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Functool

Manufactured by GE Healthcare
Sourced in United States

Functool is a versatile laboratory equipment designed to perform a range of tasks in clinical and research settings. The core function of Functool is to provide users with a reliable and efficient tool for various laboratory procedures. The product specifications and features are presented in a concise, factual, and unbiased manner without any interpretation or extrapolation.

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22 protocols using functool

1

Multiparametric MRI Tumor Analysis

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The IVIM and ASL raw data obtained are transferred into the GE-AW4.6 workstation Functool software for post-processing. On the image of IVIM-DWI, the two experienced radiologists manually delineate the region of interest(ROI) along the edge of the tumor at the maximum slice, ROI should include at least 2/3 of the lesion area. At the same time, reference should be made to plain scan and conventional enhanced scan images to avoid bleeding, necrosis, adjacent bone, air, etc. The IVIM parameters D(pure diffusion), D* , (pseudo-diffusion coefficient), and f(perfusion fraction) were measured.
The 3DpCASL images were fused with the axial T2WI-FS images, then, the region of interest(ROI), including all the entire tumor lesion, was drawn. To avoid obvious necrosis and artifacts, the enhanced images were used as a reference. The average blood flow (BFavg), minimum blood flow ( BFmin), and maximum blood flow (BFmax) were obtained by post-processing.
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2

Multiparametric MRI Analysis of Brain Regions

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Image analysis was performed automatically by the workstation (Advantage Workstation 4.4, GE Healthcare) with the use of the multi-ADC analysis algorithm (MADC) software in the Functool software package (GE Healthcare). Maps of standard ADC, fast ADC (ADCfast), slow ADC (ADCslow), fraction of fast ADC (f), distributed diffusion coefficient (DDC), and stretched exponential (α) were obtained. Parameters of ADC, ADCfast, ADCslow, f, DDC, and α were measured in the anterior limb of the internal capsule, posterior limb of the internal capsule, lenticular nucleus, and centrum semiovale that is supplied by anterior cerebral artery (ACA), middle cerebral artery (MCA), and posterior cerebral artery (PCA). To avoid the influence of the blood vessel, cerebrospinal fluid, and infarction, we drew a small region of interest (ROI, 20–40 pixels). The ROIs analysis on the parametric maps was performed by 2 radiologists who had 11 and 13 years of MRI diagnosis experience, respectively. The ROIs was drawn for 3 times at each site, and the mean of each measurement was used for analysis (Fig. 1).
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3

Multiparametric Quantitative MRI Mapping in Stroke

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The QSM reconstruction comprised the total field estimation using an adaptive, quadratic fit of the mGRE phase,48 (link) local field calculation with the projection onto dipole fields (PDF) method,47 (link) and susceptibility estimation using the morphology-enabled dipole inversion with automatic uniform CSF zero-reference algorithm.46 (link),52 (link)-54 (link) In stroke patients, the total field was estimated using a linearfit of the mGRE phase,55 (link) as 3D flow compensation was not available on the clinical scanner. The CBF maps (mL/100 g/min) were generated from the ASL data using FuncTool (GE Healthcare). All images were co-registered and interpolated to the resolution of the QSM maps using the FSL FLIRT algorithm.56 (link),57 (link)
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4

Quantitative Susceptibility Mapping and Cerebral Blood Flow

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QSM reconstruction was performed by estimating the total field via a non-linear fit of the mGRE,41 (link) obtaining the local field by the Projection onto Dipole Fields (PDF) method,42 (link) and computing susceptibility with the Morphology Enabled Dipole Inversion with automatic uniform cerebrospinal fluid zero reference (MEDI+0) algorithm.43 (link)–46 (link) Cerebral blood flow (CBF) maps (mL/100g/min) were generated from the ASL data using FuncTool (GE Healthcare, Waukesha, WI, USA). All images were co-registered and interpolated to the resolution of the QSM maps using the FSL FLIRT algorithm.47 (link),48 (link)
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5

Liver MRI Protocol for Tumor Evaluation

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The liver MR imaging was performed using a 3.0 T MR imager (Signa VH3, GE HealthCare) and a Mayo Clinic BC-10 MRI coil. After anesthesia, the rat was placed in a supine position in a plastic holder. The imaging parameters are described in Table 1. The axial liver MR imaging included free-breathing precontrast T1- and T2-weighted, DW imaging (b = 0 and b = 800 sec/mm2, with motion-sensitive gradients applied in three orthogonal directions to minimize the effects of diffusion anisotropy), and contrast-enhanced T1-weighted imaging (0.1 mmol/kg, Magnevist, Bayer-Schering). The HCC assessments were performed on a workstation (AW4.2; GE Healthcare) by the consensus of two experienced radiologists. The contours of the entire tumor on the enhanced T1-weighted images were manually drawn as regions of interest (ROIs), and the whole-tumor volume was determined. The ADC maps were generated using built-in software (Functool; GE Healthcare). The ROIs for whole-tumor volume measurement were also used for the ADC measurements. The day 10/baseline (D10/baseline) and day 20/baseline (D20/baseline) tumor volume ratios and the D10/baseline and D20/baseline ADC ratios were calculated.
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6

Multimodal Neuroimaging Analysis Pipeline

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QSM reconstruction was performed as follows: first, an adaptive quadratic-fit of the GRE phase was used to estimate the total field (34 (link)). Second, the Projection onto dipole fields (PDF) method was used to obtain the local field (35 (link)). Finally, the Morphology Enabled Dipole Inversion (MEDI) algorithm was used to compute susceptibility (12 (link),13 (link),37 (link)). The susceptibility values were referenced to the susceptibility of cerebrospinal fluid (CSF) averaged over a manually drawn ROI on the first echo of the GRE acquisition. CBF maps (ml/100g/min) were generated from the ASL data using the FuncTool software package (GE Healthcare, Waukesha, WI, USA). All images were co-registered and interpolated to the resolution of the QSM maps using the FSL FLIRT algorithm (38 (link),39 (link)).
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7

MRI-based Cervical IVD Evaluation

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MRI was performed using a 3.0-T GE Signa Echo-Speed MR scanner (GE Medical Systems, Milwaukee, WI, USA). All MRI images in this study were obtained in the afternoon to minimize the diurnal variation of T2 values in the IVDs [24] (link).
Sagittal T1-weighted fast spin echo (FSE) and sagittal, transversal, and axial T2-weighted FSE sequences were used for morphological MRI (for detailed sequence parameters see Table 1). The sagittal T2 weighted images (WIs) were used for visual Pfirrmann grading of IVD degeneration. Next, a T2 map was created using the T2 values in the midsagittal section from sagittal sections centered on the cervical midline region with optimized 8 echo multi-spin echo (Repetition time /first,last time echo time, TR/(fTE, lTE), 1500/8.5, 17.0, 25.5, 34, 42.4, 50.9, 59.4, 67.9, field of view (FOV)  = 20 mm×20 mm, section thickness  = 3.0 mm, matrix  = 256×160, number of signal intensity acquisitions  = 1, and total examination time 4 min and 27 s) obtained using an ADW 4.3 workstation (Functool, GE Medical Systems, Milwaukee WI, USA). However, the first echo from the multi-spin system was excluded to minimize the effect of the stimulated echo [14] (link). A single midline sagittal section was positioned parallel to each cervical IVD from C2–3 to C6–7. The T2 maps were computed in each pixel from the SI in the respective TE using the following formula: SI  = e−TE/T2.
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8

Diffusion Tensor Imaging Protocol for Studying Major White Matter Tracts

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Using a single-shot spin-echo echo planar image (SE-EPI) sequence, DTI data without diffusion weighting (b = 0) were acquired simultaneously in 25 non-collinear directions (b = 1,000 s/mm2). Moreover, 22 contiguous slices were acquired with a 3 mm slice thickness and with no gap. They were performed using acquisition with a 128 × 128 matrix; and a 256 x 256 mm field of view (FOV). The other acquisition parameters were: repetition time (TR) = 6,000 ms; echo-time (TE) = 76.2 ms; number of excitations (NEX) = 2.
DTI data were processed using the software FuncTool (GE Healthcare). The principal 3D orientation of the major eigenvector was color-coded per voxel. Each diffusion tensor was sampled 6 times to optimize the signal-to-noise ratio (SNR). Isotropic diffusion-weighted, ADC, and fractional anisotropy (FA) maps were generated. By positioning the region of interest (ROI) (33 ± 0.4 mm2) the targeted fibers, we got the FA values of the bilateral arcuate fasciculus (AF), uncinate fasciculus (UF), cingulum bundle (CB), fornix, superior longitudinal fasciculus (SLF) and anterior commissure (AC) as we previously described (30 (link)).
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9

Multimodal Imaging for Tumor Characterization

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For BLI, the average radiance was measured by drawing a region of interest (ROI) over the head using the software Living Image (PerkinElmer). For MRI, ADC values were calculated using Functool, a vendor-provided software analysis package for brain DTI (General Electric Healthcare). Tumor volume measurements were obtained from T2-weighted and post-CE T1-weighted images using ITK-SNAP 3.6.0, a freeware image analysis tool (www.itksnap.org). Tumor boundaries, chosen on the basis of post-CE T1-weighted images, were manually contoured in ITK-SNAP and tumor volumes were calculated. If noncontrast-enhanced (NCE) regions were present within tumors, separate ROIs were drawn to measure CE and NCE tumor volumes. Microscopic images of histological sections were coregistered to post-CE T1-weighted magnetic resonance (MR) images with a nonrigid, interactive, thin-plate spline extension by Gibson in the software 3D Slicer (Surgical Planning Laboratory, Harvard Medical School, Boston, MA) (31 ).
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10

Quantitative Susceptibility Mapping and Cerebral Blood Flow Analysis

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QSM reconstruction was performed as follows: first, an adaptive quadratic-fit of the mGRE phase was performed to estimate the total field (30 (link)). Second, the Projection onto dipole fields (PDF) method was used to obtain the local field (31 (link)). Finally, the Morphology Enabled Dipole Inversion with automatic uniform cerebrospinal fluid zero reference (MEDI+0) algorithm was used to compute susceptibility (32 (link)–35 ). CBF maps (ml/100g/min) were generated from the ASL data using FuncTool (GE Healthcare, Waukesha, WI, USA). All images were co-registered and interpolated to the resolution of the QSM maps using the FSL FLIRT algorithm (36 (link),37 (link)).
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