Advantage windows 4

Manufactured by GE Healthcare

Sourced in United States

Advantage Windows 4.6 is a software platform developed by GE Healthcare for managing and analyzing medical imaging data. It provides a user-friendly interface for accessing, manipulating, and visualizing medical images from various modalities.

Automatically generated - may contain errors

Lab products found in correlation

Close spelling variants of this product

Advantage Window 4.6 GE Advantage Windows 4.6 Advantage Windows Version 4.5 Advantage Windows 4.2 Volume Viewer Advantage Windows Workstation 4.6 Advantage Windows

24 protocols using advantage windows 4

Multi-Parametric MRI Protocol for Tumor Characterization

Check if the same lab product or an alternative is used in the 5 most similar protocols

All multi-b values and DWI images were transferred to a dedicated workstation (Advantage Windows 4.5; GE Healthcare, Milwaukee, WI, USA), and image classification was performed by using machine-assisted detection and classification (MADC) software (GE Medical Systems, Milwaukee, WI, USA) and ADC software (GE Medical Systems, Milwaukee, WI, USA). With double-exponential nonlinear model calculation, MADC software was used to generate pseudo-color parameter diagram, including slow ADC (D_slow), fast ADC (D_fast), and fraction of fast ADC (f). Here, each parameter was measured three times for the average value, and the average values were recorded. Single-exponential linear model was performed to calculate ADC, which was generated into a pseudo-color parameter diagram using the ADC software.
All PWI data were uploaded into a workstation (Advantage Windows 4.5), followed by an analysis and process in Cine Tool perfusion analysis software (GE Medical Systems, Milwaukee, WI, USA), and then all PWI data were analyzed by pharmacokinetics two-compartment model (Tofts model). With T1 calibration to tissues, the quantitative analysis was conducted, and in the meantime, pseudo-color parameter diagram was generated, including rate constant of backflux (K_ep), transfer constant of vessel at the maximum level (K^trans), and extravascular space fractional volume (V_e).

Lin M., Tian M.M., Zhang W.P., Xu L, & Jin P. (2016). Predictive values of diffusion-weighted imaging and perfusion-weighted imaging in evaluating the efficacy of transcatheter arterial chemoembolization for hepatocellular carcinoma. OncoTargets and therapy, 9, 7029-7037.

+ Open protocol

+ Expand

Diffusion-Weighted Breast Imaging Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

Diffusion-weighted breast imaging was conducted with a 1.5 Tesla MRI machine (Signa HDx; General Electric, Madison, WI, USA) and bilateral 8-channel high-density breast coil as a part of routine dynamic post-contrast breast MRI examination. The DWI sequence was obtained in the axial plane with b-values of 0 s/mm² and 600 s/mm² prior to contrast administration. The parameters of DWI were as follows: (a) sequence (echo planar imaging); (b) Repetition time (TR)/time to echo (TE): 900 ms/88.9 ms (c) Field of view (FOV: 36–40 cm); (d) matrix: 192 × 192; (e) slice thickness/interval: 5 mm/1 mm; (f): NEX (square root of the number of excitations): 16; (g) rBW (receiver bandwidth): 250 kHz. The total imaging time of the DWI sequence was 261 s. The images were transferred to the workstation (Advantage Windows 4.6; General Electric, Madison, WI, USA) to generate a black-and-white apparent diffusion coefficient map. The ADC value of the target breast lesion was measured on a single section containing the largest available tumor area, with an ROI 10–100 mm² in size. The ROI was placed manually on the solid part of the tumor, corresponding to enhancing areas, taking care to omit normal breast tissue as well as areas of necrosis and hemorrhage (Figure 2).

Orguc S, & Açar Ç.R. (2022). Correlation of Shear-Wave Elastography and Apparent Diffusion Coefficient Values in Breast Cancer and Their Relationship with the Prognostic Factors. Diagnostics, 12(12), 3021.

+ Open protocol

+ Expand

Evaluating Intervertebral Disc Degeneration and Paraspinal Muscle Characteristics

Check if the same lab product or an alternative is used in the 5 most similar protocols

All raw MR images were processed on a commercially available workstation (Advantage Windows 4.6, GE Medical Systems, USA). The degeneration degree of IVD at L4/5 and L5/S1 was assessed by two blinded experienced radiologists according to Pfirrmann grading system (I-V) by MRI T2 (37 (link)). The Pfirrmann grading system was divided into five grades to evaluate the homogeneity of intervertebral disc structure, signal strength, discrimination between nucleus and anulus, and disc height. When there was a disagreement, both radiologists discussed to achieve a consensus. Muscle cross-sectional area (CSA) and PDFF values of the bilateral paraspinal muscles were obtained on a region of interest (ROI) basis at the central level of L4/5 and L5/S1. The CSA and PDFF of the paraspinal muscles were measured at two-disc levels for each participant. The two radiologists manually delineated the shape of the bilateral multifidus and erector spinae (Figure 1). The muscle CSA was measured by manually delineating the ROI on the axial T2 images, then the same ROI was automatically copied by the workstation to the fat fraction map to obtain the PDFF value. The average of the two measurements was calculated and used for later analysis.

Huang Y., Wang L., Zeng X., Chen J., Zhang Z., Jiang Y., Nie L., Cheng X, & He B. (2022). Association of Paraspinal Muscle CSA and PDFF Measurements With Lumbar Intervertebral Disk Degeneration in Patients With Chronic Low Back Pain. Frontiers in Endocrinology, 13, 792819.

+ Open protocol

+ Expand

Paraspinal Muscle Fat Quantification

Check if the same lab product or an alternative is used in the 5 most similar protocols

All raw MR images were transferred to a commercially available workstation (Advantage Windows 4.6, GE Medical Systems, USA). Proton density fat fraction (PDFF) was automatically postprocessed with a vendor-provided algorithm after MRI scanning. PDFF values of the bilateral paraspinal muscles were obtained on a region of interest (ROI) basis at the central level of human L4/5 and L5/S1(Figure 2A), and rat L4/5 and L5/6. The edges of ROI were closed to the epimysiums of multifidus and erector spinae, excluding the subcutaneous fat and fat under profundal fascia. The two radiologists manually delineated the shape of the bilateral multifidus and erector spinae. The relative signal intensity of rat IVD = T2 signal intensity _{L4/5 or L5/6}/T2 signal intensity _L3/4. The average of the two measurements was calculated and used for later analysis. ICC Coefficients showed a high reliability of estimates between two radiologists (ICC = 0.939, 95%CI: 0.852-0.975, p<0.001).

Huang Y., Wang L., Luo B., Yang K., Zeng X., Chen J., Zhang Z., Li Y., Cheng X, & He B. (2022). Associations of Lumber Disc Degeneration With Paraspinal Muscles Myosteatosis in Discogenic Low Back Pain. Frontiers in Endocrinology, 13, 891088.

+ Open protocol

+ Expand

Reduced Field-of-View DWI Evaluation

Check if the same lab product or an alternative is used in the 5 most similar protocols

All the MRI images were evaluated and processed on the workstation (Advantage Windows 4.6; GE Healthcare). Two radiologists with 10- and 3-years’ experience in musculoskeletal imaging independently performed qualitative and quantitative image quality analyses for r-FOV DWI and conventional DWI, and measured ADC values. Conventional DW, r-FOV DW images, and ADC maps were cross-linked to ensure consistent delineation. Before the evaluation, the conventional DWI images were adjusted by a third radiologist to show the same area as in the r-FOV images for blinded and randomized reading.

Bian W., Wang L., Li J., Cui S., Wu W., Fan R, & Niu J. (2024). Comparison of reduced field-of-view DWI and conventional DWI techniques for the assessment of lumbar bone marrow infiltration in patients with acute leukemia. Frontiers in Oncology, 13, 1321080.

+ Open protocol

+ Expand

Multi-parametric MRI Analysis of Malignant Pleural Mesothelioma

Check if the same lab product or an alternative is used in the 5 most similar protocols

All raw MR images were processed on a commercially available workstation (Advantage Windows 4.6, GE Medical Systems, USA). T1WI and T2WI are used to observe the shape, size and location of the tumor. Through postprocessing software, the original T1 mapping and T2 mapping data were generated into T1 and T2 maps, and then the T1 and T2 values of the corresponding tumor region of interests (ROI) were obtained from T1 and T2 maps directly. The R2* and T2* values of the tumor tissue can be obtained in the BOLD. In addition to IVIM-DWI parameters, the apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo diffusion coefficient (D*), and perfusion fraction (f) were calculated using the biexponential mode. All MRI parameters of the whole tumor were obtained on ROI. The ROIs of all images were drawn manually and blindly at the same level and position by two radiologists with over 5 years of experience in MRI diagnostics. Considering the tumor heterogeneity of MPM, the ROI of this study was selected as the three solid component regions of the largest tumor layer. The average of the three measurements of each ROI was used for analysis.

Zhang Z., Shen S., Ma J., Qi T., Gao C., Hu X., Han D, & Huang Y. (2023). Sequential multi-parametric MRI in assessment of the histological subtype and features in the malignant pleural mesothelioma xenografts. Heliyon, 9(4), e15237.

+ Open protocol

+ Expand

Cardiac MRI and CT Ventricular Volume Measurement

Check if the same lab product or an alternative is used in the 5 most similar protocols

For the measurement of ventricular volume using cardiac MRI, a simplified contouring method was used to delineate the compacted endocardial border on short-axis slices [6 (link)]. The papillary muscles and trabeculae were excluded from mass measurements. Ventricular volumes at both end-diastolic and end-systolic phases were calculated by adding the endocardial areas of each short-axis slice and multiplying this total by the interslice distance.
To measure ventricular volume using cardiac CT, dual-source prospective electrocardiogram (ECG)-triggered and respiratory-triggered cardiac CT were performed using a body size-adapted, low-dose protocol. The technical details regarding the targeting of the end-systolic and diastolic phases in prospective ECG-triggered CT scan are described in a previous report [2 (link)]. Ventricular volume was quantified using a semiautomatic 3-dimensional (3D) region-growing method on a commercially available workstation (Advantage Windows 4.6; GE Healthcare, Milwaukee, WI, USA). The endocardial border was delineated using a signal intensity-based thresholding method. The threshold was manually adjusted until the appearances matched our visual assessment.

Kim H.J., Mun D.N., Goo H.W, & Yun T.J. (2017). Use of Cardiac Computed Tomography for Ventricular Volumetry in Late Postoperative Patients with Tetralogy of Fallot. The Korean Journal of Thoracic and Cardiovascular Surgery, 50(2), 71-77.

+ Open protocol

+ Expand

Cardiac CT Ventricular Volumetry Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

A stack of thin axial cardiac CT images was sent to a commercially available workstation (Advantage Windows 4.6; GE Healthcare, Milwaukee, WI, USA). For CT ventricular volumetry, a 3D threshold-based segmentation method enabling the exclusion of the papillary muscles and trabeculations from the ventricular cavity was used (Fig. 1A–D). An optimal segmentation threshold differentiating the most compact myocardium from ventricular blood was selected for each cardiac phase by using histogram-assisted analysis as previously described (6 (link)13 (link)). The AVV and semilunar valve planes were then manually adjusted. The preliminary volumetric segmentation was conducted by a radiology technologist and the final result was validated by a radiologist with 18 years of experience in cardiac CT. ES and ED ventricular volumes calculated for each cardiac CT examination (Fig. 1A–D) were indexed to body surface area. From the ventricular volumes, the LV-to-right-ventricular (LV/RV) volume ratio was calculated for each cardiac phase. To evaluate the reproducibility of CT ventricular volumetry, the quantification of 37 LV volumes was repeated with blinding to the initial results of 35 patients whose thin axial CT datasets were available.

Goo H.W, & Park S.H. (2018). Computed Tomography-Based Ventricular Volumes and Morphometric Parameters for Deciding the Treatment Strategy in Children with a Hypoplastic Left Ventricle: Preliminary Results. Korean Journal of Radiology, 19(6), 1042-1052.

+ Open protocol

+ Expand

Cardiac CT Volumetry for Ventricular Function Assessment

Cited 2 times

Check if the same lab product or an alternative is used in the 5 most similar protocols

The reconstructed cardiac CT image data were sent to a commercially available workstation (Advantage Windows 4.6; GE Healthcare, Milwaukee, WI, USA). A semiautomatic 3D threshold-based segmentation method allowing the exclusion of the papillary muscles and trabeculations from the ventricular cavity was used for ventricular volumetry. An optimal threshold was individually determined to separate the most compact interventricular septal myocardium from adjacent ventricular blood; then, the atrioventricular and semilunar valve planes were manually adjusted (16 (link)17 (link)18 (link)19 (link)). From these ventricular volumes, the ventricular ejection fraction (EF) {= (stroke volume [SV]/end-diastolic volume [EDV]) × 100}, SV difference (= RV SV − LV SV), RV volume load fraction (= [SV difference/RV SV] × 100), and ES and ED RV/LV volume ratios were calculated. The SV difference and the RV volume load fraction have been used to quantitate PR in previous studies using cardiac MRI (27 28 (link)). The measured volumes were normalized to the BSA.

, & Goo H.W. (2019). Changes in Right Ventricular Volume, Volume Load, and Function Measured with Cardiac Computed Tomography over the Entire Time Course of Tetralogy of Fallot. Korean Journal of Radiology, 20(6), 956-966.

+ Open protocol

+ Expand

Diffusion Tensor Imaging in Spinal Cord Injury

Check if the same lab product or an alternative is used in the 5 most similar protocols

A DTI scan was performed 24 hours before SCI and at 6 hours and 1, 2, 3 and 4 weeks after SCI using a 3.0 T SIGNA MRI scanner (GE Medical Systems, Milwaukee, WI, USA) at the same loci as the conventional MRI scan. The scanning parameters were as follows: diffusion-weighted coefficient (b-value) = 1000 s/mm²; diffusion-sensitive gradient = 15 different directions; repetition time = 3500 ms; echo time = 87.5 ms; thickness = 2.4 mm; space = 0; field of view = 10; acquisition matrix = 64 × 64. All data were input into a workstation running Advantage Windows 4.2 (GE Healthcare). The region of interest (ROI) was identified by the fat under the skin, which was displayed as a high signal on conventional T2WI MRI (Yan et al., 2007). Based on the fractional anisotropy (FA) map, the ROI was placed in the inferior medulla and the inferior oblongata. The ROI was selected by two independent testers, and apparent diffusion coefficient (ADC) and FA values were obtained. FA values reflect the degree of spatial displacement of water molecules, and higher FA values indicate stronger anisotropy. ADC values are independent of the diffusion directions, and indicate the diffusional displacement of water molecules.

Zhang D., Wang F., Zhai X., Li X.H, & He X.J. (2018). Lithium promotes recovery of neurological function after spinal cord injury by inducing autophagy. Neural Regeneration Research, 13(12), 2191-2199.

+ Open protocol

+ Expand

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?

Revolutionizing how scientists
search and build protocols!