify the uptake of nanoparticles via swell encapsulation and measure the surface concentration of encapsulated particles, which is key in optimizing the activity of functional materials.

Here we present a reliable method to monitor the incorporation of nanoparticles into a polymer host matrix via swell encapsulation. We show that the surface concentration of cadmium selenide quantum dots can be accurately visualized through cross-sectional fluorescence imaging.

The fabrication of polymer-nanoparticle composites is extremely important in the development of many functional materials. Identifying the precise ...

Nanostructured Ag-zeolite Composites as Luminescence-based Humidity Sensors

Small silver clusters confined inside zeolite matrices have recently emerged as a novel type of highly luminescent materials. Their emission has high external quantum efficiencies (EQE) and spans the whole visible spectrum. It has been recently reported that the UV excited luminescence of partially Li-exchanged sodium Linde type A zeolites [LTA(Na)] containing luminescent silver clusters can be controlled by adjusting the water content of the zeolite. These samples showed a dynamic change in their emission color from blue to green and yellow upon an increase of the hydration level of the zeolite, showing the great potential that these materials can have as luminescence-based humidity sensors at the macro and micro scale. Here, we describe the detailed procedure to fabricate a humidity sensor prototype using silver-exchanged zeolite composites. The sensor is produced by suspending the luminescent Ag-zeolites in an aqueous solution of polyethylenimine (PEI) to subsequently deposit a film of the material onto a quartz plate. The coated plate is subjected to several hydration/dehydration cycles to show the functionality of the sensing film.

A protocol for the synthesis of moisture-responsive luminescent Ag-zeolite composites is described in this report.

Small silver clusters confined inside zeolite matrices have recently emerged as a novel type of highly luminescent materials. Their emission has high ...

Pore-scale Imaging and Characterization of Hydrocarbon Reservoir Rock Wettability at Subsurface Conditions Using X-ray Microtomography

In situ wettability measurements in hydrocarbon reservoir rocks have only been possible recently. The purpose of this work is to present a protocol to characterize the complex wetting conditions of hydrocarbon reservoir rock using pore-scale three-dimensional X-ray imaging at subsurface conditions. In this work, heterogeneous carbonate reservoir rocks, extracted from a very large producing oil field, have been used to demonstrate the protocol. The rocks are saturated with brine and oil and aged over three weeks at subsurface conditions to replicate the wettability conditions that typically exist in hydrocarbon reservoirs (known as mixed-wettability). After the brine injection, high-resolution three-dimensional images (2 &#181;m/voxel) are acquired and then processed and segmented. To calculate the distribution of the contact angle, which defines the wettability, the following steps are performed. First, fluid-fluid and fluid-rock surfaces are meshed. The surfaces are smoothed to remove voxel artefacts, and in situ contact angles are measured at the three-phase contact line throughout the whole image. The main advantage of this method is its ability to characterize in situ wettability accounting for pore-scale rock properties, such as rock surface roughness, rock chemical composition, and pore size. The in situ wettability is determined rapidly at hundreds of thousands of points. 
The method is limited by the segmentation accuracy and X-ray image resolution. This protocol could be used to characterize the wettability of other complex rocks saturated with different fluids and at different conditions for a variety of applications. For example, it could help in determining the optimal wettability that could yield an extra oil recovery (i.e., designing brine salinity accordingly to obtain higher oil recovery) and to find the most efficient wetting conditions to trap more CO2 in subsurface formations.

This protocol is presented to characterize the complex wetting conditions of an opaque porous medium (hydrocarbon reservoir rock) using three-dimensional images obtained by X-ray microtomography at subsurface conditions.

In situ wettability measurements in hydrocarbon reservoir rocks have only been possible recently. The purpose of this work is to present a protocol to ...

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine micro-organisms, and/or sediments in the ocean, or fluidized bed reactors and combustion processes in engineered systems. In order to study the effect of turbulence on the kinematics of particles in such flows, simultaneous measurements of both the flow and particle kinematics are required. Non-intrusive, optical flow measurement techniques for measuring turbulence, or for tracking particles, exist but measuring both simultaneously can be challenging due to interference between the techniques. The method presented herein provides a low cost and relatively simple method to make simultaneous measurements of the flow and particle kinematics. A cross section of the flow is measured using a particle image velocimetry (PIV) technique, which provides two components of velocity in the measurement plane. This technique utilizes a pulsed-laser for illumination of the seeded flow field that is imaged by a digital camera. The particle kinematics are simultaneously imaged using a light emitting diode (LED) line light that illuminates a planar cross section of the flow that overlaps with the PIV field-of-view (FOV). The line light is of low enough power that it does not affect the PIV measurements, but powerful enough to illuminate the larger particles of interest imaged using the high-speed camera. High-speed images that contain the laser pulses from the PIV technique are easily filtered by examining the summed intensity level of each high-speed image. By making the frame rate of the high-speed camera incommensurate with that of the PIV camera frame rate, the number of contaminated frames in the high-speed time series can be minimized. The technique is suitable for mean flows that are predominantly two-dimensional, contain particles that are at least 5 times the mean diameter of the PIV seeding tracers, and are low in concentration.

The technique described herein offers a low cost and relatively simple method to simultaneously measure particle kinematics and turbulence in flows with low particle concentrations. The turbulence is measured using particle image velocimetry (PIV), and particle kinematics are calculated from images obtained with a high-speed camera in an overlapping field-of-view.

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine ...