Abstract This paper reviews approaches to improving imaging capabilities in nuclear medicine using quantum-inspired technologies. Positron emission tomography and single-photon emission computed tomography are established diagnostic methods in nuclear medicine and are extremely useful for detecting early stage cancers and other diseases through the detection of small numbers of molecules using sub-MeV gamma rays; however, the principles of these modalities have not changed for more than five decades. Recently, quantum technologies have attracted significant attention, particularly quantum sensors and quantum entanglement in the optical-photon regime. In this review, we introduce emerging efforts that utilize quantum-inspired technologies, such as electron spin, nuclear spin and quantum entanglement in the MeV photon (gamma ray) regime. Quantum-enhanced imaging in nuclear medicine may directly contribute to improvements in diagnostic capability and patients’ quality of life.
Abstract This paper reviews approaches to improving imaging capabilities in nuclear medicine using quantum-inspired technologies. Positron emission tomography and single-photon emission computed tomography are established diagnostic methods in nuclear medicine and are extremely useful for detecting early stage cancers and other diseases through the detection of small numbers of molecules using sub-MeV gamma rays; however, the principles of these modalities have not changed for more than five decades. Recently, quantum technologies have attracted significant attention, particularly quantum sensors and quantum entanglement in the optical-photon regime. In this review, we introduce emerging efforts that utilize quantum-inspired technologies, such as electron spin, nuclear spin and quantum entanglement in the MeV photon (gamma ray) regime. Quantum-enhanced imaging in nuclear medicine may directly contribute to improvements in diagnostic capability and patients’ quality of life.
Quantum technology, such as the quantum computers, has attracted significant attention in recent years. In nuclear medicine, powerful and highly sensitive molecular imaging modalities such as PET (Positron Emission Tomography), SPECT (Single Photon Emission CT) and MRI (Magnetic Resonance Imaging) provide accurate morphological and functional information. Exploiting certain aspects of quantum mechanics may bring further improvements in sensitivity, spatial resolution and enable novel capabilities in the field of biomedical imaging and sensing. In this workshop, the possibilities of biomedical applications inspired by quantum technology were discussed. The following exciting topics were covered: quantum entanglement in PET, dynamic nuclear polarization toward more sensitive MRI, quantum sensors based on the229mTh nuclear clock, plans of an advanced high-energy gamma-ray source at CERN, laser-assisted radiation detection, solid-state quantum sensors such as those based on nitrogen vacancy centers in diamond, quantum sensing using cascade multi photons, and a laser-based radioactive isotope analysis.
Over the past few decades, medical imaging has revolutionized the way clinicians diagnose, monitor, and treat diseases. However, despite significant advancements in imaging resolution, contrast, and computational reconstruction, several challenges remain unresolved — especially in terms of early-stage detection, noise reduction, and functional imaging at the cellular or molecular level. In recent years, quantum physics has emerged as a disruptive force with the potential to drastically enhance medical imaging technologies through the application of its fundamental principles such as quantum superposition, entanglement, tunneling, and spin dynamics. This research explores the intersection of quantum mechanics and medical imaging engineering, with a primary focus on how quantum phenomena are being utilized to elevate the performance and capability of key imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and optical-based quantum imaging. The study presents an in-depth analysis of how quantum concepts are applied in practice — such as the role of spin alignment and magnetic resonance in MRI, the use of entangled photon pairs in PET scanning, and the integration of quantum dots in high-contrast optical imaging. Beyond existing technologies, the paper investigates cutting-edge developments including quantum-enhanced sensors, quantum computing for real-time image reconstruction, and the promise of quantum artificial intelligence in image interpretation. By analyzing both theoretical frameworks and experimental findings, the study highlights the significant advantages that quantum technologies bring — such as improved spatial resolution, higher signal-to-noise ratio, and reduced patient exposure to radiation. Despite its promise, the integration of quantum physics into medical imaging is not without challenges. The research discusses limitations such as system decoherence, scalability, clinical costs, and the gap between laboratory research and clinical deployment. In conclusion, this paper argues that while still emerging, the application of quantum physics holds transformative potential for the future of diagnostic imaging, and could ultimately lead to more accurate, efficient, and personalized healthcare.
Medical imaging, a cornerstone of modern diagnostics that underpins early disease detection and personalized therapy, continues to confront fundamental physical limitations inherent to conventional modalities, including suboptimal sensitivity and specificity, limited resolution, and ionizing radiation risks. Quantum imaging, leveraging principles such as entanglement, superposition, and quantum sensing, has emerged as a transformative paradigm with the potential to transcend these limitations. This review systematically summarizes the physical principles, technological advancements, and major clinical applications of quantum technologies in medical imaging. We elaborate on several cutting-edge modalities, including quantum dot probes for targeted imaging and surgical navigation, wearable magnetoencephalography facilitated by optically pumped magnetometers, quantum-enhanced magnetic resonance imaging with nanoscale resolution, quantum optical coherence tomography with dispersion cancellation, photon-counting computed tomography for superior spectral imaging, and nuclear medicine imaging that can visualize the molecular microenvironment. Additionally, the roles of quantum computing and quantum artificial intelligence in accelerating and optimizing the image analysis workflow are also discussed. Furthermore, we analyze the technical bottlenecks and translational barriers that currently hinder the widespread clinical implementation of these emerging technologies. Finally, we outline promising future research directions to accelerate clinical translation, underscoring the transformative potential of quantum medical imaging to advance precision diagnostics and personalized medicine.
Quantum computing is quickly revolutionizing medicine, promising unprecedented levels of processing, analysis, and decision-making abilities. This review discusses recent developments in applying quantum computing to medical imaging, diagnostics, and treatment planning. Quantum machine learning and hybrid quantum-classical models have been used by researchers to show improved accuracy, speed, and scalability in medical image classification, segmentation, and disease prediction. Quantum-improved algorithms are being implemented for early detection of multifactorial diseases like cancer and neurologic disorders with potential for advancing clinical prognoses and biomarker discovery. Moreover, quantum cryptography methods are under development to enhance security for private medical information with concerns regarding expanding privacy and security risks. Improvements in quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) also hold promise in enhancing diagnostic efficiency and resource savings. This review synthesizes existing applications, identifies new trends, and delineates challenges and future directions of quantum computing in medicine. It underscores the importance of explainable and interpretable quantum AI systems to maintain clinical trust and utility. The results indicate that quantum technologies, although in their infancy, have transformative potential for precision medicine, personalized therapy, and sophisticated medical decision-making.
This research explores how quantum physics is redefining the landscape of medical diagnostics through sensing, imaging, and computation. It examines the principles of quantum mechanics superposition, entanglement, and coherence and their application in developing technologies capable of detecting disease at molecular and cellular levels. Quantum imaging surpasses classical resolution limits, while quantum machine learning enables faster and more accurate analysis of medical data. The study also evaluates real-world clinical applications, including wearable magnetoencephalography, quantum-enhanced MRI, and biosensing for cancer biomarkers. Alongside these advancements, it addresses ethical, economic, and regulatory challenges such as data security, cost, and accessibility. The findings suggest that quantum technologies hold transformative potential to shift medicine from reactive treatment to predictive, preventive care provided innovation remains grounded in transparency, inclusivity, and interdisciplinary collaboration.
Quantum computing is fast transforming the medical world and giving it a new level of speed, accuracy, and safety in the health-care information. The latest developments use quantum machine learning and quantum-classical to improve medical imaging like classification, segmentation, and disease prediction, going beyond the capability of the classical algorithms. In the multifactorial disease cases, like cancer and neurological diseases, quantum-enhanced analytics will yield better clinical prognoses, better biomarker discoveries, and earlier detection. In the meantime, quantum-cryptography protocols are under development that will provide protection against privacy threats that are constantly changing to harm patient data. State-of-the-art quantum neural networks and adaptive hybrid quantum convolutional neural networks (CNNs) continue to optimize the process of diagnostics, reducing resource consumption, without losing the level of accuracy. This review maps these new applications, identifies the new trends, and talks about the future challenges of such applications, especially the requirement of explainable, interpretable quantum AI, which the clinicians can believe in. In brief, quantum technologies are in their infancy, but will have the ability to advance precision medicine, customised treatment and revolutionise medical decision-making.