Log In Sign up
The largest database of trusted experimental protocols

Velocity ai

Manufactured by Agilent Technologies
Visit Supplier
Sourced in United States

Velocity AI is a high-performance liquid chromatography (HPLC) system designed for analytical and preparative applications. It features advanced automation and intelligent software to enhance productivity and reproducibility in the laboratory.

Automatically generated - may contain errors

Lab products found in correlation

Biograph 40, by Siemens (1 mentions)

Close spelling variants of this product

Velocity AI 3.2.1 Velocity AI v.3.01 Velocity AI software

17 protocols using velocity ai

1

Quantitative Contour Evaluation Metrics

Check if the same lab product or an alternative is used in the 5 most similar protocols
Presession and postsession contours were compared with the gold standard contour using three metrics: the dice similarity coefficient (DSC), the Hausdorff distance (HD), and the mean distance [9 (link)–12 (link)]. DSC = 1 indicates a perfect overlap, and DSC = 0 denotes no overlap. In the literature, a DSC > 0.7 is commonly reported to indicate excellent agreement [13 ]. In addition, the contoured volume, volumetric difference (volumetric difference = (Vstudent pre/post − Vgold standard)/Vgold standard) [12 (link)], and slice number were compared. Volumetric data were collected using VelocityAI, v.3.01 commercial software (Varian Medical Systems, Atlanta, Georgia).
Gunther J.R., Liauw S.L., Choi S., Mohamed A.S., Thaker N.G., Fuller C.D., Stepaniak C.J., Das P, & Golden D.W. (2016). A Prostate Fossa Contouring Instructional Module: Implementation and Evaluation. Journal of the American College of Radiology : JACR, 13(7), 835-841.e1.
+ Open protocol
+ Expand
2

Accurate Respiratory Motion Tracking for Abdominal Tumors

Check if the same lab product or an alternative is used in the 5 most similar protocols
For abdominal tumors that move (e.g., liver, pancreas), a T2‐weighted navigator‐triggered imaging protocol is available on the Marlin. The system uses a navigator‐triggered method to acquire the 3D images at the end‐exhalation breathing phase. The accuracy of the T2‐weighted triggered imaging sequence acquired at the end‐expiration phase was assessed using the Quasar MRI 4D motion phantom (Modus Medical Devices, London ON). The MRI 4D phantom contains separate compartments including a spherical target within a moving cylinder. The image of the spherical phantom target was automatically contoured in Velocity AI (Varian Medical Systems, Palo Alto CA) with a constant window/ level of 2000/ 1000 and a contouring threshold of 1050. Accuracy of the triggered images was determined for a 20 mm periodic sinusoidal motion at breathing rates of 10, 15, and 20 breaths per minute (bpm), and for an irregular breathing pattern where throughout the acquisition the breathing amplitude was varied between 18.5 and 21.4 mm, the breathing rate was varied between 10 and 20 bpm, and the form of the wave function was varied among sin,2 sin,4 and sin.6 The contoured target centroid positions together with the contoured volumes for the tests with motion were compared with the centroid position and volume of the stationary target.
Snyder J.E., St‐Aubin J., Yaddanapudi S., Boczkowski A., Dunkerley D.A., Graves S.A, & Hyer D.E. (2020). Commissioning of a 1.5T Elekta Unity MR‐linac: A single institution experience. Journal of Applied Clinical Medical Physics, 21(7), 160-172.
+ Open protocol
+ Expand
3

CBCT-based Daily Dose Calculation for Radiotherapy

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
For all 10 patients, CBCTs of all fractions were available, in total 240 images. Accurate CBCT-based dose calculation is difficult with the Elekta Synergy CBCT system used in this study because the Hounsfield Units (HU) numbers are not accurate. To enable daily dose distribution calculation for all images, CT HU numbers were mapped to CBCT images by registering the planning CT to CBCT images using DIR (Figure 2) [14 (link)]. For each pre-fraction CBCT image, the planning CT with accurate HU numbers was deformed to represent CBCT images using a B-spline deformable image registration based on intensity values (VelocityAI, version 3.1.0/3.2.0, Varian Medical Systems, Inc., Palo Alto, CA). Of the two available planning CT’s (i.e., full and empty bladder), we selected the CT with the closest bladder volume to the daily anatomy of the CBCT for this step. Prior to the deformable image registration, a rigid registration was performed to match the bony anatomy. The deformable match was visually assessed to ensure that the body contours and the soft tissue matched sufficiently. The quality of CT-to-CBCT deformable registration in the pelvic area using the VelocityAI software was investigated previously and DIR results were reported to be accurate for dose calculation [15 (link),16 ].
Each daily selected plan was used to calculate the corresponding daily dose distribution.
van Heerden L.E., Visser J., Koedooder K., Rasch C.R., Pieters B.R, & Bel A. (2018). Role of deformable image registration for delivered dose accumulation of adaptive external beam radiation therapy and brachytherapy in cervical cancer. Journal of Contemporary Brachytherapy, 10(6), 542-550.
+ Open protocol
+ Expand
4

Lesion Delineation on Multi-Modal MRI

Check if the same lab product or an alternative is used in the 5 most similar protocols
Each lesion was delineated on each type of MRI sequence (T1, T1c, T2, and FLAIR) for each patient by using the VelocityAI software (version 3.0.1; Varian Medical Systems, Atlanta, GA). A radiation oncologist contoured the regions of interest manually on the T1c images because the lesions were easier to identify after contrast injection than on the other scan types. The T1c contour was then rigidly mapped to the other scan sequences (T1, T2, and FLAIR) for each patient at each time point by using the Mattes mutual information metric [14 (link)] in the Velocity AI. The radiation oncologist then reviewed the contours on the T1, T2, and FLAIR scans to ensure correct mapping and modified them if necessary.
Zhang Z., Yang J., Ho A., Jiang W., Logan J., Wang X., Brown P.D., McGovern S.L., Guha-Thakurta N., Ferguson S.D., Fave X., Zhang L., Mackin D., Court L.E, & Li J. (2017). A Predictive Model for Distinguishing Radiation Necrosis from Tumor Progression after Gamma Knife Radiosurgery based on Radiomic Features from MR Images. European radiology, 28(6), 2255-2263.
+ Open protocol
+ Expand
5

4D-CT-based Patient Motion Modeling

Check if the same lab product or an alternative is used in the 5 most similar protocols
As illustrated in Figure 1, patient motion model is generated from 10-phase planning 4D-CT images. One phase of the planning 4D-CT (e.g., end of exhalation phase) is defined as the “prior.” The other nine phases are deformed to the prior using Velocity AI (Varian Medical Systems, Palo Alto, USA) to generate nine 3D deformation vector fields (DVFs) which describe patient respiratory deformations. Principal component analysis (PCA) is then used to extract the principal components {PCi} of the deformation fields, which represent the major deformation patterns of the patient. Patient respiratory deformation at any instant can be represented as a linear combination of the principal components. In this study, we used the first three principal components corresponding to the three largest eigenvalues as they were proven to be sufficient in depicting lung motion:27 (link)–29 (link)Where, D is the deformation field vector at any instant, Davg is the average of the nine deformation fields initially extracted from 4D-CT, PCi is the ith principal component, and wi represents the corresponding coefficients for the principal components.
Zhang L., Zhang Y., Zhang Y., Harris W.B., Yin F.F., Cai J, & Ren L. (2017). Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study. Cancer translational medicine, 3(6), 185-193.
+ Open protocol
+ Expand
6

Comparing NSCLC Tumor Delineation Using PET-CT

Check if the same lab product or an alternative is used in the 5 most similar protocols
A total of 60 NSCLC patients who received stereotactic body radiation therapy (SBRT) were analyzed in this study following institutional review board (IRB) approval. All patients had PET-CT images for simulation and received follow-up CT images between 2 and 4 months after radiotherapy treatment. Fluorine 18-fluorodeoxyglucose (18F-FDG) PET and CT images were obtained using a dual PET/CT scanner (Siemens Biograph 40, Siemens Medical Solutions, Erlangen, Germany). All patients were injected with 370 BMq ± 10% of 18F-FDG with an uptake time of 90 min ± 10%. In all cases, subjects fasted for more than 4 h and had a blood glucose of less than 200 mg/dl. The gross tumor volume (GTV) for each of the PET and CT image datasets was separately delineated by three radiation oncologists on both CT and 18F-FDG PET images, with the guidance of the corresponding images in the other modality. All contouring was completed using VelocityAI (Varian Medical System, Inc., Palo Alto, CA). In this study, while physicians referred to the other modality to define the tumor contours on either PET or CT, they did not visualize the corresponding PET and CT scans at the same time using the software’s fusion feature. The reference standard for each scan was then generated by applying the STAPLE algorithm42 (link) to the three manual delineations.
Zhong Z., Kim Y., Plichta K., Allen B.G., Zhou L., Buatti J, & Wu X. (2019). Simultaneous cosegmentation of tumors in PET‐CT images using deep fully convolutional networks. Medical Physics, 46(2), 619-633.
+ Open protocol
+ Expand
7

Comparing CT and MRI Contours in Brachytherapy

Cited 1 time
Check if the same lab product or an alternative is used in the 5 most similar protocols
The images and completed contours were exported as DICOM files from Oncentra to Velocity AI (version 3.0; Varian Medical Systems, Palo Alto, California). The BT-CT and BT~ MRI images were then co-registered strictly on the basis of the position of the applicator, in accordance with the assumption that the HR-CTV is fixed in relation to the applicator. Absolute volumes (in cubic centimeters) were obtained for each HR-CTV contour and the absolute discrepancy between the volumes was calculated by subtracting the MRI HR-CTV volume from the CT HR-CTV volume. The CT HR-CTV and MRI HR-CTV were then superimposed on one another, and the Dice coefficient of similarity (DC) was calculated using the formula
DC(A,B)=2|A∩B||A|+|B| , where A represents the CT HR-CTV contour, B represents the MRI HR-CTV contour, and ∩ represents the overlap of the two contours. DC has been used in previous contouring studies to quantify the extent of overlap of two volumes (13 (link), 17 , 18 (link)); DC=1 represents perfect overlap, while DC=0 represents no overlap.
Swanick C.W., Castle K.O., Vedam S., Munsell M.F., Turner L.M., Rauch G.M., Jhingran A., Eifel P.J, & Klopp A.H. (2016). Comparison of CT- and MRI-Based Clinical Target Volume Contours at the Time of Brachytherapy for Cervical Cancer. International journal of radiation oncology, biology, physics, 96(4), 793-800.
+ Open protocol
+ Expand
8

Multimodal Image Registration for Radiotherapy

Check if the same lab product or an alternative is used in the 5 most similar protocols
Postimplant CT images were performed on GE medical systems, with original volumetric dimensions of 512 × 512 × 220, voxel spacing of 0.5 × 0.5 × 1.25 mm3, kVp of 120, and mAs of 300. Preimplant T2-weighted MR images were acquired on GE medical systems, with original volumetric dimensions of 256 × 256 × 42 and a voxel spacing of 1.25 × 1.25 × 5 mm3, TP of (2316–5422) ms, echo time of (109–114) ms, percent phase field of view of 100, and flip angle of 180°.
Digital Imaging and Communications in Medicine (DICOM) files containing CT and MR images were exported to VelocityAI (Varian, Palo Alto, CA). The MR scans were then rigidly registered to the CT coordinates using the femur heads, sacrum, and coccyx as landmarks. Figure 1 compares the location and volume of preimplant MR-based HR-CTVMR and postimplant CT-based HR-CTVCT for an example patient. The voxel intensities of CT and MR images were normalized to be between 0 and 1. To manage the data size in training, we resampled the registered CT and MR images to 128 × 128 × 80 voxels by nearest-neighbor interpolation. All images have a final spatial resolution of 0.5 × 0.5 × 1.25 mm3. The model training and testing were performed using a graphics processing unit (GPU) workstation equipped with 4x RTX 2080 Ti and a total of 44 Gigabyte (GB) graphic memory.
Cao Y., Vassantachart A., Ragab O., Bian S., Mitra P., Xu Z., Gallogly A.Z., Cui J., Shen Z.L., Balik S., Gribble M., Chang E.L., Fan Z, & Yang W. (2022). Automatic segmentation of high-risk clinical target volume for tandem-and-ovoids brachytherapy patients using an asymmetric dual-path convolutional neural network. Medical physics, 49(3), 1712-1722.
+ Open protocol
+ Expand
9

4D-CT-based Patient Motion Modeling

Check if the same lab product or an alternative is used in the 5 most similar protocols
As illustrated in Figure 1, patient motion model is generated from 10-phase planning 4D-CT images. One phase of the planning 4D-CT (e.g., end of exhalation phase) is defined as the “prior.” The other nine phases are deformed to the prior using Velocity AI (Varian Medical Systems, Palo Alto, USA) to generate nine 3D deformation vector fields (DVFs) which describe patient respiratory deformations. Principal component analysis (PCA) is then used to extract the principal components {PCi} of the deformation fields, which represent the major deformation patterns of the patient. Patient respiratory deformation at any instant can be represented as a linear combination of the principal components. In this study, we used the first three principal components corresponding to the three largest eigenvalues as they were proven to be sufficient in depicting lung motion:27 (link)–29 (link)Where, D is the deformation field vector at any instant, Davg is the average of the nine deformation fields initially extracted from 4D-CT, PCi is the ith principal component, and wi represents the corresponding coefficients for the principal components.
Zhang L., Zhang Y., Zhang Y., Harris W.B., Yin F.F., Cai J, & Ren L. (2017). Markerless Four-Dimensional-Cone Beam Computed Tomography Projection-Phase Sorting Using Prior Knowledge and Patient Motion Modeling: A Feasibility Study. Cancer translational medicine, 3(6), 185-193.
+ Open protocol
+ Expand
10

Adaptive MRI-Guided SABR for Gastric Tumors

Check if the same lab product or an alternative is used in the 5 most similar protocols
Radiotherapy and Oncology j o u r n a l h o m e p a g e : w w w . t h e g r e e n j o u r n a l . c o m parameters as for the baseline plan, but with full re-optimization of dose fluences based on adjusted contours [8, (link)9] (link). Our daily plan reoptimization is also based on avoiding excessive doses to OARs, by excluding parts of adjacent OARs from the PTV, using an 'optimized PTV' for dose planning each fraction. OAR dose constraints used for the stomach within 2 and 3 cm of the PTV, were D0.1 cc 36 Gy and D1.0 cc 33 Gy, respectively. Treatment delivery is performed with visual feedback provided to patients on an inroom monitor, which projects the GTV and a gating window boundary in real-time. After SABR delivery, a repeat 3D MR scan was acquired, except for patients undergoing their final fraction.
This retrospective offline analysis was approved by the institutional ethics committee. All MR-scans, dose plans and planning contours were imported into VelocityAI (Varian Medical Systems, Palo, Alto, CA). Changes in on-table stomach anatomy were studied on 3D-MR scans acquired in 70 pre-and post-treatment datasets derived from twenty patients, and pre-treatment scans were compared to post-treatment breath-hold scans. One patient was treated for bilateral adrenal metastases.
van Sörnsen de Koste J.R., Palacios M.A., Chen H., Schneiders F.L., Bruynzeel A.M.E., Slotman B.J., Haasbeek C.J.A, & Senan S. (2020). Changes in gastric anatomy after delivery of breath-hold MR-guided SABR for adrenal metastases. Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 152.
+ 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?

Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required

Sign up now

Revolutionizing how scientists
search and build protocols!