Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Accelerated Magnetic Resonance Imaging via Quantum Computing and Deep Neural Network
Motivation: Applying quantum computing to the reconstruction of magnetic resonance imaging. Goal(s): Improving the quality of magnetic resonance reconstruction images by introducing quantum computing. Approach: Building a quantum hybrid classical neural network for undersampling magnetic resonance reconstruction. Results: The hybrid classical neural network achieved better accuracy by introducing quantum convolutional layers and verified the feasibility of applying quantum computing to magnetic resonance reconstruction. Impact: This fully demonstrates the potential and broad development prospects of quantum computing in accelerating magnetic resonance imaging.
Motivation: Applying quantum computing to the reconstruction of magnetic resonance imaging. Goal(s): Improving the quality of magnetic resonance reconstruction images by introducing quantum computing. Approach: Building a quantum hybrid classical neural network for undersampling magnetic resonance reconstruction. Results: The hybrid classical neural network achieved better accuracy by introducing quantum convolutional layers and verified the feasibility of applying quantum computing to magnetic resonance reconstruction. Impact: This fully demonstrates the potential and broad development prospects of quantum computing in accelerating magnetic resonance imaging.
Magnetic resonance image reconstruction starting from undersampled k-space data requires the recovery of many potential nonlinear features, which is very difficult for algorithms to recover these features. In recent years, the development of quantum computing has discovered that quantum convolution can improve network accuracy, possibly due to potential quantum advantages. This article proposes a hybrid neural network containing quantum and classical networks for fast magnetic resonance imaging, and conducts experiments on a quantum computer simulation system. The experimental results indicate that the hybrid network has achieved excellent reconstruction results, and also confirm the feasibility of applying hybrid quantum-classical neural networks into the image reconstruction of rapid magnetic resonance imaging.
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.
Magnetic resonance imaging (MRI) is a common technique to scan brains for strokes, tumors, and other abnormalities that cause forms of dementia. However, correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3 patients with Alzheimer's were misdiagnosed in 2019, an issue neural networks can rectify. The performance of these neural networks have been shown to be improved by applying quantum algorithms. This proposed neural network architecture uses a pooling layer, which reduces the number of features to obtain an expectation value by implementing a variational quantum circuit. Using a testing set of 1000 normal and 1000 dementia MRI images, the QCCNN detected normal and demented images correctly 98.5 percent and 99.3 percent of the time, compared to the CNN accuracies of 93.6 percent and 95.2 percent. With hospitals beginning to adopt machine learning applications for biomedical image detection, this proposed architecture would improve accuracies and speeds.
The classical neural network has provided remarkable results to diagnose neurological disorders against neuroimaging data. However, in terms of efficient and accurate classification, some standpoints need to be improved by utilizing high-speed computing tools. By integrating quantum computing phenomena with deep neural network approaches, this study proposes an AlexNet–quantum transfer learning method to diagnose neurodegenerative diseases using magnetic resonance imaging (MRI) dataset. The hybrid model is constructed by extracting an informative feature vector from high-dimensional data using a classical pre-trained AlexNet model and further feeding this network to a quantum variational circuit (QVC). Quantum circuit leverages quantum computing phenomena, quantum bits, and different quantum gates such as Hadamard and CNOT gate for transformation. The classical pre-trained model extracts the 4096 features from the MRI dataset by using AlexNet architecture and gives this vector as input to the quantum circuit. QVC generates a 4-dimensional vector and to transform this vector into a 2-dimensional vector, a fully connected layer is connected at the end to perform the binary classification task for a brain disorder. Furthermore, the classical–quantum model employs the quantum depth of six layers on pennyLane quantum simulators, presenting the classification accuracy of 97% for Parkinson’s disease (PD) and 96% for Alzheimer’s disease (AD) for 25 epochs. Besides this, pre-trained classical neural models are implemented for the classification of disorder and then, we compare the performance of the classical transfer learning model and hybrid classical–quantum transfer learning model. This comparison shows that the AlexNet–quantum learning model achieves beneficial results for classifying PD and AD. So, this work leverages the high-speed computational power using deep network learning and quantum circuit learning to offer insight into the practical application of quantum computers that speed up the performance of the model on real-world data in the healthcare domain.
Magnetic resonance imaging (MRI) is a common technique to scan brains for strokes, tumors, and other abnormalities that cause forms of dementia. However, correctly diagnosing forms of dementia from MRIs is difficult, as nearly 1 in 3 patients with Alzheimer's were misdiagnosed in 2019, an issue neural networks can rectify. The performance of these neural networks have been shown to be improved by applying quantum algorithms. This proposed novel neural network architecture uses a fully-connected (FC) layer, which reduces the number of features to obtain an expectation value by implementing a variational quantum circuit (VQC). This study found that the proposed hybrid quantum-classical convolutional neural network (QCCNN) detected normal and demented images correctly 95 and 98 percent of the time, compared to the CNN accuracies of 89 and 91 percent. With more hospitals beginning to adopt machine learning applications for biomedical image detection, this proposed architecture would approve accuracies and potentially save more lives. Furthermore, the proposed architecture is generally flexible, and can be used for transfer-learning tasks, saving time and resources.
Abstract Accurate detection of multiple sclerosis (MS) lesions in magnetic resonance imaging (MRI) requires robust deep learning models to capture subtle spatial and textural features. We introduce hybrid quantum-classical transfer learning algorithms for MS classification using axial and sagittal MRI scans, combining classical convolutional neural networks (CNNs) including EfficientNetB3, ResNet50, DenseNet121 with parameterized quantum circuits to enhance feature representation via entanglement and quantum-specific non-linearities. Quantum layers are trained end-to-end with classical backbones via backpropagation, enabling seamless integration of quantum-enhanced features. For axial MRI, QResNet50 achieved a high accuracy of 97.58% and AUC of 99.31%, while QDenseNet121 reached 97.28% accuracy and 99.13% AUC. For sagittal MRI, classical ResNet50 excelled with 99.15% accuracy and 99.93% AUC, while QEfficientNetB3 improved accuracy (97.46% to 98.30%) but reduced AUC (99.51% to 99.32%), and QDenseNet121 achieved 98.87% accuracy and 99.83% AUC. Hybrid models showed mixed results, with QCNN underperforming, suggesting quantum benefits are architecture-dependent. Despite simulated quantum circuits mitigating hardware limitations, our results demonstrate the potential to enhance diagnostic performance in specific architectures. This work clarifies a foundational step toward quantum-enhanced deep learning for clinical applications, opening research directions in quantum-aware transfer learning and error mitigation for biomedical imaging.