syngo.via Frontier Cardiac Risk Assessment

The first thing I'm going to start with is the easier research application that we have. The two that we are going to have a look at is the cardiac risk analysis and later on we're going to have a look at the coronary plaque analysis. In this case, what I have is a study with an abdominal data set as well as a calcium score. To open the case, please right click on the selected patient, select open with and then select CT cardiac risk assessment frontier. With the cardiac risk assessment, we have two areas. One is the pericardial fat measurement. That is automatically performed. We're going to have a look later on. And the other one is the abdominal fat evaluation, the visceral fat analysis. The name of the research application is a little bit confusing because you could also say it's a fat quantification of the pericardial as well as the abdominal fat. In recent years, intra abdominal visceral fat accumulation and epicardial fat has been suggested as playing an important role when it comes to cardiovascular disease. Coronary atherosclerosis, cardiovascular outcomes or even atrial fibrillation is one of the risks that could come out of that. We have different kinds of studies available. Any kind of thresholds that we have in those research applications usually come from publications. They are also uploaded in those information top cards. Let's have a look at the fat quantification. Based on the fat quantification, we will determine the risk for cardiac disease. Here in this case we have two series that are automatically imported. In case it doesn't load automatically, you can also manually drag and drop. The first step is to exclude unwanted data. For example, if I have the arms included into the data set, or I have here quite a lot of cables around there that will have an effect on the measurement that I'm going to do. O what I could do here? Even if I am not too worried about the cables, in this case I could draw different kind of contours. Two should be enough in this case. I click and find another slide over here for example, and I'm just going to get rid of those structures. In this case, I'm just going to press do removal. And as you can see, those will be excluded from the data set. Starting with the abdominal fat segmentation. In terms of quantification, as I said earlier, there are three steps. We have already performed the data preparation to remove the restructurings and now we come to the quantification. We're going to separate now the subcutaneous fat from the visceral fat. In this case, what I'm going to do I just zoom in and draw a contour. Based on the drawn contour through the muscle wall. If there is something outside, it still counts as subcutaneous fat, but we just separate it for now. Draw the contours and this is also a life wire, so my drawing should be a lot better than the ones I've done before. Anyway, I'm going to do it a little bit more slowly, just in case. I can already segment the area over here. What I have just done I separated and surrounded the visceral and the subcutaneous fat of a single slice. If the distribution of the fat is a little bit different than it is with this case right now, you can also draw multiple slices. This wouldn't be a problem if there is an interpolation going on. To perform the threshold based fat segmentation, press the segment fat button. The subcutaneous fat is highlighted in blue and the visceral fat region is colored in red. We also have the possibility to show a histogram to show the distribution of the contour. In this case this is all fine. Sometimes you have a second peak and this actually means that there is some kind of muscle included. Therefore, I would draw the region again. When it comes to the abdominal fat measurement, we have to do a few more steps. In this case, we're going to do some simple measurements. For example, what we're going to do is the waste circumference. I'm going to find an area here. This is the biggest. This is actually not a bad slice for that. Here, as you can see in this sometimes happens when you have a life wire, it just drops to the other line. The only thing you need to do is click one time with the left mouse button and then you can keep going. Good, this is already done. We have the waistline. We're going to draw the maximum diameter. Over here we're going to do the same for the minimum diameter. And we're going to do the same for the fat thickness. We're going to take the part here that has quite a lot of fat. Here are those values based on the threshold and everything else that we've done. We have here those absolute fat numbers. How do we interpret those numbers? If the value is above a certain number, as we can see here on the visceral slash subcutaneous number, there is a list of which cardiac diseases could be considered for that. Also here when it comes to subcutaneous fat and visceral fat, this is just the ratio of all the segmentation we have done. As you can see, the research application is quite easy and is an additional research application for the risk assessment when it comes to any kind of cardiac diseases that we have right now. The pericardial fat analysis is even easier. It doesn't get any easier actually, because it's completely automated. I'll just let it run. Therefore, the heart segmentation usage is already from single via. To get a heart draw over here. And a thresholding is already supplied to get the mesh of the volume. Also here we have different kind of thresholds that we use. These also come from different kind of papers that were done beforehand. When we had a look at the different thresholds, it also depends a little bit on the country what you are going to put in there. But you can also use for example threshold from America. There are some publications as references. For example, here I've read two studies and it was mentioned that an epicardial fat volume of more than 100 cubic centimeter was associated with a significantly higher coronary artery calcium score and higher incidence of a cat compared with patients with epicardial fat that was less than that. The other thing that you can do is to export the values. Save the values so that you have them available. Another thing that I have not mentioned yet, the segmentation essence in your gear is always available. Just in case you're not satisfied with the editing, you can also use that. For example, if I would like to use edit the contours, I can just do it normally as we know from singo via clicking in it and editing those structures. Thank you very much for your attention.

01.01.2013 01.01.2000 01.01.1946 01.01.1953 01.01.1964 01.01.1900 01.01.1952 01.01.1971 01.01.1957 01.01.1947 01.01.1927 11/11/2014 1.975 3/17/2022 1,975 1,001 19460101 0.6 -200 100 50 200 150 17 -150 -100 -50 0.2 0.4 >0.6 1200 1000 800 600 400 100.93 54.98 0.54 137 1375 137.51 137.5 Cardiac Risk Assessment syngo.via Frontier* *syngo.via Frontier & research applications are not intended for clinical use and are for research purposes only. Patient Browser Frontier (63) FRO* x Q Results (63 results, 16:50:59) Series Study Date ar + CT FRO_3DPRINTING_1 25.12.2008 14 dd75eccc49 11.04.2013 11 CT Pulmo 3D 04.02 2014 15 7079c72c21 17.05.2010 14 FRO_AspectScore_2 10.05.2010 18 FRO_BestContrast_Male 947239fd3e 19.04.2010 13 19.02.2008 13 FRO_BestContrast_Torso 27.09.2012 16 Instances FRO BoneSubtraction_3 c69142ac66 20.05.2011 13 26.05.2009 16 FRO CardiacRiskAnalysis_4 11.01.2013 14 e53021e6d4 FRO_Cardiac RiskAnalysis_4 11.11.2014 15 4316ba144b Search successfully completed MM Reading 1ed6b9d182 More Filters 16.12.2011 10 17.04.2012 15 01 01 1971 13.11.2012 17 FRO_CineRender_CAROTIS ... 19.01.2012 13 4313c9ca68 03.11.2014 15 03.11.2009 13 CT. OT 03.09.2010 09 07 12 2011 13 FRO_CINEMATIC_HEART1 01 01 2013 23.10.2013 12 FO_94 Abdomen 5.0 B31f 25.10.2012 08 FRO_Cardiac_Plaque_DE CT. OT ... 01 01.1971 FRO_Cardiac_Plaque_LAD FRO_CardicaFunction bd9e54674f Browse Print Open with DICOM Send to Delete Archive Results (10 results, 6:30:31 PM) Workflow Status Workflow Description Patient Name Patient ID Series Number Series Description Size Modality FRO_3DPrinting_Heart FO 94 64.04 MB 39.36 MB Report Data 88.47 KB SR 182.08 KB Segmentations MM 3D Printing_NotForClini ... 3.98 MB SEG ae6114cdfd Segmentations MM Radiomics_NotForClinic ... 8ca38b5d9a Table Segmentations 4.06 MB FRO_CineRender_Cardiac10Stents fc33d6c641 d83ab8a5e0 4 64 Workflows of the selected studies are cancelled 6:30 PM Print Open with DICOM Ope View as Read-Only with Batch Open Assign Workflow Group Studies Batch Assign Workflow Cancel Workflow Search Patient with DICOM Q/R Send to Archive Ctrl+Shift+S Add to Demo List Remove from Demo List es Set Mark state Set Delete Protection state Set Archive state Remove Correction state Correct Cut Paste Del Open Favorites CT CaScoring rClini ... CT Dental Clinic ... MM Radiomics Frontier Suggested Cinematic Playground CT Body Perfusion CT Bone Reading CT Cardiac CT Cardiac Function CT Cardiac Functional Analysis Frontier * CT Cardiac Planning CT Cardiac Risk Assessment Frontier CT cFFR Frontier Instanc Administrator * 01.01.1946 M 8ca38b5d9a Not for clinical use ASPECT Score Research Application Starting ... MM Research Frontier 8ca38b5d9a M Anonymous Hostpital SOMATOM Definition Flast CT 19460101 (-1-1-1) :- 1 HU Cardiac Risk Analysis Series Assignment Abdomen Abdomen 5.0 B Cardio FL_CaSc 3.0 B3 200 mm 300-mm Abdominal Undesired Data Removal (optional) Scan: 20141111 C/W: 40 / 400 Abdominal Fat Segmentation Histogram of Fat Volume for Threshold Estimation Abdominal Fat Measurement Pericardial Fat Analysis Number of Voxels Creat [HU] C/W: 40/ 400 Removal Text SOMATOM Definition Flash |8ca38b5d9a M of Voxels Number of V Snapshot Draw Contour (97 109 0) :- 997 HU Create Number Of V ShowHide Contout SOMATOM Definition Flash 8ca38b5d9a M Visibility (33 270 0): -984 HU (264 234 0): -9 HU (218 95 0): - 1005 HU (373 140 0) :- 903 HU (407 159 0): -202 HU (13 203 0) :- 1004 HU Remove Selected Part in Interpolated Contours (159 250 0): -3 HU Undo Last (23 274 0): -1005 HU of Voxe Delete Selected Contour Segment Show/Hide Show Hide Lower Threshold [HU] -150 Upper Threshold [HU] -50 Taxt (5 386 0): -955 HU (248 153 0) :- 68 HU 200-mm (151 223 0): -111 HU (16 220 0): -1009 HU (250 143 0): -61 HU Delete All (119 200 0): -65 HU (95 245 0): 27 HU 100 304 0): 23 HU Help (168 345 0): -108 HU (212 384 0): -31 HU 338 391 0): -77 HU Contou (399 319 0): -87 HU 440 261 0): -82 HU (412 197 0): 38 HU ShowHide Show Hide (347 156 0): -100 HU (38 217 0) :- 995 HU Segment Fat Show/Hide ShowHide Show/Hide Show Hide 300 mm Mean: -99 SD: 12 MPR+Histogram (322 59 0): 164 HU (124 76 0): -127 HU Thickness Subcutaneous Fat (ml) Visceral Fat (ml) Visceral / Subcutaneous 0.54 Save Diameter Waist le Fat (20 292 0): -997 HU (67 282 0): 33 HU (263 277 0): 137 HU Brittany Irwin (452 279 0): -116 HU (452 279 0) :- 116 HU 45.56 cm (478 329 0): -877 HU Distance 43.94 cm 42.63 cm (442 234 0): -108 HU 110 cm (472 316 0): -46 HU 45.01 cm (458 348 0): -90 HU 42.35 cm (467 296 0): -108 HU 47.01 cm (458 244 0) :- 663 HU 42.44 cm (433 384 0): -101 HU 35.78 cm (365 416 0): -805 HU (254 419 0): 39 HU 70.41 cm (46 341 0): -1005 HU (60 282 0): -872 HU (65 390 0): -767 HU Max VaistLine length (14 387 0): -954 HU Measure Left-Right Diameter (60 313 0): -796 HU (477 293 0) :- 876 HU 32.99 cm (244 115 0) :- 805 HU Max Diameter 200 m (289 407 0): -35 HU (389 329 0): -71 HU Min Diameter (436 367 0): -112 HU 5.22 cm (146 445 0): -944 HU 6.24 cm (63 471 0): -399 HU 23.76-cm VaistLine lengt VaistLine leng Min Use Abdominal Fat Thresholds Pericardial Fat (ml) 00 mm- CaSc 3.0 B35f 60% 64 bpm, 81 %, 75 ms Reset 70-mm- (52 391 0): -481 HU 70 mm (103 7 0): -72 HU The user must ensure that the Frontier Research Applications and/or the results obtained by their utilization will be used for Research Use only. 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