Parallel imaging techniques, such as GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) play an important role in Magnetic Resonance Imaging (MRI) by significantly reducing scan times and enhancing patient comfort without compromising image quality. GRAPPA's algorithmic framework involving calibration and synthesis stages is critical in reconstructing high-quality images. However, the computational load of the calibration stage, especially with large convolutional kernel sizes or an increased number of receiver coils, poses a significant bottleneck and limits its efficiency and applicability in clinical settings. In this paper, we introduce an approach by proposing a hybrid software architecture that integrates quantum computing into the GRAPPA reconstruction process. Our method exploits the computational capabilities of quantum computing to accelerate the calibration phase, thereby enabling real-time processing speeds. Through experimentation, we demonstrate that the quantum-enhanced approach can expedite the calibration process to around 10–20 milliseconds of Quantum Processing Unit (QPU) programming time for each Linear Time-Invariant (LTI) system solved. The method maintains the integrity of calibration outcomes, achieving results on par with conventional central processing unit (CPU)-based processes. The result represents the progress towards real-time MRI reconstruction by reducing clinical MRI workflows times, improving patient throughput, and potentially enabling new diagnostic capabilities. Solving LTI system with more attributes on D-Wave quantum computer will be studied to show advantages of QPU to CPU in the future work.
Parallel imaging techniques, such as GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) play an important role in Magnetic Resonance Imaging (MRI) by significantly reducing scan times and enhancing patient comfort without compromising image quality. GRAPPA's algorithmic framework involving calibration and synthesis stages is critical in reconstructing high-quality images. However, the computational load of the calibration stage, especially with large convolutional kernel sizes or an increased number of receiver coils, poses a significant bottleneck and limits its efficiency and applicability in clinical settings. In this paper, we introduce an approach by proposing a hybrid software architecture that integrates quantum computing into the GRAPPA reconstruction process. Our method exploits the computational capabilities of quantum computing to accelerate the calibration phase, thereby enabling real-time processing speeds. Through experimentation, we demonstrate that the quantum-enhanced approach can expedite the calibration process to around 10–20 milliseconds of Quantum Processing Unit (QPU) programming time for each Linear Time-Invariant (LTI) system solved. The method maintains the integrity of calibration outcomes, achieving results on par with conventional central processing unit (CPU)-based processes. The result represents the progress towards real-time MRI reconstruction by reducing clinical MRI workflows times, improving patient throughput, and potentially enabling new diagnostic capabilities. Solving LTI system with more attributes on D-Wave quantum computer will be studied to show advantages of QPU to CPU in the future work.
The prolonged time required for Magnetic Resonance Imaging (MRI) procedures often causes stress and anxiety in patients. Patients' elongated exposure to a narrow bore of a scanner increases healthcare costs. Additionally, long scan times increase patient discomfort and cause motion artifacts in images. As a clinical MRI method, Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) is a parallel imaging technique that shortens data acquisition time and increases spatial resolution by employing multiple phased-array coils. Missing data are reconstructed by solving a linear time-invariant (LTI) system. When a large size of an interpolation kernel is used, the number of equations of the LTI system is significantly increased and the reconstruction time is delayed. Both software-based and hardware-based approaches have been proposed to accelerate the GRAPPA reconstruction. Dimension reduction is often used as the software-based approach which generates virtual coils and reduces the actual number of physical coils, so the total number of LTI equations is decreased. Field-programmable gate array (FPGA) was employed as a hardware-based approach to solve the LTI system. In this research, a quantum computing method was proposed to accelerate GRAPPA reconstruction. Different from the dimension reduction and FPGA techniques, quantum computing is an emerging computing paradigm that can accelerate computing speed with quantum bits (qubits) rather than classical bits. We decomposed GRAPPA reconstruction into two components: calibration and synthesis, and then applied the D-Wave quantum computing service to accelerate the calibration process. A few calibration coefficients are estimated through solving an LTI system on the D-Wave quantum computer. Then, those calibrated coefficients are used to synthesize missing data in under sampled k-space to meet the requirements of the Nyquist sampling rate and avoid aliasing artifacts. Experimental results show that quantum computing can successfully solve the calibration problem in GRAPPA MRI. The calibrated coefficients and reconstruction results are the same as the outcomes completed on a local computer which has the classical von Neumann architecture. Quantum computing speeds have been quantitatively analyzed and it is promising to accelerate GRAPPA. One drawback of the current implementation is that the D-Wave quantum computing service cannot accept over 30 variables of an LTI system, and this may be caused by the limited qubits which can be used on the quantum computing service now.
Background: Quantum Neural Networks (QNNs) combine quantum computing and artificial intelligence to provide powerful solutions for high-dimensional data analysis. In magnetic resonance imaging (MRI), they address the challenges of advanced imaging sequences and data complexity, enabling faster optimization, enhanced feature extraction, and real-time clinical applications. Methods: A literature review using Scopus, PubMed, IEEE Xplore, ACM Digital Library and arXiv identified 84 studies on QNNs in MRI. After filtering for peer-reviewed original research, 20 studies were analyzed. Key parameters such as datasets, architectures, hardware, tasks, and performance metrics were summarized to highlight trends and gaps. Results: The analysis identified datasets supporting tasks like tumor classification, segmentation, and disease prediction. Architectures included hybrid models (e.g., ResNet34 with quantum circuits) and novel approaches (e.g., Quantum Chebyshev Polynomials). Hardware ranged from high-performance GPUs to quantum-specific devices. Performance varied, with accuracy up to 99.5% in some configurations but lower results for complex or limited datasets. Conclusions: The findings provide the first glimpse into the potential of QNNs in MRI, demonstrating accuracy and specificity in diagnostic tasks and biomarker detection. However, challenges such as dataset variability, limited quantum hardware access, and reliance on simulators remain. Future research should focus on scalable quantum hardware, standardized datasets, and optimized architectures to support clinical applications and precision medicine.
The ever-growing field of medical diagnostics is witnessing a paradigm shift with the integration of quantum computing and deep neural networks (DNN). This paper presents a novel approach that harnesses the computational prowess of quantum algorithms combined with the capabilities of DNNs. The core of this research lies in the development of a novel quantum-enhanced neural network model, for the complex task of reconstructing high-resolution medical images from limited or noisy data sets. The proposed model leverages quantum parallelism to significantly improve the training process of deep networks, while maintaining high fidelity in image reconstruction. Extensive experiments demonstrate that this integrated approach outperforms conventional methods in terms of both speed and accuracy. The quantum-DNN framework exhibits remarkable proficiency in handling large-scale medical images, showing substantial improvements in detecting fine-grained features critical for accurate diagnosis. This paper also delves into the optimization of quantum circuits and layer architectures, ensuring minimal qubit requirements while maximizing computational efficiency.
Abstract Quantum computing is set to revolutionize medical imaging and diagnostics by enhancing speed, accuracy, and the ability to process complex datasets, leading to improved patient outcomes. This review paper provides an extensive comparative study of how quantum-mechanical principles like superposition, entanglement, and interference can overcome the inherent limitations of classical binary computing in the medical field. We explore specific applications in MRI and CT scan reconstruction, the mathematical advantages of the Quantum Fourier Transform (QFT), the integration of Quantum Machine Learning (QML) for automated pathology detection, and the transformative potential of hybrid quantum-classical systems. The paper also addresses current hardware limitations such as qubit decoherence, evaluates materials science innovations in superconducting circuits, and provides a strategic roadmap for the future of quantum-enhanced healthcare. Keywords: Quantum Computing, Medical Imaging, MRI Reconstruction, Qubits, Quantum Machine Learning, Diagnostics, Materials Science Introduction The field of medical imaging has undergone significant transformations over the last few decades, progressing from simple analog X-ray plates to complex digital 3D reconstructions and functional metabolic imaging. However, as the medical community moves toward "Precision Medicine," the demand for ultra-high-resolution data and real-time diagnostic feedback is growing at an exponential rate. Classical computing systems, governed by Moore’s Law, are reaching a physical and algorithmic bottleneck. The sheer volume of data generated by modern 7-Tesla MRI, Dual-Energy CT scans, and high-resolution PET-CT systems is becoming increasingly difficult to process with traditional von Neumann architectures. In modern clinical settings, a single high-resolution volumetric scan can generate several gigabytes of raw data. This data requires intensive signal processing—often taking minutes or even hours—to reconstruct into a format that a radiologist can interpret. In emergency medicine, specifically for stroke or trauma patients, the latency of classical reconstruction algorithms can be the difference between recovery and permanent disability. Quantum computing introduces a fundamental paradigm shift. By leveraging quantum bits (qubits), which utilize the subatomic properties of superposition and entanglement, quantum computers can theoretically perform massive parallel computations that are mathematically impossible for classical binary systems. This paper provides a comprehensive review of these quantum paradigms, comparing their efficiency to classical methods and outlining the path toward clinical implementation. Theoretical Framework: The Physics of Quantum Advantage Classical computers operate on bits, representing a deterministic state of either 0 or 1. In medical imaging, this means algorithms must process pixels or voxels sequentially. Quantum computing utilizes the unique properties of quantum mechanics to alter the fundamental complexity classes of imaging tasks. 2.1 Superposition and Parallelism Unlike a bit, a qubit can exist in a state |⟩=|0⟩+|1⟩, where and are complex probability amplitudes such that ||2+||2=1. This allows n qubits to represent 2n states simultaneously. For a medical image consisting of 10241024 voxels, a quantum system can map the entire state space into a vastly smaller number of physical qubits (approximately 20 qubits for a million voxels). This allows for "Global Optimization," where the computer evaluates all possible image configurations at once to find the one with the least noise. 2.2 Entanglement and Non-Locality Entanglement allows qubits to be correlated in a way that exceeds classical physics. In image processing, this property is being researched to create "Quantum Sensors." These sensors use entangled photons or atoms to detect minute magnetic field variations in MRI far exceeding the sensitivity of classical induction coils. This could lead to MRI machines that do not require massive, expensive superconducting magnets, making them portable and more accessible. 2.3 Quantum Interference and Phase Estimation Quantum interference is used to bias the probability of measuring the correct answer in an algorithm. In image denoising and deconvolution, interference patterns can be engineered to amplify the signal (the anatomical structure) while canceling out the noise (electronic artifacts or motion blur). This process, known as Quantum Phase Estimation (QPE), is a core component of many imaging algorithms that provide a "Quantum Speedup." Applications in Medical Modalities 3.1 Accelerated MRI Reconstruction via Quantum Fourier Transform (QFT) MRI relies on the Fast Fourier Transform (FFT) to convert raw signals from the frequency domain (k-space) into the spatial domain. The classical FFT has a complexity of O(NlogN). In contrast, the Quantum Fourier Transform (QFT) operates with a complexity of O(log2N). This exponential speedup has several critical clinical implications: Real-time Guided Surgery: Surgeons currently rely on static images. QFT could allow for "Live MRI," providing updated feedback during neurosurgery as brain tissue shifts. Pediatric and Geriatric Care: Reducing scan times from 45 minutes to 45 seconds would eliminate the need for anesthesia in children who cannot remain still. Low-Field Imaging: Higher computational power can compensate for lower-quality raw signals, allowing for high-quality images from smaller, less expensive MRI units in rural areas. 3.2 CT and PET Optimization: The Radon Transform CT scans utilize the Radon transform to reconstruct 3D volumes from 2D projections. Classical iterative reconstruction (IR) is computationally expensive and often results in "staircase artifacts." Quantum algorithms, such as the HHL (Harrow-Hassidim-Lloyd) algorithm for linear systems of equations, can solve the massive matrices required for CT reconstruction with logarithmic scaling. This allows for higher resolution while simultaneously reducing the radiation dose required for the patient. 3.3 Comparative Analysis: A Detailed Quantitative Review Table 1: Comparative Analysis: Classical vs. Quantum Computing in Medical Imaging Feature Classical Computing (Binary) Quantum Computing (Qubit) Clinical Impact & Consequences Basic Data Unit Bits (0 or 1) Qubits (Superposition) Exponentially higher data density and state representation. Logic Gates Boolean Logic (AND, OR, NOT) Unitary Gates (Hadamard, CNOT) Ability to explore multiple logical paths simultaneously. Processing Power Sequential or Vectorized (CPU/GPU) Massively Parallel (Global) Enables real-time diagnostic feedback during surgical procedures. MRI Scaling O (N log N) Complexity O (log2 N) Complexity Theoretical 100x speedup in image reconstruction times. Algorithmic Aim Gradient Descent (Local Minima) Quantum Annealing (Global Minima) Superior noise reduction and image clarity by finding the "true" global optimum. Storage & Memory Linear Growth Exponential Growth (2n) Efficient management of high-dimensional datasets like whole-genome sequencing. Radiation Safety Higher Dose (to maintain SNR) Lower Dose (Q-enhanced SNR) Significant reduction in long-term radiation exposure for chronic patients. Biomarker Detection Limited by pixel resolution/noise Enhanced via Quantum Interference Early-stage detection of micro-metastases and neurodegenerative markers. Hardware State Mature (Silicon CMOS) Emerging (NISQ Era) Shift toward hybrid architectures in the immediate clinical future. The transition from classical to quantum paradigms represents a shift from linear deterministic processing to exponential probabilistic optimization. While classical systems are robust and reliable for standard data management, quantum systems address the "computational bottleneck" inherent in high-resolution volumetric imaging (7T MRI, 4D-CT). The primary clinical advantage identified is the acceleration of the Radon and Fourier transforms, which reduces patient wait times and allows for lower-dose diagnostic protocols without sacrificing image quality. Integration with Artificial Intelligence (AI) and Machine Learning The synergy between Quantum Science and Artificial Intelligence Quantum Machine Learning (QML) is a primary focus for current research at institutions like eminent University. 4.1 Quantum Neural Networks (QNN) and Feature Spaces In classical AI, deep learning models often struggle with the "curse of dimensionality" when analyzing 4D imaging datasets. QNNs utilize "Quantum Feature Maps" to project data into a high-dimensional Hilbert space where classes of data (e.g., malignant vs. benign) become linearly separable. This leads to significantly higher sensitivity in automated mammography and lung cancer screening. 4.2 Automated Segmentation and Pathology Detection Automated segmentation involves the computer outlining the boundaries of an organ or a tumor. Classical AI requires thousands of labeled images to learn these boundaries. Preliminary studies suggest that QML algorithms can achieve higher accuracy with 70% less training data because the quantum kernels are inherently better at recognizing complex geometric patterns in biological tissue. Materials Science and Hardware Implementation The transition from theory to clinical practice is a challenge of Materials Science. The qubits must be isolated from all external interference to prevent "decoherence." Superconducting Qubits: Currently favored by IBM and Google, these use Josephson junctions made of superconducting materials like Niobium. They require dilution refrigerators to reach temperatures colder than deep space (15 mK). Trapped Ion Qubits: Utilize individual atoms (li
Multi-modal biomedical imaging has become central to modern diagnostics, offering complementary anatomical, functional, and molecular information through modalities such as MRI, CT, PET, ultrasound, and optical imaging. Yet integrating these heterogeneous datasets remains computationally demanding due to differences in spatial resolution, noise profiles, acquisition dynamics, and high-dimensional feature distributions. This paper investigates AI–quantum hybrid diagnostic systems as an emerging paradigm for multimodal image fusion, reconstruction, and disease classification. By combining deep learning architectures with quantum-enhanced algorithms—including variational quantum circuits, quantum feature encoders, and quantum kernel methods—the hybrid framework aims to accelerate image processing, improve cross-modal consistency, and enhance diagnostic precision. The analysis highlights advancements in quantum-accelerated denoising, multi-modal registration, probabilistic inference, and high-dimensional pattern recognition. It also evaluates workflow integration challenges, such as NISQ-era noise, hardware scalability, and clinical interpretability. Overall, AI–quantum hybrid systems represent a promising frontier in medical imaging, offering potential improvements in speed, sensitivity, and personalized diagnostic accuracy.
Introduction: Quantum computing is increasingly being investigated for integration into medical radiology and healthcare applications worldwide. Given its potential to enhance clinical care and medical research, there is growing interest in evaluating its practical applications in clinical workflows. Methods: We developed an evaluation of quantum computing-based auto-contouring methods to introduce medical physicists to this emerging technology. We implemented existing quantum algorithms as prototypes tailored for specific quantum hardware, focusing on their application to auto-contouring in medical imaging. The evaluation was performed using a medical resonance imaging (MRI) abdominal dataset, comprising 102 patient scans. Results: The quantum algorithms were applied to the dataset and assessed for their potential in auto-contouring tasks. One of the quantum-based auto contouring methods demonstrated conceptual feasibility, practical performance is still limited by current available quantum hardware and scalability constraints. Discussion: Our findings suggest that while quantum computing for auto-contouring shows promise, it remains in its early stages. At present, artificial intelligence-based algorithms continue to be the preferred choice for auto-contouring in treatment planning due to their greater efficiency and accuracy. As quantum hardware and algorithms mature, their integration into clinical workflows may become more viable.