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CT protocol

CT protocols are essential tools for optimizing computed tomography (CT) imaging procedures.
These protocols provide step-by-step guidelines for conducting CT scans, including details on patient positioning, scan parameters, and image reconstruction.
By following standardized CT protocols, researchers and clinicians can ensure consistent, high-quality imaging results, enabling more accurate diagnoses and effective treatment planning.
Discover the power of PubCompare.ai, an AI-driven platform that helps you effortlessly locate and compare CT protocols from literature, pre-prints, and patents.
This cutting-edg optimization tool provides insightful comparisons to identify the optimal protocols and products for your needs, elevating your CT research to new heights.

Most cited protocols related to «CT protocol»

1
Chest CT Imaging for COVID-19 Diagnosis
An observer study was performed on a set of 105 randomly selected chest CT scans obtained in a group of consecutive patients presenting to the emergency ward between March 14th 2020 and March 25th 2020 with suspected SARS-CoV-2 infection, in whom RT-PCR was performed. Patient inclusion, CT protocol, and radiation parameters are described in Supplement 2. Medical ethics committee approval was obtained prior to the study. Informed consent was waived, and data collection and storage were carried out in accordance with local guidelines.
Patient characteristics (age, gender, comorbidities), clinical follow up, including a multidisciplinary clinical diagnosis, if applicable, and RT-PCR results were extracted from electronic patient records. These data allowed stratification of all patients into one of the following three groups: patients with at least one positive RT-PCR result for SARS-CoV-2 within five days after CT (PCR+), patients with one or multiple negative RT-PCR results but a clinical diagnosis of COVID-19 according to clinical records (PCR-/Clinical+), and patients with one or multiple negative RT-PCR results and a clinical course not consistent with COVID-19, or consistent with an alternative diagnosis (PCR-/Clinical-).
Prokop M., van Everdingen W., van Rees Vellinga T., Quarles van Ufford J., Stöger L., Beenen L., Geurts B., Gietema H., Krdzalic J., Schaefer-Prokop C., van Ginneken B, & Brink M. (2020). CO-RADS – A categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation. Radiology.
Publication 2020
Chest COVID 19 CT protocol Diagnosis Dietary Supplements Ethics Committees Gender Patients Radiotherapy Reverse Transcriptase Polymerase Chain Reaction SARS-CoV-2 X-Ray Computed Tomography
2
In Vivo PET/CT Imaging of Mice
Two normal mice were injected with 8.9 MBq 18FDG and 9.4 MBq 18F−, respectively. One and a half hours later the mice were centered inside the gantry and imaged for 1 hour. CT transmission scans of the mice were performed on a MicroCAT™ II tomograph with the same protocol as the CT scan for the image quality phantom. The PET data were reconstructed with FORE+2DFBP (with a ramp filter cutoff at the Nyquist frequency) and MAP reconstructions (β = 0.01) with all available corrections applied, including attenuation and scatter.
Bao Q., Newport D., Chen M., Stout D.B, & Chatziioannou A.F. (2009). Performance Evaluation of the Inveon Dedicated PET Preclinical Tomograph Based on the NEMA-NU4 Standards. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 50(3), 401-408.
Publication 2009
CT protocol F18, Fluorodeoxyglucose Mice, House Radionuclide Imaging Reconstructive Surgical Procedures Tomography Tomography, X-Ray
3
COVID-19 Chest CT and RT-PCR Retrospective Study
The institutional review board of all seven hospitals in Hunan Providence, China and Rhode Island Hospital from the U.S. approved this retrospective study and written informed consent was waived. A total of 256 patients with both positive COVID-19 by RT-PCR and chest CT imaging within two weeks were retrospectively identified from 7 Chinese hospitals in Hunan Providence, China from January 6 to February 20, 2020. The RT-PCR results were extracted from the patients’ electronic medical records in the hospital information system (HIS). The RT-PCR assays were performed by using TaqMan One-Step RT-PCR Kits from Shanghai Huirui Biotechnology Co., Ltd or Shanghai BioGerm Medical Biotechnology Co., Ltd, both of which have approved use by the China Food and Drug Administration (CFDA). For patients with multiple RT-PCR assays, positive result on the last performed test was adopted as confirmation of diagnosis. The number of cases included from each hospital is shown in Supplementary Table 1. Among these patients, 37 with negative chest CT were excluded, resulting in a final cohort of 219 patients. The chest CT protocols from the 7 hospitals are shown in Supplementary Table 2.
The radiology search engine MONTAGE (Nuance Communications, Burlington, MA) at Rhode Island Hospital in Providence, RI was used to identify cases that contain the word “pneumonia” in the impression section of the radiology CT reports from 2017 to 2019. The identified CT scans were directly downloaded from the hospital Picture Archiving and Communications Systems (PACS) and non-chest CTs were excluded. Positive results from Respiratory Pathogen Panel (RPP) were used to locate patients of possible viral pneumonia from 2017 to 2019. The tests of ePlex Respiratory Pathogen panel (GenMark Diagnostics, Carlsbad, CA) were performed in the Microbiology Lab of Rhode Island Hospital Pathology Department according to it manufacture protocol (13 ). A diagram illustrating the initial breakdown of RPP search results is shown in Supplementary Figure 1. The two lists were cross-matched to generate a final list that contained CT chest scans with the word “pneumonia” in the final impression and positive RPP test within 7 days of each other. Then, the impression sections of these CT reports were reviewed by a research assistant (BH) and a radiologist (HXB) board-certified in general diagnostic radiology and interventional radiology with one year of practice experience to identify 205 cases with final CT impression being “consistent with” or “highly suspicious for” pneumonia. Our final cohort consisted of 424 patients. A diagram illustrating patient inclusion and exclusion is shown in Figure 1. A diagram illustrating the final breakdown of RPP results is shown in Figure 2.
Bai H.X., Hsieh B., Xiong Z., Halsey K., Choi J.W., Tran T.M., Pan I., Shi L.B., Wang D.C., Mei J., Jiang X.L., Zeng Q.H., Egglin T.K., Hu P.F., Agarwal S., Xie F., Li S., Healey T., Atalay M.K, & Liao W.H. (2020). Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology.
Publication 2020
Biological Assay Catabolism Chest Chinese COVID 19 CT protocol Diagnosis Ethics Committees, Research pathogenesis Patients Pneumonia Pneumonia, Viral Radiologist Radionuclide Imaging Respiratory Function Tests Respiratory Rate Reverse Transcriptase Polymerase Chain Reaction X-Ray Computed Tomography X-Rays, Diagnostic
4
CT Imaging Features of COVID-19 Pneumonia
All the patients underwent thin-section CT. The median duration from illness onset to CT scan was 4 days, ranging from 1 to 14 days. All CT examinations were performed with a 64-section scanner (Scenaria 64 CT; Hitachi Medical, Kashiwa, Chiba Prefecture, Japan) without the use of contrast material. The CT protocol was as follows: tube voltage, 120 kV; automatic tube current (180 mA–400 mA); iterative reconstruction technique; detector, 64 mm; rotation time, 0.35 second; section thickness, 5 mm; collimation, 0.625 mm; pitch, 1.5; matrix, 512 × 512; and breath hold at full inspiration. Reconstruction kernel used was lung smooth with a thickness of 1 mm and an interval of 0.8 mm. The following windows were used: a mediastinal window with a window width of 350 HU and a window level of 40 HU, and a lung window with a width of 1200 HU and a level of −600 HU.
Three chest radiologists (F.Song, N.S., and Y.S., with approximately 6–32 years of experience in thoracic imaging, especially in the setting of viral pneumonias such as H1N1 and H7N9 pneumonia) reviewed the images independently, with a final finding reached by consensus when there was a discrepancy.
CT images were assessed for the presence and distribution of parenchymal abnormalities including pure ground-glass opacity (GGO), which were defined as a hazy increase in lung attenuation with no obscuration of the underlying vessels; GGO with interlobular septal thickening or reticulation, or intralobular networks in GGO; GGO with consolidation, which was defined as an area of opacification obscuring the underlying vessels in GGO; consolidation; air bronchogram(s); reticulation; lymphadenopathy, which was defined as a lymph node greater than 1 cm in short-axis diameter; and pleural effusion. On the axial CT images, we drew a horizontal line across the axillary midline to divide anterior and posterior parts of the lungs. The outer one-third of the lung was defined as peripheral, and the rest was defined as central.
Chest CT lesions in each patient were identified by the readers. A lesion occupying only one lung segment was counted as one lesion. When a large lesion or fused lesion involved more than one lung segment, the lesion number was recorded as the number of the involved lung segments. For example, a large lesion involving three lung segments was counted as three lesions. Each side of the chest containing pleural fluid was counted as one lesion. A pericardial effusion was counted as one lesion.
Song F., Shi N., Shan F., Zhang Z., Shen J., Lu H., Ling Y., Jiang Y, & Shi Y. (2020). Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology.
Publication 2020
Axilla Blood Vessel Bronchography Chest Congenital Abnormality Contrast Media CT protocol Effusion, Pericardial Epistropheus Influenza in Birds Inhalation Lung Lymphadenopathy Mediastinum Microtomy Nodes, Lymph Patients Physical Examination Pleura Pleural Effusion Pneumonia Pneumonia, Viral Radiologist Reconstructive Surgical Procedures Reticulum X-Ray Computed Tomography
5
Quantifying Myocardial Extracellular Volume
The DynEQ-CT protocol consisted of three steps (Fig. 1 for flow chart): first, a CT scan to obtain baseline pre-contrast blood and myocardial attenuation in Hounsfield units (HU); second, contrast administration and delay so the contrast distributes into a blood:myocardial dynamic equilibration; third, a repeat scan to re-measure blood and myocardial attenuation. The ratio of the change in blood and myocardial attenuation (ΔHU) represents the contrast agent partition coefficient. If the blood volume of distribution is substituted in (1 minus venous hematocrit; obtained prior to imaging), the myocardial extracellular volume, ECVCT, is obtained, reflecting the myocardial interstitium: ECVCT = (1−Hematocrit) × (ΔHUtissue/ΔHUblood).
CT examinations were performed on a 64–detector row CT scanner (Somatom Sensation 64; Siemens Medical Solutions, Erlangen, Germany). A topogram was used to plan CT volumes from the level of the aortic valve to the inferior aspect of the heart, typically a 10 cm slab. Cardiac scans (tube voltage, 120 kV; tube current–time product, 160 mAs; section collimation, 64 detector rows, 1.2-mm section thickness; gantry rotation time, 330 msec) were acquired with prospective gating (65%–75% of R-R interval), and reconstructed into 3-mm-thick axial sections with a B20f kernel.
To establish the best timing, post contrast imaging was performed at both 5- and 15-minutes following a bolus of Iodixanol (652 mg/mL) at a standard dose of 1 mL/kg and injection rate of 3 ml/sec without a saline chaser. An additional single 3 mm slice acquisition at 1-minute (other parameters as previously described) was introduced in the amyloid cohort to aid blood:myocardial boundary detection for segmentation of the myocardium during analysis.
CT image analysis was performed using a free and open-source Digital Imaging and Communications in Medicine viewer (OsiriX v4.1.2; Pixmeo, Bernex, Switzerland) independently by two experienced readers blinded to all other study data; this was repeated by the second reader to establish inter- and intra-observer agreement. Regions of interest (ROIs) were drawn in the contrast-enhanced 1-minute acquisition in axial sections and propagated to the pre-contrast, 5-minute and 15-minute acquisitions. For myocardium, polygonal ROIs were drawn in an axial slice containing the greatest area of myocardial septum; for the blood pool, circular ROIs were drawn in the LV blood pool away from papillary muscles and the myocardial septum to avoid the endocardial edge and therefore partial voluming (Fig. 2). Myocardial and blood attenuation values were used to calculate the ECV fraction as described.
Signal-to-noise ratios (SNR) were measured in five myocardial ROIs per time point from the ratio of the average HU attenuation value to the standard deviation of the HU attenuation. Radiation exposure was quantified using the dose-length product multiplied by a chest conversion coefficient (κ = 0.014 mSv/mGy cm).20 (link)
Treibel T.A., Bandula S., Fontana M., White S.K., Gilbertson J.A., Herrey A.S., Gillmore J.D., Punwani S., Hawkins P.N., Taylor S.A, & Moon J.C. (2015). Extracellular volume quantification by dynamic equilibrium cardiac computed tomography in cardiac amyloidosis. Journal of Cardiovascular Computed Tomography, 9(6), 585-592.
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Publication 2015
Amyloid Proteins BLOOD Blood Volume CAT SCANNERS X RAY Chest Cone-Beam Computed Tomography CT protocol Endocardium Fingers Heart iodixanol Myocardium Papillary Muscles Pharmaceutical Preparations Physical Examination Radiation Exposure Radionuclide Imaging Saline Solution Septal Area Valves, Aortic Veins Volumes, Packed Erythrocyte X-Ray Computed Tomography

Most recents protocols related to «CT protocol»

1
Standardized Imaging Protocol for Thoracic CT
All the imaging was obtained as described per standard imaging protocol.
CT imaging: A high-resolution CT of the thorax was usually obtained volumetrically and reformatted in 3 planes (sagittal, axial, and coronal). CT equipment included the following: Somatom GO now 32-slice (Dist. Wenlock Hospital); BRIGHT SPEED Elite 16-slice MDCT GE medical systems (KMC Ambedkar); CT BRIVO 385-EXCITE 16-slice – GE (KMC Attavar).
All CT imaging was interpreted by the institutions’ own radiologists. Furthermore, all the images with the patients’ clinical details after anonymization were collected and reviewed for data analysis purposes.
Ullal S, & Goyal A. (2023). Post-COVID-19 sequelae in lungs: retrospective computed tomography analysis in selected tertiary care hospitals of Mangalore. Polish Journal of Radiology, 88, e98-e102.
Publication 2023
Chest CT protocol Multidetector Computed Tomography Patients Radiologist
2
Chest CT Scan Protocol
The Chest CT scan protocol: All patients underwent chest CT plain scan after routine chest X-ray examination. Hitachi CT(ECLOS) 16-slice CT machine was used for CT examination, and the parameters of the instrument scan examination were as follows: Current control is 50Ma, voltage control is 100Kv, layer spacing is 5mm, pitch is 1.25mm, layer spacing and reconstruction layer thickness is 5mm, scanning time is 1.53s.
Liu C., Ma M., Zhou X., Zhang Z, & Guo Y. (2023). Multivariate analysis of prognostic factors in patients with lung cancer. Frontiers in Oncology, 13, 1022862.
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Publication 2023
Chest CT protocol Patients Reconstructive Surgical Procedures X-Ray Computed Tomography
3
Optimized CT Imaging Protocols for Musculoskeletal Examinations
Scans were performed using a Canon 320-detector-row CT scanner (Aquilion One Vision; Canon Medical Systems, Otawara, Japan). For the shoulder, knee, ankle, and wrist, SD-CT scanning parameters were 120 kV tube voltage and 150, 120, 120, and 50 mA tube current, respectively; ULD-CT scanning parameters were 80 kV tube voltage and 52, 11, 11, and 4 mA tube current, respectively; scan range was 160 mm, 140 mm, 140 mm, and 100 mm, respectively. Scan slice thickness was 0.5–1 mm. CTDIvol (mGy) and DLP (mGy*cm) were automatically implemented for all CT-protocols by the scanner software.
Effective dose (ED = DLP*k) for each patient was calculated by multiplying DLP by k (a conversion coefficient): shoulder k = 0.0113 (SD-CT) k = 0.0091 (ULD-CT); knee k = 0.0004 (SD-CT and ULD-CT); ankle and wrist k = 0.0002 (SD-CT and ULD-CT) [12 ]
Post-processing was performed on a dedicated workstation (VitreaFX3.0). Image reconstruction involved multiplanar reformatting (MPR), volume rendering (VR), and maximum intensity projection (MIP).
Xiao M., Zhang M., Lei M., Lin F., Chen Y., Chen J., Liu J, & Ye J. (2023). Diagnostic accuracy of ultra-low-dose CT compared to standard-dose CT for identification of non-displaced fractures of the shoulder, knee, ankle, and wrist. Insights into Imaging, 14, 40.
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Publication 2023
Ankle CAT SCANNERS X RAY CT protocol Knee Patients Radionuclide Imaging Shoulder Vision Wrist Joint
4
Robotic Gastrectomy for Gastric Cancer
This study was designed as a prospective single-arm observational study allowing for comparison with the control group after propensity-score matching. We enrolled gastric cancer patients who were scheduled for robotic surgery for distal subtotal gastrectomy. We included patients aged 18 years or older who had an abdominopelvic CT according to the established protocol. We excluded patients whose major vascular structures around the stomach had been altered due to previous surgery and those with history of any gastric surgery. We also excluded patients who could not have a CT scan due to contrast agent allergy, creatinine level above 1.5 times the normal maximum, and claustrophobia. To perform robotic subtotal gastrectomy using surgical navigation in 30 patients, this clinical trial was designed to enroll 36 patients considering 20% drop out and exclusion of enrolled participants during 6 months enrollment period.
The control group was selected among 175 patients who took CT angiography with an established protocol capable of 3-D model reconstruction between September 2014 and September 2021 from the prospectively collected gastric cancer database. We used the same eligibility criteria for the control and the experimental group. After excluding 28 patients who underwent total gastrectomy or proximal gastrectomy, 147 gastric cancer patients underwent robotic distal gastrectomy. Of these 147 patients, we used propensity-score matching for control group selection to balance the two groups for different clinical and surgical features. A control group was selected using 1:1 propensity-score matching with covariates of patient demographics (age, sex, body mass index) and operative factors (extent of lymph node dissection and reconstruction type). We set the caliper value of 0.1 for 1:1 matching using the nearest method without replacement.
This study was approved by the Institutional Review Board (IRB) of Severance Hospital, Yonsei University Health System (1-2021-0036). Written informed consent was obtained from patients after a full explanation of the study. Informed consent for patients included in the control group was waived by the IRB because of its retrospective nature.
Park S.H., Kim K.Y., Kim Y.M, & Hyung W.J. (2023). Patient-specific virtual three-dimensional surgical navigation for gastric cancer surgery: A prospective study for preoperative planning and intraoperative guidance. Frontiers in Oncology, 13, 1140175.
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Publication 2023
Billroth I Procedure Blood Vessel Claustrophobia Computed Tomography Angiography Contrast Media Creatinine CT protocol Eligibility Determination Ethics Committees, Research Gastrectomy Gastric Cancer Hypersensitivity Index, Body Mass Lymph Node Excision Operative Surgical Procedures Patients Reconstructive Surgical Procedures Robotic Surgical Procedures Stomach Surgical Navigation X-Ray Computed Tomography
5
Tumor Volume Quantification from CT Scans
All patients underwent a CT scan within 2 months before the immunotherapy treatment start date. When available, follow-up CT scans were acquired within 4 months after treatment (up to three temporal time points per patient). All CT images were acquired after contrast injection during a patient inspiratory breath hold, following the contrast-enhanced CT chest protocol. CT scans were reconstructed using a standard kernel. A description of CT parameters is available in Additional file 1: Table S3.
For each case, the primary tumor was selected as the target lesion. 3D tumors were identified and segmented by an experienced radiologist on the baseline and follow-up CT images using either the syngo.via Siemens Healthineers software or 3D Slicer [23 (link)]. The largest lesion was considered if a patient had an ambiguous primary tumor. Follow-up CT scans were discarded if the tumor found in the baseline CT scan was no longer visible.
For pre-processing, Hounsfield units of all CT images were clipped between -1000 and 3050, and z-score normalization was then applied.
Farina B., Guerra A.D., Bermejo-Peláez D., Miras C.P., Peral A.A., Madueño G.G., Jaime J.C., Vilalta-Lacarra A., Pérez J.R., Muñoz-Barrutia A., Peces-Barba G.R., Maceiras L.S., Gil-Bazo I., Gómez M.D, & Ledesma-Carbayo M.J. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21, 174.
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Publication 2023
Chest CT protocol Immunotherapy Inhalation Neoplasms Patients Radiologist X-Ray Computed Tomography

Top products related to «CT protocol»

1
Somatom definition flash by Siemens
Sourced in Germany, United States, Japan, Netherlands, United Kingdom
The SOMATOM Definition Flash is a computed tomography (CT) scanner developed by Siemens. It is designed to provide high-quality imaging for a wide range of medical applications. The SOMATOM Definition Flash utilizes advanced technology to capture detailed images of the body, enabling medical professionals to make accurate diagnoses and inform treatment decisions.
2
Somatom definition as by Siemens
Sourced in Germany, United States, Japan, Netherlands, United Kingdom, China
The SOMATOM Definition AS is a computed tomography (CT) imaging system manufactured by Siemens. It is designed to provide high-quality medical imaging for diagnostic purposes. The core function of the SOMATOM Definition AS is to generate detailed cross-sectional images of the human body using X-ray technology.
3
Somatom force by Siemens
Sourced in Germany, United States, Japan
The SOMATOM Force is a high-performance computed tomography (CT) system developed by Siemens. It is designed to deliver fast, precise, and efficient imaging capabilities for a wide range of clinical applications. The SOMATOM Force features advanced technologies that enable high-quality imaging while minimizing radiation exposure.
4
Lightspeed vct by GE Healthcare
Sourced in United States, Germany, Japan, United Kingdom, Netherlands
The LightSpeed VCT is a computed tomography (CT) imaging system produced by GE Healthcare. It is designed to provide high-quality, high-speed imaging for a variety of medical applications. The LightSpeed VCT features a multi-slice detector array that enables rapid data acquisition and reconstruction, allowing for efficient patient scanning.
5
Aquilion one by Toshiba
Sourced in Japan, Germany, United States
The Aquilion ONE is a computed tomography (CT) scanner developed by Toshiba. It is capable of performing whole-body scans in a single rotation, allowing for faster and more comprehensive imaging. The Aquilion ONE utilizes advanced technology to capture high-quality images, but a detailed description of its core function is not available without extrapolation or interpretation.
6
Lightspeed 16 by GE Healthcare
Sourced in United States, Germany, Italy
The LightSpeed 16 is a computed tomography (CT) imaging system developed by GE Healthcare. It is designed to capture high-quality, detailed images of the body's internal structures. The LightSpeed 16 utilizes advanced imaging technology to provide efficient and reliable diagnostic capabilities for healthcare professionals.
7
Discovery ct750 hd by GE Healthcare
Sourced in United States, Germany, Japan, China, United Kingdom
The Discovery CT750 HD is a computed tomography (CT) scanner developed by GE Healthcare. It is designed to provide high-quality medical imaging for a variety of clinical applications. The core function of this product is to generate detailed cross-sectional images of the body using advanced X-ray technology.
8
Somatom definition by Siemens
Sourced in Germany, United States, Japan, Netherlands
The Somatom Definition is a computed tomography (CT) scanner developed by Siemens. It is a diagnostic imaging device that uses X-rays to create detailed cross-sectional images of the body.
9
Brilliance 64 by Philips
Sourced in United States, Netherlands, Germany, Japan, United Kingdom
The Philips Brilliance 64 is a computed tomography (CT) imaging system designed for medical diagnostic purposes. It features a 64-slice detector configuration, enabling rapid data acquisition and high-resolution imaging. The Brilliance 64 provides detailed anatomical information to support clinical decision-making for a variety of medical applications.
10
Ultravist 370 by Bayer
Sourced in Germany, United States, Italy, United Kingdom, Switzerland
Ultravist 370 is a non-ionic, water-soluble contrast medium used for radiographic examinations. It contains the active ingredient iopromide and has a concentration of 370 mg iodine per milliliter.

More about "CT protocol"

Computed Tomography (CT) Imaging Protocols: Optimizing Diagnostic Accuracy and Treatment Planning Computed tomography (CT) imaging has become an indispensable tool in modern healthcare, enabling healthcare professionals to diagnose and monitor a wide range of medical conditions.
At the heart of effective CT imaging are standardized protocols, which provide step-by-step guidelines for conducting CT scans, including details on patient positioning, scan parameters, and image reconstruction.
These CT protocols are essential for ensuring consistent, high-quality imaging results, enabling more accurate diagnoses and effective treatment planning.
By following established protocols, researchers and clinicians can minimize variability and optimize the imaging process, ultimately leading to better patient outcomes.
Discover the power of PubCompare.ai, an AI-driven platform that helps you effortlessly locate and compare CT imaging protocols from literature, pre-prints, and patents.
This cutting-edge optimization tool provides insightful comparisons to identify the optimal protocols and products for your needs, elevating your CT research to new heights.
Explore a wide range of CT imaging systems, including the SOMATOM Definition Flash, SOMATOM Definition AS, SOMATOM Force, LightSpeed VCT, Aquilion ONE, LightSpeed 16, Discovery CT750 HD, Somatom Definition, Brilliance 64, and Ultravist 370.
These advanced systems, combined with standardized CT protocols, can help you achieve the best possible imaging results for your research or clinical practice.
Take your CT imaging to the next level with PubCompare.ai and the power of standardized protocols.
Elevate your diagnostic accuracy, optimize treatment planning, and deliver better patient outcomes with this cutting-edge optimization tool.