Volume 10 Issue 2 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 10 · NO. 2 | March 2023

CONTENTS

IN THIS ISSUE

Physics of Medical Imaging (2)

Image Processing (7)

Computer-Aided Diagnosis (2)

Image-Guided Procedures, Robotic Interventions, and Modeling (1)

Image Perception, Observer Performance, and Technology Assessment (3)

PACS and Imaging Informatics (1)

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Physics of Medical Imaging

Terahertz-to-infrared converters for imaging the human skin cancer: challenges and feasibility

Kamil Moldosanov, Alexander Bykov, Nurlanbek Z. Kairyev, Mikhail Khodzitsky, Grigory Kropotov, Valery Lelevkin, Igor Meglinski, Andrei Postnikov, Alexey Shakhmin

Journal of Medical Imaging, Vol. 10, Issue 02, 023501, (March 2023) https://doi.org/10.1117/1.JMI.10.2.023501

TOPICS: Terahertz radiation, Phonons, Infrared cameras, Gold nanoparticles, Cameras, Photons, Gold, Matrices, Infrared imaging, Alloys

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Timing resolution in double-sided silicon photon-counting computed tomography detectors

Christel Sundberg, Mats Persson, Jacob Wikner, Mats Danielsson

Journal of Medical Imaging, Vol. 10, Issue 02, 023502, (March 2023) https://doi.org/10.1117/1.JMI.10.2.023502

TOPICS: Electrodes, Silicon, Signal to noise ratio, Pulse signals, Photodetectors, Optical computing, Silicon photonics, Semiconducting wafers, Computed tomography, Interference (communication)

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Image Processing

Deep-learning approach to automate the segmentation of aorta in non-contrast CTs

Qixiang Ma, Antoine Lucas, Houda Hammami, Huazhong Shu, Adrien Kaladji, Pascal Haigron

Journal of Medical Imaging, Vol. 10, Issue 02, 024001, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024001

TOPICS: Aorta, Image segmentation, 3D modeling, Education and training, Data modeling, Visualization, Feature fusion, Contrast transfer function, Aneurysms, Tissues

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Relational reasoning network for anatomical landmarking

Neslisah Torosdagli, Syed Anwar, Payal Verma, Denise Liberton, Janice Lee, Wade Han, Ulas Bagci

Journal of Medical Imaging, Vol. 10, Issue 02, 024002, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024002

TOPICS: Anatomy, Bone, Machine learning, Electronic filtering, Deformation, Image segmentation, Ruthenium, Cone beam computed tomography, Deep learning, Pathology

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TextureWGAN: texture preserving WGAN with multitask regularizer for computed tomography inverse problems

Masaki Ikuta, Jun Zhang

Journal of Medical Imaging, Vol. 10, Issue 02, 024003, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024003

TOPICS: Education and training, CT reconstruction, Image analysis, Statistical analysis, Matrices, Medical imaging, Image processing, Computed tomography, Gallium nitride, Cooccurrence matrices

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Purpose

This paper presents a deep learning (DL) based method called TextureWGAN. It is designed to preserve image texture while maintaining high pixel fidelity for computed tomography (CT) inverse problems. Over-smoothed images by postprocessing algorithms have been a well-known problem in the medical imaging industry. Therefore, our method tries to solve the over-smoothing problem without compromising pixel fidelity.

Approach

The TextureWGAN extends from Wasserstein GAN (WGAN). The WGAN can create an image that looks like a genuine image. This aspect of the WGAN helps preserve image texture. However, an output image from the WGAN is not correlated to the corresponding ground truth image. To solve this problem, we introduce the multitask regularizer (MTR) to the WGAN framework to make a generated image highly correlated to the corresponding ground truth image so that the TextureWGAN can achieve high-level pixel fidelity. The MTR is capable of using multiple objective functions. In this research, we adopt a mean squared error (MSE) loss to maintain pixel fidelity. We also use a perception loss to improve the look and feel of result images. Furthermore, the regularization parameters in the MTR are trained along with generator network weights to maximize the performance of the TextureWGAN generator.

Results

The proposed method was evaluated in CT image reconstruction applications in addition to super-resolution and image-denoising applications. We conducted extensive qualitative and quantitative evaluations. We used PSNR and SSIM for pixel fidelity analysis and the first-order and the second-order statistical texture analysis for image texture. The results show that the TextureWGAN is more effective in preserving image texture compared with other well-known methods such as the conventional CNN and nonlocal mean filter (NLM). In addition, we demonstrate that TextureWGAN can achieve competitive pixel fidelity performance compared with CNN and NLM. The CNN with MSE loss can attain high-level pixel fidelity, but it often damages image texture.

Conclusions

TextureWGAN can preserve image texture while maintaining pixel fidelity. The MTR is not only helpful to stabilize the TextureWGAN’s generator training but also maximizes the generator performance.

CROPro: a tool for automated cropping of prostate magnetic resonance images

Alexandros Patsanis, Mohammed R. S. Sunoqrot, Tone F. Bathen, Mattijs Elschot

Journal of Medical Imaging, Vol. 10, Issue 02, 024004, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024004

TOPICS: Prostate, Education and training, Magnetic resonance imaging, Image segmentation, Performance modeling, Deep learning, Magnetism, Principal component analysis, Image classification, Visual process modeling

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Self-supervised denoising of Nyquist-sampled volumetric images via deep learning

Matthew B. Applegate, Kivanc Kose, Sandesh Ghimire, Milind Rajadhyaksha, Jennifer Dy

Journal of Medical Imaging, Vol. 10, Issue 02, 024005, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024005

TOPICS: Denoising, Education and training, Sampling rates, Deep learning, Error analysis, Point spread functions, Optical coherence tomography, Image restoration, Biomedical optics, Machine learning

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Multiscale denoising generative adversarial network for speckle reduction in optical coherence tomography images

Xiaojun Yu, Chenkun Ge, Mingshuai Li, Muhammad Zulkifal Aziz, Jianhua Mo, Zeming Fan

Journal of Medical Imaging, Vol. 10, Issue 02, 024006, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024006

TOPICS: Optical coherence tomography, Image processing, Denoising, Speckle, Education and training, Signal to noise ratio, Optical coherence, Gallium nitride, Image quality, Visualization

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Evaluating semi-supervision methods for medical image segmentation: applications in cardiac magnetic resonance imaging

Sarah M. Hooper, Sen Wu, Rhodri H. Davies, Anish Bhuva, Erik B. Schelbert, James C. Moon, Peter Kellman, Hui Xue, Curtis Langlotz, Christopher Ré

Journal of Medical Imaging, Vol. 10, Issue 02, 024007, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024007

TOPICS: Image segmentation, Education and training, Data modeling, Medical imaging, Performance modeling, Machine learning, Current controlled current source, Cardiovascular magnetic resonance imaging, Ablation, Heart

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Computer-Aided Diagnosis

Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images

Anush Agarwal, Gaikar Rohini, Schieda Nicola, Elfaal Mohamed WalaaEldin, Ukwatta Eranga

Journal of Medical Imaging, Vol. 10, Issue 02, 024501, (March 2023) https://doi.org/10.1117/1.JMI.10.2.024501

TOPICS: Kidney, Image segmentation, Magnetic resonance imaging, Cancer detection, Data modeling, Solids, Education and training, Tumor growth modeling, Tissues, 3D modeling

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Automatic assessment of mammographic density using a deep transfer learning method

Steven Squires, Elaine Harkness, Dafydd Gareth Evans, Susan Astley

Journal of Medical Imaging, Vol. 10, Issue 02, 024502, (April 2023) https://doi.org/10.1117/1.JMI.10.2.024502

TOPICS: Education and training, Breast density, Deep learning, Data modeling, Mammography, Feature extraction, Linear regression, Performance modeling, Cancer, Image processing

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Image-Guided Procedures, Robotic Interventions, and Modeling

HoloLens augmented reality system for transperineal free-hand prostate procedures

Ming Li, Sherif Mehralivand, Sheng Xu, Nicole Varble, Ivane Bakhutashvili, Sandeep Gurram, Peter A. Pinto, Peter Choyke, Bradford Wood, Baris Turkbey

Journal of Medical Imaging, Vol. 10, Issue 02, 025001, (March 2023) https://doi.org/10.1117/1.JMI.10.2.025001

TOPICS: Augmented reality, Prostate, Magnetic resonance imaging, Autoregressive models, Visualization, Computed tomography, Cameras, Biopsy, 3D displays, Anatomy

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Image Perception, Observer Performance, and Technology Assessment

Comparison of image quality assessments between interventional radiographers and interventional radiologists using digital subtraction angiography

Don Nocum, John Robinson, Mark Halaki, Magnus Båth, Nejc Mekiš, Eisen Liang, Nadine Thompson, Michelle Moscova, Warren Reed

Journal of Medical Imaging, Vol. 10, Issue 02, 025501, (March 2023) https://doi.org/10.1117/1.JMI.10.2.025501

TOPICS: Image quality, Radiography, Visualization, Angiography, Anatomy, Visual analytics, Statistical analysis, Medical imaging, Image-guided intervention, Arteries

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Variations of image interpretations of radiologists from different populations in mammography and tomosynthesis with different levels of breast density

Phuong Dung Trieu, Qin Xiao, Yajia Gu, Sarah J. Lewis, Melissa L. Barron, Kriscia Tapia, Patrick C. Brennan, Tong Li

Journal of Medical Imaging, Vol. 10, Issue 02, 025502, (March 2023) https://doi.org/10.1117/1.JMI.10.2.025502

TOPICS: Digital breast tomosynthesis, Breast density, Mammography, Breast, Cancer, Education and training, Diagnostics, Breast cancer, Cancer detection, Radiology

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Purpose

This study aims to investigate the diagnostic performances of Australian and Shanghai-based Chinese radiologists in reading full-field digital mammogram (FFDM) and digital breast tomosynthesis (DBT) with different levels of breast density.

Approach

Eighty-two Australian radiologists interpreted a 60-case FFDM set, and 29 radiologists also reported a 35-case DBT set. Sixty Shanghai radiologists read the same FFDM set, and 32 radiologists read the DBT set. The diagnostic performances of Australian and Shanghai radiologists were assessed using truth data (cancer cases were biopsy proven) and compared overall in specificity, case sensitivity, lesion sensitivity, receiver operating characteristics (ROC) area under the curve, and jack-knife free-response receiver operating characteristics (JAFROC) figure of merit, and they were stratified by case characteristics using the Mann–Whitney U test. The Spearman rank test was used to explore the association between radiologists’ performances and their work experience in mammogram interpretation.

Results

There were significantly higher performances of Australian radiologists compared with Shanghai radiologists in low breast density for case sensitivity, lesion sensitivity, ROC, and JAFROC in the FFDM set (P < 0.0001); in high breast density, Shanghai radiologists’ performances in lesion sensitivity and JAFROC were also lower than Australian radiologists (P < 0.0001). In the DBT test set, Australian radiologists performed better than Shanghai radiologists in cancer detection in both low and high breast density. The work experience of Australian radiologists was positively linked to their diagnostic performances, whereas this association was not statistically significant in Shanghai radiologists.

Conclusion

There were significant variations in reading performances between Australian and Shanghai radiologists in FFDM and DBT across different levels of breast density, lesion types, and lesion sizes. An effective training initiative tailored to suit local readers is essential to enhancing the diagnostic accuracy of Shanghai radiologists.

Pathologist pupil dilation reflects experience level and difficulty in diagnosing medical images

Trafton Drew, Catherine E. Konold, Mark Lavelle, Tad T. Brunyé, Kathleen F. Kerr, Hannah Shucard, Donald L. Weaver, Joann G. Elmore

Journal of Medical Imaging, Vol. 10, Issue 02, 025503, (April 2023) https://doi.org/10.1117/1.JMI.10.2.025503

TOPICS: Diagnostics, Pathology, Biopsy, Education and training, Eye tracking, Medical imaging, Reflection, Windows, Breast, Signal detection

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Purpose: Digital whole slide imaging allows pathologists to view slides on a computer screen instead of under a microscope. Digital viewing allows for real-time monitoring of pathologists’ search behavior and neurophysiological responses during the diagnostic process. One particular neurophysiological measure, pupil diameter, could provide a basis for evaluating clinical competence during training or developing tools that support the diagnostic process. Prior research shows that pupil diameter is sensitive to cognitive load and arousal, and it switches between exploration and exploitation of a visual image. Different categories of lesions in pathology pose different levels of challenge, as indicated by diagnostic disagreement among pathologists. If pupil diameter is sensitive to the perceived difficulty in diagnosing biopsies, eye-tracking could potentially be used to identify biopsies that may benefit from a second opinion.

Approach: We measured case onset baseline-corrected (phasic) and uncorrected (tonic) pupil diameter in 90 pathologists who each viewed and diagnosed 14 digital breast biopsy cases that cover the diagnostic spectrum from benign to invasive breast cancer. Pupil data were extracted from the beginning of viewing and interpreting of each individual case. After removing 122 trials (<10 % ) with poor eye-tracking quality, 1138 trials remained. We used multiple linear regression with robust standard error estimates to account for dependent observations within pathologists.

Results: We found a positive association between the magnitude of phasic dilation and subject-centered difficulty ratings and between the magnitude of tonic dilation and untransformed difficulty ratings. When controlling for case diagnostic category, only the tonic-difficulty relationship persisted.

Conclusions: Results suggest that tonic pupil dilation may indicate overall arousal differences between pathologists as they interpret biopsy cases and could signal a need for additional training, experience, or automated decision aids. Phasic dilation is sensitive to characteristics of biopsies that tend to elicit higher difficulty ratings and could indicate a need for a second opinion.

PACS and Imaging Informatics

Three-dimensional printing accuracy analysis for medical applications across a wide variety of printers

Joshua Ray Chen, Jonathan Morris, Adam Wentworth, Victoria Sears, Andrew Duit, Eric Erie, Kiaran McGee, Shuai Leng

Journal of Medical Imaging, Vol. 10, Issue 02, 026501, (April 2023) https://doi.org/10.1117/1.JMI.10.2.026501

TOPICS: Printing, 3D printing, 3D modeling, Biomedical applications, Photopolymerization, Extrusion, Data modeling, Standards development, Anatomy, Accuracy assessment

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