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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 8418

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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Research

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 2482
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)
►▼ Show Figures

Figure 1

Figure 1
<p>Graphical Abstract: Simulation and reconstruction of a two-view binary tomographic problem using hybrid quantum annealing.</p> Full article ">Figure 2
<p>Graphs and physical embeddings on the QPU for binary reconstruction problems. (<b>a</b>,<b>c</b>) depict the directed graph for binary tomographic problems with image sizes of <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>×</mo> <mn>4</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>8</mn> <mo>×</mo> <mn>8</mn> </mrow> </semantics></math>, respectively. (<b>b</b>,<b>d</b>) show the corresponding embedding of the directed graph on the QPU topology for (<b>a</b>,<b>c</b>), respectively.</p> Full article ">Figure 3
<p>Binary reconstructions of sample image ‘foam’ (<b>a</b>) and ‘tree’ (<b>b</b>) for image sizes <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>×</mo> <mi>N</mi> </mrow> </semantics></math>, where <span class="html-italic">N</span> is 4, 8, 16, and 32.</p> Full article ">Figure 4
<p>Mean and variance RMSE (<b>a</b>) and SSIM (<b>b</b>) evaluation of images ‘foam’, ‘molecule’, ‘snowflake’ and ‘tree’ for image sizes <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>×</mo> <mi>N</mi> </mrow> </semantics></math>, where <span class="html-italic">N</span> is 4, 8, 16, and 32.</p> Full article ">Figure 5
<p>4-bit integer reconstructions of Shepp–Logan phantom for image sizes <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>×</mo> <mi>N</mi> </mrow> </semantics></math>, where <span class="html-italic">N</span> is 4, 8, 16, and 32.</p> Full article ">Figure 6
<p>RMSE (<b>a</b>) and SSIM (<b>b</b>) of 4-bit integer reconstructions of the Shepp–Logan phantom for image sizes <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>×</mo> <mi>N</mi> </mrow> </semantics></math>, where <span class="html-italic">N</span> is 4, 8, 16, and 32.</p> Full article ">Figure 7
<p>4-bit integer reconstructions of four digits from the UCI digits dataset without (<b>a</b>) and with random noise (<b>b</b>).</p> Full article ">Figure 8
<p>Comparison of RMSE and SSIM for 4-bit integer reconstructions of 32 images from the UCI digits dataset. (<b>a</b>,<b>b</b>) show results without noise, while (<b>c</b>,<b>d</b>) show results with noise.</p> Full article ">Figure 9
<p>Binary reconstruction of the <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> </mrow> </semantics></math> image ‘foam’ (<b>a</b>) and ‘tree’ (<b>b</b>) from 2, 4, and 32 views.</p> Full article ">Figure 10
<p>RMSE (<b>a</b>) and SSIM (<b>b</b>) of binary reconstructions from 2, 4, and 32 views.</p> Full article ">Figure 11
<p>Outside of the inverse-crime scenario: Binary reconstructions of sample image ‘foam’ (<b>a</b>) and ‘tree’ (<b>b</b>) for image size <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> </mrow> </semantics></math>. The projections are simulated on an upscaled higher-resolution image <math display="inline"><semantics> <mrow> <mn>128</mn> <mo>×</mo> <mn>128</mn> </mrow> </semantics></math> of the input, and the sinogram is consequently rebinned.</p> Full article ">Figure A1
<p>Binary reconstructions of sample image ‘snowflake’ (<b>a</b>) and ’molecule’ (<b>b</b>) for image sizes <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>×</mo> <mi>N</mi> </mrow> </semantics></math>, where <span class="html-italic">N</span> is 4, 8, 16, and 32.</p> Full article ">Figure A2
<p>Gradual analysis of the Shepp–Logan phantom. The y-axis shows the number of components (discretization values) in the Shepp–Logan phantom.</p> Full article ">Figure A3
<p>4-bit integer reconstructions of digits from the UCI digits dataset.</p> Full article ">Figure A4
<p>4-bit integer reconstructions of digits from the UCI digits dataset.</p> Full article ">Figure A5
<p>4-bit integer reconstructions of digits from the UCI digits dataset.</p> Full article ">Figure A6
<p>4-bit integer reconstructions of digits from the UCI digits dataset.</p> Full article ">Figure A7
<p>4-bit integer reconstructions of digits from the UCI digits dataset with random noise.</p> Full article ">Figure A8
<p>4-bit integer reconstructions of digits from the UCI digits dataset with random noise.</p> Full article ">Figure A9
<p>4-bit integer reconstructions of digits from the UCI digits dataset with random noise.</p> Full article ">Figure A10
<p>4-bit integer reconstructions of digits from the UCI digits dataset with random noise.</p> Full article ">Figure A11
<p>Binary reconstruction of the <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> </mrow> </semantics></math> image ‘molecule’ (<b>a</b>) and ‘snowflake’ (<b>b</b>) from 2, 4, and 32 views.</p> Full article ">Figure A12
<p>Outside of the inverse-crime scenario: Binary reconstructions of sample image ‘molecule’ (<b>a</b>) and ‘snowflake’ (<b>b</b>) for image size <math display="inline"><semantics> <mrow> <mn>32</mn> <mo>×</mo> <mn>32</mn> </mrow> </semantics></math>. The projections are simulated on an upscaled higher-resolution image <math display="inline"><semantics> <mrow> <mn>128</mn> <mo>×</mo> <mn>128</mn> </mrow> </semantics></math> of the input, and the sinogram is consequently rebinned.</p> Full article ">
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 4911
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)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Images from the cardiomegaly subset along with their counterpart from the control subset. First column: no positive label for any other finding. Three last columns: cases of pleural effusion, edema and lung opacity, which were the findings most frequently associated with cardiomegaly in the dataset.</p> Full article ">Figure 2
<p>High-level model design. Six CXRs are represented (<b>A</b>–<b>F</b>) to describe the process output. Cardiomegaly is detected in (<b>A</b>,<b>C</b>,<b>F</b>).</p> Full article ">Figure 3
<p>Training models for classification: On the left, a model based on pre-trained DenseNet-121. On the right, a model based on pre-trained AlexNet. In both versions, the flowchart forks into the classical and quantum versions of the trainable classifier. n: number of qubits.</p> Full article ">Figure 4
<p>Qiskit rendering of the PQC with four qubits. After initialization in the ground state, all qubits are first placed in a superposition state by applying Hadamard gates (H). A feature map is produced by encoding each qubit by a φ rotation around the y-axis (Ry gates). Then, the ansatz consists of a series of entanglement by 2-qubit CNOT gates, each followed by a θ rotation around the <span class="html-italic">y</span>-axis at a quantum depth of 4.</p> Full article ">Figure 5
<p>ROC curves obtained by 10-fold cross-validation in four CC models (test set).</p> Full article ">Figure 6
<p>ROC curves obtained by 10-fold cross-validation in five CQ models (test set).</p> Full article ">Figure 7
<p>Original CXRs (left) along with the corresponding Grad-CAM++ heatmaps obtained with the last convolutional layer from the three models compared for trusworthiness. (<b>a</b>): Normal heart. Large hot zone including the heart with the CC model, hot zones covering the heart with the CQ models. (<b>b</b>): Cardiomegaly and artificial pacemaker. Hot zones covering the heart with the three models. (<b>c</b>): Cardiomegaly. Hot zone in the right lung base for the CC model (example of non-trustworthy heatmap), hot zones covering the heart for the CQ models.</p> Full article ">Figure 8
<p>(<b>a</b>) NGED for the quantum layer in the classifier in Qiskit four-qubit models with four-dimensional (4-dim) and two-dimensional output (2-dim), each with 24 trainable parameters. (<b>b</b>) Training loss curves observed in these models with and without freezer.</p> Full article ">Figure A1
<p>ROC curves for the CC models by 70/30 train–test split: (<b>a</b>) Training set. (<b>b</b>) Test set.</p> Full article ">Figure A2
<p>ROC curves for the QC models by 70/30 train–test split: (<b>a</b>) Training set. (<b>b</b>) Test set.</p> Full article ">Figure A3
<p>Training loss curves and standard deviation for the CC (<b>a</b>) and QC (<b>b</b>) models.</p> Full article ">Figure A4
<p>Upper left: confusion matrix for the training set observed for the CC model with Densenet 121 as a feature extractor. Upper right box: two CXRs labeled as control and predicted cardiomegaly. Lower box: 9 CXRs labeled as cardiomegaly and predicted control.</p> Full article ">
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