Scans were performed according to SCCT guidelines (22 (link)) for the performance of CCTA with a multidetector computed tomography (CT) scanner (256 iCT; Philips Healthcare, Andover, MA, USA). Helical scan data was obtained using retrospective or prospective electrocardiographic gating. Prior to performing the CCTA, 50–60 ml iodinated contrast media (370 mg I/ml, Bayer AG, Leverkusen, Germany) was injected followed by a 35–40 ml saline flush (4.5–5 ml/s). The threshold tract trigger mode of the region of interest was adopted (23 (link)). Images were reconstructed at 40, 45 and 78% of the cardiac cycle, consistently and immediately following the completion of a scan. The optimal phase reconstruction was assessed by comparison with the phase that had the least amount of coronary artery motion. The images were evaluated using transaxial two-dimensional image stacks (raw data), multiplanar reformations, maximum intensity projections, curved multiplanar reformations and volume-rendering technique reconstructions. If a coronary artery segment was uninterpretable (characterized by artifacts, such as motion, beam hardening, metal or calcium-related partial volume averaging, and adequacy of over contrast concentration or opacification) (24 (link)), it was not included in the analysis. IntelliSpace Portal 6 (Philips Healthcare, Andover, MA, USA) was used to analyze the images.
Intellispace portal 6
Manufactured by Philips
Sourced in United States, Netherlands
The IntelliSpace Portal 6 is a comprehensive, advanced visualization and analysis platform designed for healthcare professionals. It provides advanced imaging tools and applications to aid in clinical decision-making. The core function of the IntelliSpace Portal 6 is to offer a centralized, integrated solution for medical image analysis and interpretation.
Automatically generated - may contain errors
Lab products found in correlation
12 protocols using intellispace portal 6
1
Multidetector CT Coronary Angiography Protocol
2
Quantitative Phantom Imaging Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Phantom Study. Quantitative measurements were performed on a dedicated workstation (IntelliSpace Portal 6; Philips Healthcare). For each sphere, we determined the mean and maximum 18 F-FDG concentration (kBq/mL) in both standard-and small-voxel reconstructed image sets. The mean 18 F-FDG concentration was calculated from a 3D isocontour created at 50% of the maximum pixel value. Furthermore, background measurements were performed in a circlebased region of interest (ROI) of approximately 2,000 mm 2 (NEMA phantom) and 400 mm 2 (microphantom) localized in a homogeneous region in a background part of the phantom. For the NEMA phantom, we performed background measurements on the most central axial slice, at least 20 mm away from both the phantom edge and the phantom spheres to prevent influence of the partial-volume effect. For the microphantom, we performed the background measurements in an axial slice 20 mm below the spheres. The mean 18 F-FDG concentration and SD in the ROI were determined. Using Equation 1, we calculated the noise in the phantom background compartment.
3
Pericardial Fat Quantification from mDIXON
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
In-phase, opposed-phase, water-only, and fat-only images were reconstructed online at the scanner. Pericardial fat volume was extracted from fat-only mDixon images using a dedicated volumetric tool (IntelliSpace Portal 6; Philips Healthcare).
4
Cardiac Functional Analysis by Experienced CMR Readers
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Two readers with 2 (J.A.L.) and 10 (C.P.N.) years of experience in CMR analyzed the data and performed the measurements. Readers were blinded to the clinical information. Cardiac functional analysis was performed offline using dedicated software (IntelliSpace Portal 6; Philips Healthcare).
5
Quantifying Myocardial Fibrosis on LGE
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The presence, respectively, and the absence of fibrosis on LGE images were qualitatively assessed by consensus agreement between the 2 readers. In addition, enhanced areas were determined quantitatively using dedicated software (IntelliSpace Portal 6; Philips Healthcare) after manually contouring the endocardial and the epicardial contours Downloaded from http://ahajournals.org by on September 3, 2024 of SA LGE images. Enhanced areas were defined as those with SI≥3.0 SD above the mean SI of normal myocardium. 21 (link) Enhanced volume percentage was calculated from enhanced and nonenhanced myocardial volumes.
6
Measuring Liver Stiffness and Steatosis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
One of two abdominal radiologists independently measured liver stiffness using a magnetic resonance software tool (Philips IntelliSpace Portal 6.0, Philips Healthcare, Best, Netherlands). Regions of interest were manually drawn as geographic areas on each of the four slices in portions of the liver that had adequate wave amplitude, avoiding areas close to the major blood vessels, liver margins, and artifacts. The mean stiffness value was assessed by averaging the values across the regions of interest, and the result was displayed automatically on each MRE slice in units of kilopascals (kPa). The values measured on the four slices were averaged. When the value was ≥3.6 kPa, it was defined as advanced fibrosis [18 (link)]. In contrast, an axial 3D multi-echo modified Dixon gradient echo sequence (mDIXON–Quant) was obtained to evaluate hepatic steatosis [19 (link)].
7
Quantifying Liver Stiffness via MRE
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
One of two abdominal radiologists independently measured liver stiffness using a magnetic resonance software tool (Philips Intellispace Portal 6.0, Philips Healthcare). Regions of interest were drawn as geographic areas manually at each of the four slices in portions of the liver that had adequate wave amplitude, avoiding areas close to the major blood vessels, liver margins, and artifacts. The mean stiffness value was assessed by averaging the values across the regions of interest, and the result was displayed automatically on each MRE slice in units of kilopascals (kPa). The values measured on the four slices were averaged.
8
Automated Coronary Artery Centerline and Stenosis Quantification
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
We computed the coronary artery centerlines and the aorta segmentation automatically with a commercially available software dedicated for cardiac image analysis (Comprehensive Cardiac Analysis, IntelliSpace Portal 6.0, Philips Healthcare). We further manually adjusted the result of the automatic software to improve overall centerlines quality using its graphical user interface.
Next, we segmented the coronary lumen and wall using the automatic coronary lumen segmentation algorithm of Freiman et al 20 (link) with manual adjustments where required.
We then extracted 3D coronary cross-sectional patches of size 80x80x8 mm along the coronary centerline using an in-house software. Inspired by Kitamura et al 21 (link) , we calculate the ratio between the wall area (𝑊 ) and lumen area (𝐿 ) for each coronary cross-section and define a cross-sectional stenosis grade as:
Finally, we select coronary cross-sections with stenosis grade < 0.2 from the training datasets and used them to train the neural networks.
Next, we segmented the coronary lumen and wall using the automatic coronary lumen segmentation algorithm of Freiman et al 20 (link) with manual adjustments where required.
We then extracted 3D coronary cross-sectional patches of size 80x80x8 mm along the coronary centerline using an in-house software. Inspired by Kitamura et al 21 (link) , we calculate the ratio between the wall area (𝑊 ) and lumen area (𝐿 ) for each coronary cross-section and define a cross-sectional stenosis grade as:
Finally, we select coronary cross-sections with stenosis grade < 0.2 from the training datasets and used them to train the neural networks.
9
AI-Driven Coronary Lumen Segmentation
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The proposed coronary lumen segmentation algorithm requires the following inputs:
1) The CCTA volume
2) The coronary-artery centerlines
3) The segmentation of the aortic root The coronary artery centerlines and the aorta segmentation were computed automatically and adjusted manually by a cardiac CT expert (M.V) to account for algorithm inaccuracies using a commercially available software dedicated for cardiac image analysis (Comprehensive Cardiac Analysis, IntelliSpace Portal 6.0, Philips Healthcare).
The coronary lumen segmentation algorithm starts with the analysis of the intensity profile along the coronary centerline to detect regions with small lumen diameter that may be overestimated due to the PVE, followed by estimation of underlying lumen radius, which is then used within a machine-learning based graph-cut algorithm yielding the final segmentation. Fig. 2 presents a schematic flowchart of the proposed algorithm. We describe each step in detail in the following. Analysis of the intensity profile along the coronary centerline to detect regions with small diameter lumen that may be overestimated due to the PVE, 2) Estimation of underlying lumen radius, 3) Transformation into a cylindrical coordinate system around the coronary centerline, 4) machine-learning based likelihood estimation, and; 5) final segmentation by the graph-cut segmentation framework.
1) The CCTA volume
2) The coronary-artery centerlines
3) The segmentation of the aortic root The coronary artery centerlines and the aorta segmentation were computed automatically and adjusted manually by a cardiac CT expert (M.V) to account for algorithm inaccuracies using a commercially available software dedicated for cardiac image analysis (Comprehensive Cardiac Analysis, IntelliSpace Portal 6.0, Philips Healthcare).
The coronary lumen segmentation algorithm starts with the analysis of the intensity profile along the coronary centerline to detect regions with small lumen diameter that may be overestimated due to the PVE, followed by estimation of underlying lumen radius, which is then used within a machine-learning based graph-cut algorithm yielding the final segmentation. Fig. 2 presents a schematic flowchart of the proposed algorithm. We describe each step in detail in the following. Analysis of the intensity profile along the coronary centerline to detect regions with small diameter lumen that may be overestimated due to the PVE, 2) Estimation of underlying lumen radius, 3) Transformation into a cylindrical coordinate system around the coronary centerline, 4) machine-learning based likelihood estimation, and; 5) final segmentation by the graph-cut segmentation framework.
10
Dual-layer Spectral CT Imaging of Chest
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Contrast-enhanced chest scans were performed in both arterial phase (AP) and venous phase (VP) on dual-layer spectral detector CT (IQon Spectral CT, Philips Healthcare, Best, The Netherlands). The range of scan was from the thoracic inlet to the bottom of the thoracic cavity in order to cover all lung tissues. After a native chest scan, contrast agent (Iodixanol, 350 mg/mL, GE Healthcare, Ireland) was injected via the cubital vein with a power injector (Ulrich REF XD 2051), at a volume of 80 ml and flow rate of 2.5 mL/s. AP and VP scans were acquired at 25 and 60 s after the contrast agent injection. Spectral CT scan parameters were as follows: tube voltage = 120 kV; automatic tube current exposure control [Dose Right Index (DRI)] = 22; tube rotation time = 0.5 s; detector collimation = 64 × 0.625 mm; a reconstructed slice thickness = 0.9 mm; slice increment = 0.45 mm; field of view = 250 × 250 mm; image reconstruction matrix = 512 × 512. All original images were reconstructed as Spectral Base Image (SBI) datasets with reconstructed slice thickness of 1 mm and increment of 1 mm, then were transmitted to a dedicated post-processing workstation of spectral CT (IntelliSpace Portal 6.5, Philips Healthcare, Best, The Netherlands) for image analysis.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!