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Positron-Emission Tomography

Positron-Emission Tomography (PET) is a non-invasive imaging technique that uses positron-emitting radiotracers to visualize and measure biochemical processes in the body.
PET scans provide valuable information about organ and tissue function, metabolism, and the presence of disease.
This technology has revolutionized medical research and clinical practice, enabling eariler diagnosis, more accurate disease staging, and personalized treatment planning.
PET imaging is widely used in oncology, neurology, cardiology, and other fields to enhance understanding of physiological processes and improve patient outcomes.
Reasearchers and clinicians can leveraage the power of PET to advance scientific knowledge and deliver high-quality, reproducible results.

Most cited protocols related to «Positron-Emission Tomography»

1
Consensus Criteria for PET-Based Treatment Response
Selected articles obtained using Internet search tools, including PubMed and syllabi from meetings (e.g., Clinical PET and PET/CT syllabus, Radiological Society of North America, 2007), were identified. Publications resulting from database searches and including the main search terms RECIST, positron, FDG, ROI (region of interest), cancer, lymphoma, PET, WHO, and treatment response were included. The search strategy for relevant 18F-FDG PET studies articulated by Mijnhout et al. was also applied (34 (link),35 (link)). These were augmented by key references from those studies, as well as the authors' own experience with PET assessments of treatment response, informal discussions with experts on PET treatment response assessment, and pilot evaluations of clinical data from the authors' clinical practice. Limitations and strengths of the anatomic and functional methods to assess treatment response were evaluated with special attention to studies that had applied qualitative or quantitative imaging metrics, had determined the precision of the method, and had histologic correlate or outcome data available. On the basis of these data, proposed treatment response criteria including PET were formulated, drawing from both prior anatomic models (notably WHO, RECIST, and RECIST 1.1) and the EORTC PET response draft criteria (36 (link)). These conclusions were based on a consensus approach among the 4 authors. Thus, a systematic review and a limited Delphilike approach augmented by key data were undertaken to reach consensus in a small group. For demonstration purposes, 18F-FDG PET scans obtained at our institution on 1 of 2 GE Healthcare PET/CT scanners were analyzed with several tools, including a tool for response assessment.
Wahl R.L., Jacene H., Kasamon Y, & Lodge M.A. (2009). From RECIST to PERCIST: Evolving Considerations for PET Response Criteria in Solid Tumors. Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 50(Suppl 1), 122S-150S.
Publication 2009
Attention CAT SCANNERS X RAY F18, Fluorodeoxyglucose Lymphoma Malignant Neoplasms Positron-Emission Tomography Radiography Scan, CT PET
2
Survival Analysis in Nuclear Cardiology
In comparing the survival distributions of two or more groups (for example, new therapy vs standard of care), Kaplan-Meier estimation1 and the log-rank test2 are the basic statistical methods of analyses. These are non-parametric methods in that no mathematical form of the survival distributions is assumed. If an investigator is interested in quantifying or investigating the effects of known covariates (e.g., age or race) or predictor variables (e.g., blood pressure), regression models are utilized. As in the conventional linear regression models, survival regression models allow for the quantification of the effect on survival of a set of predictors, the interaction of two predictors, or the effect of a new predictor above and beyond other covariates.
Among the available survival regression models, the Cox proportional hazards model developed by Sir David Cox3 has seen great use in epidemiological and medical studies, and the field of nuclear cardiology is no exception. What follows are some examples of Cox models being used in nuclear cardiology. Xu et al4 (link) looked at how myocardial scarring (assessed with positron emission tomography [PET] or single photon emission computed tomography [SPECT]) and other demographic and medical history factors predicted mortality in patients with advanced heart failure who received cardiac resynchronization therapy. Bourque et al5 (link) looked at how left ventricular ejection fraction (LVEF, assessed with angiography) and nuclear summed rest score (SRS, assessed with SPECT) interacted to change the risk of mortality. Hachamovitch and Berman6 (link) looked at the incremental prognostic value of myocardial perfusion SPECT (MPS) parameters in the prediction of sudden cardiac death. Nakata et al7 (link) looked at how the heart-to-mediastinum ratio (assessed with metaiodobenzylguanidine [MIBG] imaging) predicted cardiac death.
Survival models other than the Cox model have been used in nuclear cardiology as well. For example, in a study of diagnosis strategies for quantifying myocardial perfusion with SPECT, Duvall et al8 (link) utilized a log-normal survival model, a member of the parametric family of regression survival models, since initial data exploration revealed that the proportional hazards assumption of the Cox model was invalid. While this is an excellent example of when to utilize other survival models, it has been more common to see such data presented in conjunction with a Cox model analysis. In earlier studies of MPS-derived predictors of cardiac events, Hachamovitch et al9 (link) used Cox models to identify significant predictors and parametric models, specifically the accelerated failure time (AFT) model, to make estimates of the time to certain percentiles of survival. An identical analysis strategy was used by the research group comprised of Cuocolo, Acampa, Petretta, Daniele et al10 (link)–13 (link) in their research of the impact of various SPECT-derived predictors on the occurrence of cardiac events.
George B., Seals S, & Aban I. (2014). Survival analysis and regression models. Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 21(4), 686-694.
Publication 2014
3-Iodobenzylguanidine Angiography Blood Pressure Cardiac Death Cardiac Events Cardiac Resynchronization Therapy Cardiovascular System Family Member Heart Heart Failure Mediastinum Myocardium Patients Perfusion Positron-Emission Tomography Sudden Cardiac Death Tests, Diagnostic Therapeutics Tomography, Emission-Computed, Single-Photon Ventricular Ejection Fraction
3
Alzheimer's Diagnosis and Progression in ADNI
Our study population was 426 ADNI participants with an available florbetapir and MRI scan as of November 2011 (126 normal, 162 early MCI, 85 LMCI, 53 AD); 417 of these participants also had an FDG-PET scan acquired approximately concurrently with the florbetapir scan (average time between FDG-PET and florbetapir, <1 week). Approximately ⅔ of the total sample were newly enrolled subjects who had no longitudinal follow-up, whereas approximately ⅓ were continuing normal (n = 76) and LMCI (n = 81) participants from ADNI 1 who were followed for an average of about 4 years prior to their florbetapir scans.
Full inclusion/exclusion criteria are described in detail at www.adni-info.org. Briefly, all subjects were between the ages of 55 and 90 years, had completed at least 6 years of education, were fluent in Spanish or English, and were free of any other significant neurologic diseases. LMCI participants had a subjective memory complaint, a Clinical Dementia Rating (CDR) of 0.5, and were classified as single- or multidomain amnestic.14 The EMCI group differed from LMCI only based on education-adjusted scores for the delayed paragraph recall subscore on the Wechsler Memory Scale–Revised Logical Memory II such that EMCI subjects were intermediate between normal subjects and LMCI. Normal subjects had CDR scores of 0, and patients with AD met standard diagnostic criteria.15 (link)The ADAS-cog16 (link) was used in our cross-sectional analyses and as the primary outcome variable in our longitudinal analyses; total score ranges from 0 to 70, with a higher score indicating poorer cognitive function. We also assessed changes in diagnostic status (eg, remaining LMCI or converting to AD), which was determined at individual testing sites.
Apolipoprotein E (APOE) genotypes were determined with blood samples for all except 2 EMCI subjects.
Landau S.M., Mintun M.A., Joshi A.D., Koeppe R.A., Petersen R.C., Aisen P.S., Weiner M.W, & Jagust W.J. (2012). Amyloid Deposition, Hypometabolism, and Longitudinal Cognitive Decline. Annals of neurology, 72(4), 578-586.
Publication 2012
Apolipoproteins E BLOOD Cognition Diagnosis florbetapir Genotype Hispanic or Latino Memory Mental Recall MRI Scans Nervous System Disorder Patients Positron-Emission Tomography Radionuclide Imaging
4
Multimodal PET Imaging of Tau and Amyloid-beta

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Publication 2017
AH 22 AV-1451 Cerebellum Cortex, Cerebral florbetapir Pittsburgh compound B Positron-Emission Tomography Scan, CT PET
5
Automated Reference Tissue Extraction for PET
Two SVCA algorithms were used to automatically extract reference tissue TACs from dynamic PET scans. Predefined kinetic classes were available from a previous study using data from seven healthy controls and seven patients with traumatic brain injury (Boellaard et al, 2008 (link)). Details concerning this procedure and cluster analysis were reported in Turkheimer et al (2007) (link); however, a summary of cluster analysis is as follows.
The original method (SVCA6) uses six kinetic classes normal gray matter, normal white matter, blood, bone, and soft tissue regions, and gray matter with specific binding. The first five classes were defined on a separate set of normal controls while the last one, corresponding to gray matter with high microglia density, was obtained from the brain injury patients.
To extract the reference, the dynamic PET scan is first normalized as described by Turkheimer et al (2007) (link): each voxel value is reduced by the frame average and divided by the standard deviation. Therefore, the normalization is affected by the size of the reconstructed field of view; for this study, both definition of kinetic classes and application cluster analysis were preformed on scans acquired from the same scanner using similar scanning protocol. However, in case of using SVCA4, normalization was done on voxels that correspond with brain tissue only (based on MRI-derived coregistered gray- and white-matter segmentations). Thereby, this method also avoids the effects of differences in field of view between different scanners.
Next, each voxel TAC of this scan is analyzed using the set of predefined kinetic classes to find the scaling coefficient of each kinetic class, so that the total TAC is equal to the sum of these scaled kinetic classes. As the kinetic classes are not orthogonal, a nonnegative least squares algorithm (Turkheimer et al, 2007 (link)) is used for finding the scaling coefficients. Scaling coefficients of each kinetic class are stored in coefficient maps showing their spatial distribution.
Finally, to extract the reference tissue curve, the coefficient map from the (normal) gray-matter kinetic class is used to calculate the weighted average, as follows: where, N is the number of voxels, TACNS(t) the resulting reference tissue TAC, TACiVoxel(t) the TAC from voxel i of the (nonnormalized) dynamic PET scan, and wiGray the gray-matter kinetic class scaling coefficient estimated for voxel i.
The modified supervised cluster analysis method (SVCA4) (Boellaard et al, 2008 (link)) is similar to SVCA6, except that only four kinetic classes are used: gray matter with specific (R)-[11C]PK11195 binding, gray matter without specific binding, white matter, and blood. This modified method uses the mentioned coregistered segmented MRI scans to exclude skull and soft tissue parts from each frame of the PET scan before performing cluster analysis, same as mentioned above but now with only four kinetic classes. Removal of skull and soft tissue was simply done by setting voxel values to zero for nonbrain structures.
Yaqub M., van Berckel B.N., Schuitemaker A., Hinz R., Turkheimer F.E., Tomasi G., Lammertsma A.A, & Boellaard R. (2012). Optimization of supervised cluster analysis for extracting reference tissue input curves in (R)-[11C]PK11195 brain PET studies. Journal of Cerebral Blood Flow & Metabolism, 32(8), 1600-1608.
Publication 2012
BLOOD Bones Brain Brain Injuries Cranium CXCL11 protein, human Gray Matter Kinetics Microglia Microtubule-Associated Proteins MRI Scans Patients PK 11195 Positron-Emission Tomography Radionuclide Imaging Reading Frames Tissues Traumatic Brain Injury White Matter

Most recents protocols related to «Positron-Emission Tomography»

1
Psilocybin Therapy Receptor Modulation
Not available on PMC !

Example 9

The following example provides details of a study used to determine whether pimavanserin or ketanserin induced changes in subjective experience during psilocybin therapy are due to changes in 5-HT2A occupancy. If not, downstream molecular and cellular effects that may be important in psilocybin's therapeutic effects may be preserved after co-treatment with a 5-HT2A specific antagonist and/or inverse agonist.

In this study, [11C]CIMBI-36 (a selective 5-HT2A receptor agonist positron emission tomography (PET radioligand) will be used to investigate whether 5-HT2A binding is affected by placebo vs. pimavanserin or ketanserin

At time t=0, subjects will be administered 25 mg psilocybin (PSI) in combination with either a placebo, or a low or high dose of pimavanserin or ketanserin. At t=2 hours, subjects will be given a tracer dose of [11C]CIMBI-36. At t=2-3 hours, a PET scan will be performed, to determine whether 5-HT2A binding is affected by either dose of pimavanserin or ketanserin.

US11865126B2. Method for treating anxiety disorders, headache disorders, and eating disorders with psilocybin (2024-01-09). Compass Pathfinder Limited [GB]. Inventors: Derek John Londesbrough [GB], Christopher Brown [GB], Julian Scott Northen [GB], Gillian Moore [GB], Hemant Kashinath Patil [GB], David E. Nichols [US], Megan Croal [GB], Hans Åke Eriksson [GB], George Goldsmith [GB], Molly Tabitha Hickey [GB], Shaun Hurley [GB], Ekaterina Malievskaia [GB], Lindsey Marwood [GB], Drummond E-Wen Joe Mcculloch [GB], Laurie Emma Medhurst [GB], Nathan Poulsen [US], Aslihan Selimbeyoglu [GB], Anaïs Soula [GB], Amanda Tan Shuxiang [GB], Manon Cecile Elisabeth Veraart [GB], Tobias Patrick Whelan [GB], Lars Christian Wilde [GB], Stephen Wright [GB].
Full text: Click here
Patent 2024
Cells Cimbi-36 Ketanserin pimavanserin Placebos Positron-Emission Tomography Psilocybin Serotonin 5-HT2A Receptor Antagonists Therapeutic Effect
2
Optimizing Targeted Molecular Probe Avidity
Not available on PMC !

Example 14

Eight NH2—PEGn-RGD peptides containing spacers of various PEG lengths (n=2, 4, 6, 8, 10, 12, 14, 16) will be prepared by adding the corresponding Boc-PEGn-NHS to RGD in a PBS buffer (pH=8.2), followed by Boc deprotection. Photo-ODIBO-NHS, prepared using previously reported procedures, will then be mixed with the prepared NH2—PEGn-RGD in a PBS buffer (pH=8.2) to produce photo-OIDBO-PEGn-RGD. N3-PEG4-cetuximab will be prepared using previously reported procedures. N3—PEG4-cetuximab and the eight photo-ODIBO-PEGn-RGD peptides (n=2, 4, 6, 8, 10, 12, 14, 16) will be used for in vitro screening (at 4° C. to minimize the internalization of targeting probes). As shown in FIG. 11: 1): eight mixed-ligands stock solutions will be prepared by mixing N3-PEG4-cetuximab with one of the eight photo-OIDBO-PEGn-RGD peptides; 2) U87MG cells will be cultured in a 96-well plate; 3) one of the above eight mixed-ligands stock solution will be added into each well (eight wells in total) pre-seeded with U87MG; 2) after the ligands bind to the targeted receptors, the excess (unbound) targeting ligands will be washed off using a PBS buffer (repeated 5 times to ensure complete removal); 3) a UV lamp (365 nm) will be applied to deprotect the azide-inactive photo-ODIBO and generate azide-active “ODIBO”, subsequently triggering ligation between the N3-PEG4-cetuximab and ODIBO-PEGn-RGD; 4) after being incubated for an additional 2 h, 64Cu-labeled N3—NOTA will be added to click with the “excess” ODIBO-PEGn-RGD (that binds to cells, but does not click to N3-PEG4-cetuximab); and 5) the excess N3-(64Cu)NOTA will be removed, and the N3-(64Cu)NOTA clicked to “excess” ODIBO-PEGn-RGD will be measured on MicroBeta2 Plate Counter. One group without UV irradiation will be used as a negative control to get counts from the non-specific binding of N3-(64Cu)NOTA. After subtracting the non-specific binding, the specific binding of N3-(64Cu)NOTA obtained from the eight ODIBO-PEGn-RGD (n=2, 4, 6, 8, 10, 12, 14, 16) will be compared. The well with the lowest specific binding will contain the highest amount of clicking product (between cetuximab-PEG4-N3 and ODIBO-PEGn-RGD), thus the corresponding spacer will be the most potent.

The ODIBO-PEGn-RGD containing the most potent PEG spacer will click with Tz-NOTA-N3 and then be radiolabeled with 64Cu, and the resulting Tz-(64Cu)NOTA-PEGn-RGD will be used for the in vitro avidity studies on U87MG cells. Tz-(64Cu)NOTA-RGD (without a PEG spacer) will be used as a negative control because the distance between RGD and cetuximab in the resulting heterodimer is too short to achieve avidity effect (proved in preliminary study, FIG. 5B). Briefly, Tz-(64Cu)NOTA-PEGn-RGD/TCO-PEG4-cetuximab ligation product (cetuximab-PEG4-(64Cu)NOTA-PEGn-RGD) will be used for cell uptake/efflux, binding affinity and Bmax measurements on U87MG cells. After high avidity effect is confirmed on the above ligation product, in vivo evaluation will be performed then. Mice bearing U87MG xenografts will be pre-injected with 100 μg of TCOPEG4-cetuximab, and 24 h later, ˜250-350 pCi of Tz-(64Cu)NOTA-PEGn-RGD (or Tz-(64Cu)NOTA-RGD in the negative control group) will be injected. Then 1 h dynamic PET scans will be performed at multiple time points (p.i., 4, 18, and/or 28 h). As cetuximab is cleared through the liver, kinetics on tumor and liver at mid and late time points can be evaluated. At mid/late time points (4, 18, 28 h) when most of the un-ligated Tz-(64Cu)NOTAPEGn-RGD has been washed off, observation of relatively slower tumor washing out and faster liver clearing (compared to that from Tz-(64Cu)NOTA-RGD) can indicate the much stronger binding with tumor cells, and thus an avidity effect of in vivo ligation product (cetuximab-PEG2-(64Cu)NOTA-PEGn-RGD) is being achieved.

Various references are cited in this document, which are hereby incorporated by reference in their entireties herein.

US11857648B2. Dimerization strategies and compounds for molecular imaging and/or radioimmunotherapy (2024-01-02). UNIVERSITY OF PITTSBURGH—OF THE COMMONWEALTH SYSTEM OF HIGHER EDUCATION [US]. Inventors: Dexing Zeng [US], Lingyi Sun [US], Yongkang Gai [US].
Full text: Click here
Patent 2024
1,4,7-triazacyclononane-N,N',N''-triacetic acid Azides Buffers Cells Cetuximab Heterografts Kinetics Ligands Ligation Liver Mus Neoplasms Peptides Positron-Emission Tomography Ultraviolet Rays
3
Psilocybin and Benzodiazepine Effects on 5-HT2A Binding
Not available on PMC !

Example 6

The following example provides details of a study used to determine whether alprazolam-induced changes in subjective experience during psilocybin therapy are due to changes in 5-HT2A occupancy. If not, downstream molecular and cellular effects that may be important in psilocybin's therapeutic effects may be preserved after co-treatment with a benzodiazepine.

In this study, [11C]CIMBI-36 (a selective 5-HT2A receptor agonist positron emission tomography (PET) radioligand) will be used to investigate whether 5-HT2A binding is affected by placebo vs. alprazolam.

At time t=0, subjects will be administered 25 mg psilocybin (PSI) in combination with either a placebo, or alprazolam. At t=2 hours, subjects will be given a tracer dose of [11C]CIMBI-36. At t=2-3 hours, a PET scan will be performed, to determine whether 5-HT2A binding is affected by either dose of alprazolam.

This study may optionally be performed using diazepam instead of alprazolam.

US11865126B2. Method for treating anxiety disorders, headache disorders, and eating disorders with psilocybin (2024-01-09). Compass Pathfinder Limited [GB]. Inventors: Derek John Londesbrough [GB], Christopher Brown [GB], Julian Scott Northen [GB], Gillian Moore [GB], Hemant Kashinath Patil [GB], David E. Nichols [US], Megan Croal [GB], Hans Åke Eriksson [GB], George Goldsmith [GB], Molly Tabitha Hickey [GB], Shaun Hurley [GB], Ekaterina Malievskaia [GB], Lindsey Marwood [GB], Drummond E-Wen Joe Mcculloch [GB], Laurie Emma Medhurst [GB], Nathan Poulsen [US], Aslihan Selimbeyoglu [GB], Anaïs Soula [GB], Amanda Tan Shuxiang [GB], Manon Cecile Elisabeth Veraart [GB], Tobias Patrick Whelan [GB], Lars Christian Wilde [GB], Stephen Wright [GB].
Full text: Click here
Patent 2024
Alprazolam Benzodiazepines Cells Cimbi-36 Diazepam Placebos Positron-Emission Tomography Psilocybin Therapeutic Effect
4
Evaluating Sleep Quality and Alzheimer's Risk
Last, we replicated analyses from an earlier WRAP PET publication.20 (link) That study showed significant yet small associations between less adequate sleep, more sleep problems and greater SOM on the MOS and greater amyloid PET burden in Alzheimer’s disease–sensitive brain regions among 98 cognitively unimpaired adults (aged 62.4 ± 5.7 years) at their fourth WRAP visit. Participants were identified for the present analysis if they had completed WRAP Visit 4 (including sleep assessment), had completed a PiB PET scan and were non-demented; 315 individuals met these inclusion criteria. To match with the data set in Sprecher et al., we then excluded 95 people, leaving a sample size n = 220. We performed the same linear regression previously performed in Sprecher et al.,20 (link) including age, sex, APOE e4 genotype, family history of Alzheimer’s disease and BMI as covariates.
Du L., Langhough R., Hermann B.P., Jonaitis E., Betthauser T.J., Cody K.A., Mueller K., Zuelsdorff M., Chin N., Ennis G.E., Bendlin B.B., Gleason C.E., Christian B.T., Plante D.T., Chappell R, & Johnson S.C. (2023). Associations between self-reported sleep patterns and health, cognition and amyloid measures: results from the Wisconsin Registry for Alzheimer’s Prevention. Brain Communications, 5(2), fcad039.
Full text: Click here
Publication 2023
Adult Apolipoproteins E APP protein, human Brain Familial Alzheimer Disease (FAD) Genotype Positron-Emission Tomography Sleep
5
Midlife Factors and Alzheimer's Disease
Participants were drawn from the WRAP, a longitudinal study designed to identify midlife factors associated with the development of Alzheimer’s disease.39 (link),40 (link) Enrolment of participants began in 2001, with the first follow-up visit occurring 2 to 4 years after the baseline visit and all additional visits occurring at 2-year intervals thereafter. WRAP participants were free of dementia at enrolment (mean age 54 years). All study procedures were approved by the University of Wisconsin School of Medicine and Public Health Institutional Review Board and are in concordance with the Declaration of Helsinki.
At each study visit, participants completed comprehensive neuropsychological assessment and multiple questionnaires related to a broad array of factors, including lifestyle, modifiable risk factors, medical history and memory functioning. Sleep measures were added in two stages to the WRAP assessment protocol. To be eligible for the primary analyses, participants needed to have completed the full set of sleep measures at least once and be free of dementia at time of sleep assessment (n = 619). To be eligible for secondary analyses, participants needed to have completed at least one of the sleep questionnaires described below and had completed a Pittsburgh Compound B (PiB) PET scan.
Du L., Langhough R., Hermann B.P., Jonaitis E., Betthauser T.J., Cody K.A., Mueller K., Zuelsdorff M., Chin N., Ennis G.E., Bendlin B.B., Gleason C.E., Christian B.T., Plante D.T., Chappell R, & Johnson S.C. (2023). Associations between self-reported sleep patterns and health, cognition and amyloid measures: results from the Wisconsin Registry for Alzheimer’s Prevention. Brain Communications, 5(2), fcad039.
Full text: Click here
Publication 2023
Developmental Disabilities Ethics Committees, Research factor A Neuropsychological Tests Pittsburgh compound B Positron-Emission Tomography Presenile Dementia Sleep

Top products related to «Positron-Emission Tomography»

1
Biograph mct by Siemens
Sourced in Germany, United States, Netherlands, Japan
The Biograph mCT is a Positron Emission Tomography (PET) and Computed Tomography (CT) system manufactured by Siemens. It is designed to provide high-quality imaging for clinical and research applications. The core function of the Biograph mCT is to acquire and analyze PET and CT scans simultaneously, allowing for accurate anatomical and functional information to be obtained.
2
Inveon by Siemens
Sourced in Germany, United States
The Inveon is a versatile and advanced pre-clinical imaging system designed for small animal research. It provides high-resolution imaging capabilities, supporting various modalities such as PET, SPECT, CT, and optical imaging. The Inveon is a robust and reliable platform that enables researchers to conduct non-invasive in vivo studies, facilitating the understanding of disease mechanisms and the evaluation of new therapies.
3
Biograph mmr by Siemens
Sourced in Germany, United States
The Biograph mMR is a positron emission tomography (PET) and magnetic resonance imaging (MRI) system manufactured by Siemens. It integrates PET and MRI technologies to provide simultaneous imaging of the body. The Biograph mMR enables the acquisition of high-quality PET and MRI data in a single examination.
4
Discovery 690 by GE Healthcare
Sourced in United States, Germany, France, United Kingdom
The Discovery 690 is a highly versatile positron emission tomography (PET) and computed tomography (CT) system designed for advanced imaging applications. It features a powerful combination of cutting-edge technology and innovative engineering to provide high-quality images and efficient workflow.
5
Discovery ste by GE Healthcare
Sourced in United States, United Kingdom, Netherlands
The Discovery STE is a medical imaging system designed for Positron Emission Tomography (PET) and Computed Tomography (CT) scans. It combines PET and CT modalities to provide high-quality images for diagnostic and clinical applications.
6
Inveon pet ct scanner by Siemens
Sourced in United States, Germany, United Kingdom
The Inveon PET/CT scanner is a laboratory equipment product developed by Siemens. It combines positron emission tomography (PET) and computed tomography (CT) imaging modalities in a single system. The Inveon PET/CT scanner allows for the acquisition of both functional and anatomical data from small animal subjects.
7
Ecat exact hr scanner by Siemens
Sourced in Germany, United States, Sweden
The ECAT EXACT HR+ scanner is a high-resolution positron emission tomography (PET) imaging system designed for research and clinical applications. It features a large field-of-view, high-sensitivity detection, and advanced image reconstruction capabilities. The ECAT EXACT HR+ provides detailed visualization of physiological and metabolic processes within the human body.
8
Matlab by MathWorks
Sourced in United States, United Kingdom, Germany, Canada, Japan, Sweden, Austria, Morocco, Switzerland, Australia, Belgium, Italy, Netherlands, China, France, Denmark, Norway, Hungary, Malaysia, Israel, Finland, Spain
MATLAB is a high-performance programming language and numerical computing environment used for scientific and engineering calculations, data analysis, and visualization. It provides a comprehensive set of tools for solving complex mathematical and computational problems.
9
Pmod software by PMOD Technologies
Sourced in Switzerland, United States
PMOD software is a comprehensive platform for the analysis of biomedical imaging data. It provides a wide range of tools for image processing, kinetic modeling, and quantitative analysis of various medical imaging modalities, including PET, SPECT, CT, and MRI. The software is designed to facilitate the evaluation of in vivo biochemical and physiological processes in both clinical and research settings.
10
Gamma counter by PerkinElmer
Sourced in United States
The Gamma counter is a laboratory instrument used to measure the radioactivity of samples. It detects and quantifies gamma radiation emitted by radioactive isotopes present in the samples.

More about "Positron-Emission Tomography"

Positron Emission Tomography (PET) is a powerful non-invasive medical imaging technique that uses radioactive tracer substances, known as radiotracers, to visualize and quantify biological processes within the body.
This revolutionary technology has transformed medical research and clinical practice, enabling earlier disease detection, more accurate staging, and personalized treatment planning.
PET scans leverage the unique properties of positron-emitting radiotracers to generate detailed images of organ and tissue function, metabolism, and the presence of disease.
By tracing the distribution and concentration of these radiotracers, clinicians and researchers can gain invaluable insights into physiological processes, including those related to oncology, neurology, cardiology, and other medical fields.
The versatility of PET imaging is further enhanced by the use of advanced scanners, such as the Biograph mCT, Inveon, Biograph mMR, Discovery 690, and Discovery STE.
These state-of-the-art PET/CT and PET/MRI systems combine the functional information provided by PET with the anatomical detail of other imaging modalities, enabling more comprehensive and accurate diagnoses.
To maximize the potential of PET research, researchers often leverage specialized software like MATLAB and PMOD, which facilitate data analysis, image processing, and the interpretation of PET scans.
Additionally, tools like the ECAT EXACT HR+ scanner and Gamma counters play a crucial role in acquiring and analyzing PET data, ensuring the reproducibility and accuracy of research findings.
By harnessing the power of PET technology, clinicians and scientists can advance their understanding of disease mechanisms, develop more effective treatments, and ultimately improve patient outcomes.
Whether you're a healthcare professional, a medical researcher, or simply interested in the latest advancements in medical imaging, the world of Positron Emission Tomography offers a wealth of opportunities to explore and discover.