HYPERImage Project Background Information

HYPERImage Project Background InformationAdvances in healthcare have been boosted by the constant improvement of medical imaging technologies - for example, in terms of sensitivity and resolution. Magnetic Resonance (MR) imaging, Positron Emission Tomography (PET) and Computed Tomography are now widely used in the diagnosis and staging of many different diseases, including cardiovascular diseases and cancer.

The main goal of the HYPERImage project is to demonstrate the diagnostic value of simultaneous PET/MR imaging. The project partners aim to contribute to significant improvement in the diagnosis of several diseases, and to help in providing a state-of-the-art imaging technology for future applications in biomedical and clinical research.

PET, the most sensitive molecular imaging modality, requires an additional modality such as CT to provide an anatomical reference for lesion localization. The integration of anatomical (CT) and functional (PET) imaging has proven itself as a standard clinical tool. However, PET/CT has its limitations, notably irradiation (X-ray) dose and reduced soft tissue contrast compared to MR imaging. Thanks to recent advances in the field, MR imaging can now be considered an improved alternative to CT. Like CT imaging, MR imaging can provide excellent human anatomical information, but it also offers superior soft-tissue characterization, excellent temporal resolution and a significant amount of functional information as well.

However, a combined PET/MR scanner can only provide accurate, reliable images when the two imaging modalities are optimally integrated into a single scanner. To this end, the HYPERImage project is investigating the following topics:

MR-compatible PET detector technology with excellent time resolution PET produces three-dimensional images of functional processes in the body - for example, the uptake of glucose that fuels metabolic activity. To do so, it detects pairs of gamma rays (high energy electromagnetic radiation) originating from a radioactive tracer, a small amount of which is injected into the patient prior to the scan.

The gamma-ray detectors used in current PET scanners are based on vacuum photomultiplier tubes that do not function well in the strong electromagnetic fields found in an MR scanner. The HYPERImage project will particularly focus on reducing the mutual interference between PET and MR data measurements to a level that allows undisturbed image acquisition with both modalities at the same time.

The technical breakthrough behind the team's development of an MR-compatible gamma-ray detector is the development of a new solid-state, scalable and compact digital detector technology. This technology is based around silicon photomultiplier arrays that offer the desired sensitivity, energy resolution and timing resolution required for time-of-flight PET measurements, and that feature integrated digital read-out electronics.

To increase the effective sensitivity, and to reduce the scan-time and dependence of sensitivity on patient size, the detector has been designed to support time-of-flight PET measurements with extremely short coincidence time resolution. In time-of-flight PET scanners, not only the direction of the gamma ray paths is measured but also the difference in time it takes the pair of gamma rays generated by the PET tracers to reach the detector. This time difference measurement substantially increase the precision with which the tracer can be localized. Time-of-flight measurements increase the effective sensitivity by a factor 10 compared to standard systems.

The silicon photomultiplier array's integrated digital read-out electronics contain a low-jitter and low-power signal acquisition unit. Low power consumption is an essential requirement when preparing the technology for integrated whole body scanning applications.

Hybrid PET/MR test systems
The HYPERImage project aims to develop two PET/MR test systems in order to investigate the technical and pre-clinical/clinical feasibility of simultaneous PET/MR. Initially, a pre-clinical system will be constructed, both to guide further development towards a clinical test system and to test simultaneous PET/MR under pseudo-clinical conditions.

Dynamic PET/MR motion, attenuation and functional data acquisition techniques
The longer acquisition times for PET mean that the structures being imaged typically move during the scan - for example, as the patient breathes. This results in significant blurring of the image. Monitoring of the exact position of the relevant organs using the MR imaging could be used to significantly reduce motion-induced PET image blurring.

The HYPERImage project therefore aims to develop acquisition and data processing techniques that will enable MR monitoring of patient motion. These will be used to correct the PET data for the effects of patient motion. The MR data may also enable the PET data to be corrected for the effects of attenuation (the scattering of high-energy gamma rays generated by the PET tracers by parts of the human body).

Such an attenuation correction is required to obtain quantitative PET images with the hybrid system. MR acquisition sequences will be developed in such a way that the PET correction data is acquired along with complementary functional MR data. These techniques will be evaluated in phantom studies and subsequently in pre-clinical studies.

PET/MR investigations towards cancer and cardiovascular diseases The HYPERImage project aims to validate the improvement in diagnostic efficacy gained with motion compensation and MR-based attenuation correction during concurrent PET/MR, and compare this with conventional PET/CT and MR. The registration of (f)MRI features (e.g. vascular physiology) will be correlated with values obtained with PET in order to improve the early detection, staging, and response monitoring of cancer, atherosclerosis and myocardial infarction.

Furthermore, the HYPERImage project aims to assess the advantages of concurrent imaging during dual-agent injection studies. Its pre-clinical investigations have to demonstrate the added diagnostic value of the PET/MR animal insert that it is developing, which will contribute to the fine-tuning of a system ready for clinical validation.

HYPERImage consortium
The HYPERImage consortium comprises three universities (King's College London, UK; Universität Heidelberg, Germany; and Universiteit Ghent - Institute for Broadband Technology, Belgium), three research foundations (Fundación Centro Nacional de Investigaciones Cardiovasculares, Spain; Fondazione Bruno Kessler, Italy; and The Netherlands Cancer Institute, The Netherlands), a university medical center (Uniklinikum Hamburg-Eppendorf, Germany) and the industrial partner (Philips, The Netherlands and Germany).

For further information, please visit:
http://www.hybrid-pet-mr.eu

Related news articles:

Most Popular Now

Researchers Find Telemedicine may Help R…

Low-value care - medical tests and procedures that provide little to no benefit to patients - contributes to excess medical spending and both direct and cascading harms to patients. A...

AI Revolutionizes Glaucoma Care

Imagine walking into a supermarket, train station, or shopping mall and having your eyes screened for glaucoma within seconds - no appointment needed. With the AI-based Glaucoma Screening (AI-GS) network...

AI may Help Clinicians Personalize Treat…

Individuals with generalized anxiety disorder (GAD), a condition characterized by daily excessive worry lasting at least six months, have a high relapse rate even after receiving treatment. Artificial intelligence (AI)...

Accelerating NHS Digital Maturity: Paper…

Digitised clinical noting at South Tees Hospitals NHS Foundation Trust is creating efficiencies for busy doctors and nurses. The trust’s CCIO Dr Andrew Adair, deputy CCIO Dr John Greenaway, and...

Mobile App Tracking Blood Pressure Helps…

The AHOMKA platform, an innovative mobile app for patient-to-provider communication that developed through a collaboration between the School of Engineering and leading medical institutions in Ghana, has yielded positive results...

AI can Open Up Beds in the ICU

At the height of the COVID-19 pandemic, hospitals frequently ran short of beds in intensive care units. But even earlier, ICUs faced challenges in keeping beds available. With an aging...

Can AI Help Detect Cognitive Impairment?

Mild cognitive impairment (MCI) can be an early indicator of Alzheimer's disease or dementia, so identifying those with cognitive issues early could lead to interventions and better outcomes. But diagnosing...

Customized Smartphone App Shows Promise …

A growing body of research indicates that older adults in assisted living facilities can delay or even prevent cognitive decline through interventions that combine multiple activities, such as improving diet...

New Study Shows Promise for Gamified mHe…

A new study published in Multiple Sclerosis and Related Disorders highlights the potential of More Stamina, a gamified mobile health (mHealth) app designed to help people with Multiple Sclerosis (MS)...

AI Model Predicting Two-Year Risk of Com…

AFib (short for atrial fibrillation), a common heart rhythm disorder in adults, can have disastrous consequences including life-threatening blood clots and stroke if left undetected or untreated. A new study...

Patients' Affinity for AI Messages …

In a Duke Health-led survey, patients who were shown messages written either by artificial intelligence (AI) or human clinicians indicated a preference for responses drafted by AI over a human...

New Research Explores How AI can Build T…

In today’s economy, many workers have transitioned from manual labor toward knowledge work, a move driven primarily by technological advances, and workers in this domain face challenges around managing non-routine...