Volume 10 Issue S1 | Journal of Medical Imaging

Journal of Medical Imaging

VOL. 10 · NO. S1 | February 2023

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Special Issue on Medical Image Perception and Observer Performance (18)

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Special Issue on Medical Image Perception and Observer Performance

Special Issue Editorial: Medical Image Perception and Observer Performance

Karla Evans, Asli Kumcu, Elizabeth Krupinski

Journal of Medical Imaging, Vol. 10, Issue S1, S11901, (August 2023) https://doi.org/10.1117/1.JMI.10.S1.S11901

TOPICS: Medical imaging, Education and training, Mammography, Visualization, Image quality, Image processing, Radiology, Medical research, Diagnostics, Performance modeling

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Eye-tracking differences between free text and template radiology reports: a pilot study

DeAngelo Harris, David Yousem, Elizabeth Krupinski, Mina Motaghi

Journal of Medical Imaging, Vol. 10, Issue S1, S11902, (February 2023) https://doi.org/10.1117/1.JMI.10.S1.S11902

TOPICS: Eye tracking, Radiology, Displays, Education and training, Magnetic resonance imaging, Eye, Radiography, Diagnostics, Brain, Neuroimaging

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Purpose

One possible limitation of structured template radiology reports is that radiologists look back and forth between viewing and dictation monitors, thereby impacting the length of time spent reviewing images and generating a report. We hypothesize that the total time spent viewing case images is diminished and/or the total time spent creating a report is prolonged when the report is generated using a structured template compared with free text format.

Approach

Three neuroradiologists and three senior residents viewed five brain magnetic resonance imaging cases with unique findings while eye position was recorded. Participants generated reports for each case utilizing both structured templates and free text dictation. The time spent viewing images was compared with the time spent looking at the dictation screen.

Results

The two main hypotheses were confirmed: the total time viewing images diminished with templates versus free text dictation and the total time to create a report was prolonged with templates. The mean time (s) spent on the “image” region of interest approached statistical significance as a function of the report type [free: attendings = 236.79 (154.43), residents = 223.55 (77.79); template: attendings = 163.40 (73.42), residents = 182.48 (77.47)] and was overall lower with the template reporting for both attendings and residents (F = 3.77, p = 0.0623), but it did not differ as a function of seniority (F = 0.017, p = 0.8977).

Conclusions

Template-based radiology reports have significant potential to alter the way radiologists view images and report on them, spending more time viewing the report monitor rather than diagnostic images compared with free text dictation. Many radiologists prefer templates for reporting as the structured format may aid in conducting a more systematic or thorough search for findings, although prior work on this assumption is mixed. Future eye-tracking studies could further elucidate whether and how templates and free reports impact the detection and classification of radiographic findings.

E pluribus unum: prospective acceptability benchmarking from the Contouring Collaborative for Consensus in Radiation Oncology crowdsourced initiative for multiobserver segmentation

Diana Lin, Kareem Wahid, Benjamin Nelms, Renjie He, Mohamed Naser, Simon Duke, Michael Sherer, John Christodouleas, Abdallah S. R. Mohamed, Michael Cislo, James Murphy, Clifton Fuller, Erin Gillespie

Journal of Medical Imaging, Vol. 10, Issue S1, S11903, (February 2023) https://doi.org/10.1117/1.JMI.10.S1.S11903

TOPICS: Image segmentation, Breast, Radiation oncology, Diseases and disorders, Radiotherapy, Tumors, Neck, Lymph nodes, Head, Cancer

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UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images

Federico Valeri, Maurizio Bartolucci, Elena Cantoni, Roberto Carpi, Evaristo Cisbani, Ilaria Cupparo, Sandra Doria, Cesare Gori, Mauro Grigioni, Lorenzo Lasagni, Alessandro Marconi, Lorenzo Nicola Mazzoni, Vittorio Miele, Silvia Pradella, Guido Risaliti, Valentina Sanguineti, Diego Sona, Letizia Vannucchi, Adriana Taddeucci

Journal of Medical Imaging, Vol. 10, Issue S1, S11904, (March 2023) https://doi.org/10.1117/1.JMI.10.S1.S11904

TOPICS: Computed tomography, Education and training, Molybdenum, Mathematical optimization, Image restoration, Infrared imaging, Image quality, Performance modeling, X-ray computed tomography, Medical image reconstruction

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RADHunters: gamification in radiology perceptual education

Soham Banerjee, Rishabh Agarwal, William F. Auffermann

Journal of Medical Imaging, Vol. 10, Issue S1, S11905, (March 2023) https://doi.org/10.1117/1.JMI.10.S1.S11905

TOPICS: Education and training, Radiology, Radiography, Perceptual learning, Chest imaging, Video, Statistical analysis, Medical imaging, Web 2.0 technologies, Printing

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Gaze patterns reflect and predict expertise in dynamic echocardiographic imaging

Jochen Laubrock, Alexander Krutz, Jonathan Nübel, Sebastian Spethmann

Journal of Medical Imaging, Vol. 10, Issue S1, S11906, (March 2023) https://doi.org/10.1117/1.JMI.10.S1.S11906

TOPICS: Diagnostics, Video, Echocardiography, Control systems, Heart, Eye tracking, Eye, Visualization, Reflection, Education and training

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Perceptual training and teaching medical students how to window and level chest radiographs

William Auffermann, Megan Mills

Journal of Medical Imaging, Vol. 10, Issue S1, S11907, (April 2023) https://doi.org/10.1117/1.JMI.10.S1.S11907

TOPICS: Education and training, Medical imaging, Windows, Chest imaging, Radiology, Perceptual learning, Chest, Error analysis, Medicine, Radiography

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Assessing saliency models of observers’ visual attention on acquired facial differences

Haoqi Wang, Krista Nicklaus, Eloise Jewett, Eeshaan Rehani, Tzuan A. Chen, Jeff Engelmann, Mary Catherine Bordes, Deepti Chopra, Gregory P. Reece, Z-Hye Lee, Mia K. Markey

Journal of Medical Imaging, Vol. 10, Issue S1, S11908, (April 2023) https://doi.org/10.1117/1.JMI.10.S1.S11908

TOPICS: Photography, Eye models, Cancer, Visualization, Performance modeling, Artificial neural networks, Head, Neck, Eye, Tumor growth modeling

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Visual adaptation to medical images: a comparison of digital mammography and tomosynthesis

Mohana Kuppuswamy Parthasarathy, Margarita L. Zuley, Andriy I. Bandos, Craig K. Abbey, Michael A. Webster

Journal of Medical Imaging, Vol. 10, Issue S1, S11909, (April 2023) https://doi.org/10.1117/1.JMI.10.S1.S11909

TOPICS: Medical imaging, Digital breast tomosynthesis, Visualization, Digital mammography, Digital imaging, Image processing, Breast, Tomosynthesis, Mammography, Imaging systems

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Purpose

Radiologists and other image readers spend prolonged periods inspecting medical images. The visual system can rapidly adapt or adjust sensitivity to the images that an observer is currently viewing, and previous studies have demonstrated that this can lead to pronounced changes in the perception of mammogram images. We compared these adaptation effects for images from different imaging modalities to explore both general and modality-specific consequences of adaptation in medical image perception.

Approach

We measured perceptual changes induced by adaptation to images acquired by digital mammography (DM) or digital breast tomosynthesis (DBT), which have both similar and distinct textural properties. Participants (nonradiologists) adapted to images from the same patient acquired from each modality or for different patients with American College of Radiology—Breast Imaging Reporting and Data System (BI-RADS) classification of dense or fatty tissue. The participants then judged the appearance of composite images formed by blending the two adapting images (i.e., DM versus DBT or dense versus fatty in each modality).

Results

Adaptation to either modality produced similar significant shifts in the perception of dense and fatty textures, reducing the salience of the adapted component in the test images. In side-by-side judgments, a modality-specific adaptation effect was not observed. However, when the images were directly fixated during adaptation and testing, so that the textural differences between the modalities were more visible, significantly different changes in the sensitivity to the noise in the images were observed.

Conclusions

These results confirm that observers can readily adapt to the visual properties or spatial textures of medical images in ways that can bias their perception of the images, and that adaptation can also be selective for the distinctive visual features of images acquired by different modalities.

Visual hindsight bias for abnormal mammograms in radiologists

Hayden M. Schill, Samantha M. Gray, Timothy F. Brady

Journal of Medical Imaging, Vol. 10, Issue S1, S11910, (May 2023) https://doi.org/10.1117/1.JMI.10.S1.S11910

TOPICS: Visualization, Mammography, Decision making, Medical imaging, Information visualization, Image enhancement, Radiology, Medicine, Diagnostics, Medical research

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Contributions of global and local processing on medical image perception

Hyung-Bum Park, Lilian Azer, Shinhae Ahn, Tam-Dan Dinh, Gabriela Macias, Gavin Zhang, Bihong Beth Chen, Huiyan Ma, Mahesh Botejue, Eric Choi, Weiwei Zhang

Journal of Medical Imaging, Vol. 10, Issue S1, S11911, (May 2023) https://doi.org/10.1117/1.JMI.10.S1.S11911

TOPICS: Image processing, Mammography, Signal processing, Medical imaging, Visual process modeling, Data modeling, Signal detection, Visualization, Cancer detection, Modeling

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Signals of global symmetry are important for abnormality detection in mammograms

Cameron Kyle-Davidson, Lyndon L. Rakusen, Emma Raat, Roisin Bradley, Karla K. Evans

Journal of Medical Imaging, Vol. 10, Issue S1, S11912, (May 2023) https://doi.org/10.1117/1.JMI.10.S1.S11912

TOPICS: Mammography, Signal detection, Cancer detection, Neural networks, Education and training, Breast, Breast density, Cancer, Tunable filters, Linear filtering

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Image quality assessment of advanced reconstruction algorithm for point-of-care MRI scanner

Elizabeth A. Krupinski, DeAngelo Harris, Lori R. Arlinghaus, Jo Schlemper, Michal Sofka

Journal of Medical Imaging, Vol. 10, Issue S1, S11913, (May 2023) https://doi.org/10.1117/1.JMI.10.S1.S11913

TOPICS: Image quality, Magnetic resonance imaging, Ischemic stroke, Image restoration, Diagnostics, Reconstruction algorithms, Scanners, Radiology, Portability, Point-of-care devices

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Purpose

Portable magnetic resonance imaging (pMRI) has potential to rapidly acquire images at the patients’ bedside to improve access in locations lacking MRI devices. The scanner under consideration has a magnetic field strength of 0.064 T, thus image-processing algorithms to improve image quality are required. Our study evaluated pMRI images produced using a deep learning (DL)-based advanced reconstruction scheme to improve image quality by reducing image blurring and noise to determine if diagnostic performance was similar to images acquired at 1.5 T.

Approach

Six radiologists viewed 90 brain MRI cases (30 acute ischemic stroke (AIS), 30 hemorrhage, 30 no lesion) with T1, T2, and fluid attenuated inversion recovery sequences, once using standard of care (SOC) images (1.5 T) and once using pMRI DL-based advanced reconstruction images. Observers provided a diagnosis and decision confidence. Time to review each image was recorded.

Results

Receiver operating characteristic area under the curve revealed overall no significant difference (p = 0.0636) between pMRI and SOC images. Examining each abnormality, for acute ischemic stroke, there was a significant difference (p = 0.0042) with SOC better than pMRI; but for hemorrhage, there was no significant difference (p = 0.1950). There was no significant difference in viewing time for pMRI versus SOC (p = 0.0766) or abnormality (p = 0.3601).

Conclusions

The deep learning (DL)-based reconstruction scheme to improve pMRI was successful for hemorrhage, but for acute ischemic stroke the scheme could still be improved. For neurocritical care especially in remote and/or resource poor locations, pMRI has significant clinical utility, although radiologists should be aware of limitations of low-field MRI devices in overall quality and take that into account when diagnosing. As an initial triage to aid in the decision of whether to transport or keep patients on site, pMRI images likely provide enough information.

Taking a second look and zooming out: does this help with abnormality detection in chest radiography?

Grace Zhu, Theresa Pham M.D., Soham Banerjee, William Auffermann M.D.

Journal of Medical Imaging, Vol. 10, Issue S1, S11914, (June 2023) https://doi.org/10.1117/1.JMI.10.S1.S11914

TOPICS: Education and training, Radiology, Medical imaging, Chest imaging, Diagnostics, Radiography, Image understanding, Statistical analysis, Image processing, Chest

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Lesion detection in digital breast tomosynthesis: human reader experiments indicate no benefit from the integration of information from multiple planes

Christiana Balta, Ingrid Reiser, Mireille J. M. Broeders, Wouter J. H. Veldkamp, Ruben E. van Engen, Ioannis Sechopoulos

Journal of Medical Imaging, Vol. 10, Issue S1, S11915, (June 2023) https://doi.org/10.1117/1.JMI.10.S1.S11915

TOPICS: Digital breast tomosynthesis, 3D image processing, Breast, Optical spheres, 3D acquisition, Spherical lenses, Signal detection, 3D vision, Target detection, Computed tomography

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Purpose

In digital breast tomosynthesis (DBT), radiologists need to review a stack of 20 to 80 tomosynthesis images, depending upon breast size. This causes a significant increase in reading time. However, it is currently unknown whether there is a perceptual benefit to viewing a mass in the 3D tomosynthesis volume. To answer this question, this study investigated whether adjacent lesion-containing planes provide additional information that aids lesion detection for DBT-like and breast CT-like (bCT) images.

Method

Human reader detection performance was determined for low-contrast targets shown in a single tomosynthesis image at the center of the target (2D) or shown in the entire tomosynthesis image stack (3D). Using simulations, targets embedded in simulated breast backgrounds, and images were generated using a DBT-like (50 deg angular range) and a bCT-like (180 deg angular range) imaging geometry. Experiments were conducted with spherical and capsule-shaped targets. Eleven readers reviewed 1600 images in two-alternative forced-choice experiments. The area under the receiver operating characteristic curve (AUC) and reading time were computed for the 2D and 3D reading modes for the DBT and bCT imaging geometries and for both target shapes.

Results

Spherical lesion detection was higher in 2D mode than in 3D, for both DBT- and bCT-like images (DBT: AUC_2D = 0.790, AUC_3D = 0.735, P = 0.03; bCT: AUC_2D = 0.869, AUC_3D = 0.716, P < 0.05), but equivalent for capsule-shaped signals (DBT: AUC_2D = 0.891, AUC_3D = 0.915, P = 0.19; bCT: AUC_2D = 0.854, AUC_3D = 0.847, P = 0.88). Average reading time was up to 134% higher for 3D viewing (P < 0.05).

Conclusions

For the detection of low-contrast lesions, there is no inherent visual perception benefit to reviewing the entire DBT or bCT stack. The findings of this study could have implications for the development of 2D synthetic mammograms: a single synthesized 2D image designed to include all lesions present in the volume might allow readers to maintain detection performance at a significantly reduced reading time.

Roe and Metz identical-test simulation model for validating multi-reader methods of analysis for comparing different radiologic imaging modalities

Stephen Hillis

Journal of Medical Imaging, Vol. 10, Issue S1, S11916, (July 2023) https://doi.org/10.1117/1.JMI.10.S1.S11916

TOPICS: Data modeling, Error analysis, Covariance, Statistical analysis, Statistical modeling, Computer simulations, Sampling rates, Medical statistics, Statistical methods, Analytical research

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Assessing satisfaction of search in virtual mammograms for experienced and novice searchers

Stephen H. Adamo, Nelson Roque, Bruno Barufaldi, Joseph Schmidt, Claudia Mello-Thoms, Miguel Lago

Journal of Medical Imaging, Vol. 10, Issue S1, S11917, (July 2023) https://doi.org/10.1117/1.JMI.10.S1.S11917

TOPICS: Target detection, Mammography, Breast, Visualization, Medical imaging, Breast imaging, Digital imaging, Education and training, Breast cancer, Switches

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Purpose

Satisfaction of search (SOS) is a phenomenon where searchers are more likely to miss a lesion/target after detecting a first lesion/target. Here, we investigated SOS for masses and calcifications in virtual mammograms with experienced and novice searchers to determine the extent to which: (1) SOS affects breast lesion detection, (2) similarity between lesions impacts detection, and (3) experience impacts SOS rates.

Approach

The open virtual clinical trials framework was used to simulate the breast anatomy of patients, and up to two simulated masses and/or single-calcifications were inserted into the breast models. Experienced searchers (residents, fellows, and radiologists with breast imaging experience) and novice searchers (undergraduates who had no breast imaging experience) were instructed to search for up to two lesions (masses and calcifications) per image.

Results

2 × 2 mixed factors analysis of variances (ANOVAs) were run with: (1) single versus second lesion hit rates, (2) similar versus dissimilar second-lesion hit rates, and (3) similar versus dissimilar second-lesion response times as within-subject factors and experience as the between subject’s factor. The ANOVAs demonstrated that: (1) experienced and novice searchers made a significant amount of SOS errors, (2) similarity had little impact on experienced searchers, but novice searchers were more likely to miss a dissimilar second lesion compared to when it was similar to a detected first lesion, (3) experienced and novice searchers were faster at finding similar compared to dissimilar second lesions.

Conclusions

We demonstrated that SOS is a significant cause of lesion misses in virtual mammograms and that reader experience impacts detection rates for similar compared to dissimilar abnormalities. These results suggest that experience may impact strategy and/or recognition with theoretical implications for determining why SOS occurs.

Is it blur or is it texture? An analysis of computational blur and texture measures commonly used in medical image analysis

Andrew William Chen, Murray H. Loew

Journal of Medical Imaging, Vol. 10, Issue S1, S11920, (May 2023) https://doi.org/10.1117/1.JMI.10.S1.S11920

TOPICS: Thulium, Data modeling, Mammography, Medical imaging, Image classification, Matrices, Histograms, Diamond, Imaging devices, Image processing

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