Velocity ai 3

Manufactured by Agilent Technologies

Sourced in United States

Velocity AI 3.2.1 is a high-performance data analysis software developed by Agilent Technologies. It is designed to provide users with advanced analytical capabilities for their laboratory equipment and experiments. The core function of Velocity AI 3.2.1 is to process and analyze data from various sources, enabling users to derive insights and make informed decisions.

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Lab products found in correlation

Standard t2 weighted mri scanner, by Siemens (3 mentions) Avanto 1.5t scanner, by Siemens (2 mentions) Somatom definition as, by Siemens (1 mentions) Mri scanner, by GE Healthcare (1 mentions) Somatom definition as ct scanner, by Siemens (1 mentions)

11 protocols using velocity ai 3

Multimodal Imaging for Prostate Cancer

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17 prostate cancer patients who were treated with photon radiotherapy at a single academic center were randomly selected. Image data were extracted under an IRB-approved protocol. Routine treatment-planning pelvic CT and diagnostic MR scans were acquired. CT scans were acquired on a Siemens (Erlangen, Germany) SOMATOM Definition AS with a voxel size of 0.98 mm × 0.98 mm × 2 mm. T2-weighted MRIs were acquired on Siemens Avanto 1.5T scanner with a voxel size of 1 mm × 1 mm × 2 mm. The MR and CT were acquired at different time intervals, ranging from one day to two months. During the acquisition, the patients were in supine position and knee rest was used to minimize rotation of pelvis. The MR images were aligned to the corresponding CT images using the rigid registration method in Velocity AI 3.2.1 (Varian Medical Systems, Inc. Palo Alto, USA). The registered MR images were then resampled to obtain the same field-of-view and voxel size as the CT images. The resampled MR and CT pairs were used as the training dataset for our deep-learning-based algorithm.

Liu Y., Lei Y., Wang Y., Shafai-Erfani G., Wang T., Tian S., Patel P., Jani A.B., McDonald M., Curran W.J., Liu T., Zhou J, & Yang X. (2019). Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning. Physics in medicine and biology, 64(20), 205022.

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Multi-modal Image Registration and Preprocessing

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MRI data were first resampled to match the resolution of CT data. For each patient, all training MR and CT images were first rigidly registered by an intra-subject registration using Velocity AI 3.2.1 (Varian Medical Systems, Palo Alto, CA). Inter-patient registration of MR images consisted of a rigid registration followed by a B-spline deformable image registration. The transformation matrix obtained during this registration process was applied to the CT images to generate the deformable template CT. CT images were first segmented by a previously decribed thresholding method (Huang and Wang 1995 ). Segmentation holes were filled by morphological erosion with a 5 voxel spherical kernel to eliminate any objects that were not physically part of the patient. To improve the MR image quality, we performed denoising, inhomogeneity correction, and histogram matching for MRIs as recommended by our previous work (Lei et al 2018 ).

Lei Y., Harms J., Wang T., Tian S., Zhou J., Shu H.K., Zhong J., Mao H., Curran W.J., Liu T, & Yang X. (2019). MRI-based synthetic CT generation using semantic random forest with iterative refinement. Physics in medicine and biology, 64(8), 085001.

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Pelvic Radiation Therapy Imaging Protocol

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In this retrospective study, we collected the CT and MRI images of 49 patients who had received pelvic radiation therapy in our institution. MRIs were acquired using a Siemens standard T2-weighted MRI scanner with 3D T2-SPACE sequence and 1.0 × 1.0 × 2.0 mm³ voxel size (TR/TE: 1000/123 ms, flip angle: 95°). MRIs were acquired at 1.5 T. Built-in distortion corrections were applied during reconstruction. CTs were captured with a Siemens CT scanner with 1.0 × 1.0 × 2.0 mm³ voxel size with 120 kVp and 299 mAs. The prostate contours manually delineated by physician on MRI images were also collected for our study. For each patient, all training MR and CT images were deformably registered with an intra-subject strategy using a commercial software, Velocity AI 3.2.1 (Varian Medical Systems, Palo Alto, CA).

Lei Y., Dong X., Tian Z., Liu Y., Tian S., Wang T., Jiang X., Patel P., Jani A.B., Mao H., Curran W.J., Liu T, & Yang X. (2019). CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network. Medical physics, 47(2), 530-540.

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Prostate Cancer MRI-TRUS Fusion for Brachytherapy

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Experiments were conducted on a dataset of 36 pairs of T2-weighted MR and TRUS images collected from 36 prostate cancer patients who have been treated with HDR brachytherapy. TRUS data were acquired with a Hitachi HI VISION with a voxel size of 0.12 × 0.12 × 2.0 mm³. The T2-weighted MR images were obtained using a Siemens Avanto 1.5 T scanner (Spin-echo sequence with a repetition time/echo time of: 1200 ms/123 ms, flip angle 150°, voxel size 1 × 1 × 1 cm³ with each slice of 256 × 256 pixels, and pixel bandwidth 651 Hz), and then resampled to the same sizes and resolutions as those of the TRUS images. Both the original MR and TRUS images were reconstructed into a 3D volume and resampled to 0.5 × 0.5 × 0.5 mm³ isotropic voxels by a third order spline interpolation. The manual prostate labels of the TRUS and MRI, represented by binary masks, were contoured by one and three radiologists, respectively, using VelocityAI 3.2.1 (Varian Medical Systems, Palo Alto, CA). In addition, the TRUS and MR labels were resampled to 0.5 × 0.5 × 0.5 mm³.
Our proposed methods were implemented in TensorFlow with a 3D image augmentation layer from an open-source code in NiftyNet (Gibson et al 2018 ). The augmentation generated 300 times more training datasets. Each network was trained with a 12 GB NVIDIA Quadro TITAN Linux general-purpose graphic process unit.

Zeng Q., Fu Y., Tian Z., Lei Y., Zhang Y., Wang T., Mao H., Liu T., Curran W.J., Jani A.B., Patel P, & Yang X. (2020). Label-driven magnetic resonance imaging (MRI)-transrectal ultrasound (TRUS) registration using weakly supervised learning for MRI-guided prostate radiotherapy. Physics in medicine and biology, 65(13), 135002.

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Multimodal Brain and Pelvis Imaging for Radiation Therapy

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We retrospectively analyzed the MRI and CT data acquired during
treatment planning for 24 brain patients and 20 pelvis patients who received
radiation therapy. For the brain images, standard T1-weighted MRI was captured
using a GE MRI scanner with Brain Volume Imaging sequence (BRAVO) and
1.0×1.0×1.4 mm³ voxel size (TR/TE: 950/13 ms, flip
angle: 90°) and CT was captured with a Siemens CT scanner with
1.0×1.0×1.0 mm³ voxel size with 120 kVp and 220 mAs.
For the pelvis images, MRI was acquired using a Siemens standard T2-weighted MRI
scanner with 3D T2-SPACE sequence and 1.0×1.0×2.0 mm³voxel size (TR/TE: 1000/123 ms, flip angle: 95°) and CT was captured with
a Siemens CT scanner with 1.0×1.0×2.0mm³ voxel size
with 120 kVp and 299 mAs. MRI data were first resampled to match the resolution
of CT data. For each patient, all training MRI and CT images were first rigidly
registered by an intra-subject registration using a commercial software Velocity
AI 3.2.1 (Varian Medical Systems, Palo Alto, CA).

Lei Y., Harms J., Wang T., Liu Y., Shu H., Jani A.B., Curran W.J., Mao H., Liu T, & Yang X. (2019). MRI‐only based synthetic CT generation using dense cycle consistent generative adversarial networks. Medical Physics, 46(8), 3565-3581.

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Retrospective Analysis of Prostate Radiotherapy

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In this retrospective study, we reviewed 140 patients with prostate malignancies treated with external beam radiation therapy in our clinic. All 140 patients underwent CT simulation and diagnostic MRIs. MRIs were fused with planning CT images to aid prostate delineation. The median prostate volume was 30.37 cc (range 8.39 −130.02 cc), based on 140 patients. Institutional review board approval was obtained, and this HIPAA-compliant retrospective analysis did not require informed consent.
The CT images were acquired by a Siemens SOMATOM Definition AS CT scanner at 120 kVp and 220 mAs with pixel size of 0.586 × 0.586 × 0.6mm³.The MRIs were acquired using a Siemens standard T2-weighted MRI scanner with 3D T2-SPACE sequence and 1.0×1.0×2.0 mm³ voxel size (TR/TE: 1000/123 ms, flip angle: 95°) The training MR and CT images were deformably registered using Velocity AI 3.2.1 (Varian Medical Systems, Palo Alto, CA).

Dong X., Lei Y., Tian S., Wang T., Patel P., Curran W.J., Jani A.B., Liu T, & Yang X. (2019). Synthetic MRI-aided Multi-organ Segmentation on Male Pelvic CT Using Cycle Consistent Deep Attention Network. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 141, 192-199.

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Prostate Cancer CBCT-MRI Radiotherapy Dataset

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We retrospectively collected data from 100 patients with prostate cancer treated with external beam radiotherapy. Each dataset includes a CT simulation, a diagnostic MRI, and at least one set of CBCTs during treatment. Varian On-Board Imager CBCT system, with imaging spacing of 0.908 × 0.908 × 2.0 mm³, was used for CBCT acquisition. A Siemens MRI scanner was used to acquire T2-weighted images, specifically a 3D T2-SPACE sequence with 1.0 × 1.0 × 2.0 mm³ voxel size (TR/TE: 1000/123 ms, flip angle: 95°). Institutional review board approval was obtained; no informed consent was required for this HIPAA-compliant retrospective analysis. Ground truth contours were needed for the segmentation network training. The five pelvic organs were first manually contoured on MRIs by physicians. The contours were then propagated to CBCT using image registration software, Velocity AI 3.2.1 (Varian Medical Systems, Palo Alto, CA, USA). The propagated contours were then modified and approved by the physicians to obtain the ground truth for training and testing.

Fu Y., Lei Y., Wang T., Tian S., Patel P., Jani A.B., Curran W.J., Liu T, & Yang X. (2020). Pelvic multi-organ segmentation on cone-beam CT for prostate adaptive radiotherapy. Medical physics, 47(8), 3415-3422.

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Deformable MR-CT Image Registration

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First, the intensity inhomogeneity of the MR images was corrected by the N4ITK MRI Bias correction filter, available at the open source 3D SLICER 4.8.1. N4ITK was used with BSpline grid resolution of 10,10,10, and the other parameters were the default values. The MR images were then rigidly registered and deformed to match with the corresponding CT images using Velocity AI 3.2.1 (Varian Medical Systems, Inc. Palo Alto, USA). The option of MR corrected deformable was used as the algorithm to deform the MR images to the CT images. Resample was applied on the deformed MR images. Finally, the registered MR images and their CT pairs were uploaded our machine-learning algorithm to train.

Liu Y., Lei Y., Wang Y., Wang T., Ren L., Lin L., McDonald M., Curran W.J., Liu T., Zhou J, & Yang X. (2019). MRI-based Treatment Planning for Proton Radiotherapy: Dosimetric Validation of a Deep Learning-based Liver Synthetic CT Generation Method. Physics in medicine and biology, 64(14), 145015.

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Deformable MR-CT Image Registration

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Evaluating 4D VMAT Plan Quality

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To investigate the 4D VMAT plan quality, the 4D VMAT plans were compared with their corresponding 3D VMAT plans. It consisted of the following steps (Fig. 1). First, the 4D VMAT plan DICOM file was physically separated into 10 files corresponding to 10 phases based on the known correlation between the target position and the beam aperture of each control point. Second, the 10 sub‐files of the 4D VMAT plan were imported back to the TPS. The dose matrix was calculated on each phase of the 4D CT data set using the corresponding sub‐file of the 4D VMAT plan. Third, the dose matrices from the 10 phases were then deformed to the reference phase to generate a 4D dose matrix summation using the Varian VelocityAI 3.1.0 software. The differences between the deformable and the rigid registration for the QUASAR™ phantom 4D VMAT plans were also studied. The 4D dose matrix summation was imported back to Eclipse to calculate the dose distribution and DVHs for the target and OARs on the reference phase. Fourth, the dosimetric parameters of the 4D plan were compared with those of the ideal 3D VMAT plan using the coverage of planning target volume (PTV) and the sparing of organs‐at‐risk. The conformity indices (CI) were also calculated and compared. The CI was defined as:

C I = \frac{T V_{P I}}{P I} \times \frac{T V_{P I}}{T V},

Where TV_PI is the target volume within the prescribed isodose volume PI, TV is the target volume.

Zhang Y., Yang Y., Fu W., Li X., Li T., Heron D.E, & Huq M.S. (2017). 4D VMAT planning and verification technique for dynamic tracking using a direct aperture deformation (DAD) method. Journal of Applied Clinical Medical Physics, 18(2), 50-61.

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