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J. Imaging
Special Issues
Quantum Computing: Techniques and Applications in Medical Image Processing
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Quantum Computing: Techniques and Applications in Medical Image Processing

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: closed (20 April 2023) | Viewed by 8365

Special Issue Editors

Dr. Laszlo Papp

E-Mail Website
Guest Editor
Applied Quantum Computing (AQC) Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
Interests: quantum computing; medical imaging; artificial inteligence; radiomics
Dr. Sasan Moradi

E-Mail Website
Guest Editor
Applied Quantum Computing (AQC) Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
Interests: quantum computing; quantum physics; quantum error correction; classic-quantum data encoding; artificial inteligence

Special Issue Information

Dear Colleagues,

Quantum computing (QC) has been recently in the frontlines of both industrial and academic discussions, that attempt to interpret its current capabilities, its promising results as well as its hype, and sometimes, its anticipated controversies.

We do recognize the advantages of QC and we believe in its transformative potential which will impact various fields that need to deal with computationally-complex problems such as modeling and simulation, optimization, and artificial intelligence (AI). Nevertheless, we also recognize that given its novelty—especially in the field of healthcare— clinicians and quantum computing researchers may feel the engagement with QC in the context of real-life medical imaging and image processing problem domains challenging.

With this special issue, we wish to contribute to the process of shaping the future of QC and medical imaging science by calling for articles that focus on the utilization of quantum computing methodologies within the fields of medical imaging, image reconstruction, image processing, radiomics, and AI. We are particularly interested in articles that aim to solve clinically-relevant problems utilizing medical imaging data and by proposing novel quantum computing methodologies and applications. We are interested to read about QC approaches that deal with, e.g., classic-to-quantum imaging data encoding, error mitigation, quantum circuit optimization, quantum image reconstruction, quantum radiomics, quantum image processing, and manipulation as well as quantum AI for predicting clinical end-points.

Dr. Laszlo Papp
Dr. Sasan Moradi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum computing
  • medical imaging
  • image reconstruction
  • image analysis
  • AI
  • classic-to-quantum data encoding
  • error mitigation
  • circuit optimization

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Published Papers (2 papers)

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22 pages, 4250 KiB  
Article
Exploring the Limitations of Hybrid Adiabatic Quantum Computing for Emission Tomography Reconstruction
by Merlin A. Nau, A. Hans Vija, Wesley Gohn, Maximilian P. Reymann and Andreas K. Maier
J. Imaging 2023, 9(10), 221; https://doi.org/10.3390/jimaging9100221 - 11 Oct 2023
Cited by 2 | Viewed by 2468
Abstract
Our study explores the feasibility of quantum computing in emission tomography reconstruction, addressing a noisy ill-conditioned inverse problem. In current clinical practice, this is typically solved by iterative methods minimizing a L2 norm. After reviewing quantum computing principles, we propose the use [...] Read more.
Our study explores the feasibility of quantum computing in emission tomography reconstruction, addressing a noisy ill-conditioned inverse problem. In current clinical practice, this is typically solved by iterative methods minimizing a L2 norm. After reviewing quantum computing principles, we propose the use of a commercially available quantum annealer and employ corresponding hybrid solvers, which combine quantum and classical computing to handle more significant problems. We demonstrate how to frame image reconstruction as a combinatorial optimization problem suited for these quantum annealers and hybrid systems. Using a toy problem, we analyze reconstructions of binary and integer-valued images with respect to their image size and compare them to conventional methods. Additionally, we test our method’s performance under noise and data underdetermination. In summary, our method demonstrates competitive performance with traditional algorithms for binary images up to an image size of 32×32 on the toy problem, even under noisy and underdetermined conditions. However, scalability challenges emerge as image size and pixel bit range increase, restricting hybrid quantum computing as a practical tool for emission tomography reconstruction until significant advancements are made to address this issue. Full article
(This article belongs to the Special Issue Quantum Computing: Techniques and Applications in Medical Image Processing)
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20 pages, 4934 KiB  
Article
Hybrid Classical–Quantum Transfer Learning for Cardiomegaly Detection in Chest X-rays
by Pierre Decoodt, Tan Jun Liang, Soham Bopardikar, Hemavathi Santhanam, Alfaxad Eyembe, Begonya Garcia-Zapirain and Daniel Sierra-Sosa
J. Imaging 2023, 9(7), 128; https://doi.org/10.3390/jimaging9070128 - 25 Jun 2023
Cited by 5 | Viewed by 4884
Abstract
Cardiovascular diseases are among the major health problems that are likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily for a non-cardiological indication. Based [...] Read more.
Cardiovascular diseases are among the major health problems that are likely to benefit from promising developments in quantum machine learning for medical imaging. The chest X-ray (CXR), a widely used modality, can reveal cardiomegaly, even when performed primarily for a non-cardiological indication. Based on pre-trained DenseNet-121, we designed hybrid classical–quantum (CQ) transfer learning models to detect cardiomegaly in CXRs. Using Qiskit and PennyLane, we integrated a parameterized quantum circuit into a classic network implemented in PyTorch. We mined the CheXpert public repository to create a balanced dataset with 2436 posteroanterior CXRs from different patients distributed between cardiomegaly and the control. Using k-fold cross-validation, the CQ models were trained using a state vector simulator. The normalized global effective dimension allowed us to compare the trainability in the CQ models run on Qiskit. For prediction, ROC AUC scores up to 0.93 and accuracies up to 0.87 were achieved for several CQ models, rivaling the classical–classical (CC) model used as a reference. A trustworthy Grad-CAM++ heatmap with a hot zone covering the heart was visualized more often with the QC option than that with the CC option (94% vs. 61%, p < 0.001), which may boost the rate of acceptance by health professionals. Full article
(This article belongs to the Special Issue Quantum Computing: Techniques and Applications in Medical Image Processing)
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