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Bellmann Q, Peng Y, Genske U, Yan L, Wagner M, Jahnke P. Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms. Eur Radiol Exp 2024; 8:84. [PMID: 39046565 PMCID: PMC11269546 DOI: 10.1186/s41747-024-00486-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 06/18/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. METHODS Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. RESULTS DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). CONCLUSION DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. RELEVANCE STATEMENT Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. KEY POINTS Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.
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Affiliation(s)
- Quirin Bellmann
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Yang Peng
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei Province, China
| | - Ulrich Genske
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Li Yan
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Moritz Wagner
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany
| | - Paul Jahnke
- Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
- Berlin Institute of Health (BIH), Anna-Louisa-Karsch-Str. 2, 10178, Berlin, Germany.
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Lang M, Kenda M, Scheel M, Martola J, Wheeler M, Owen S, Johnsson M, Annborn M, Dankiewicz J, Deye N, Düring J, Friberg H, Halliday T, Jakobsen JC, Lascarrou JB, Levin H, Lilja G, Lybeck A, McGuigan P, Rylander C, Sem V, Thomas M, Ullén S, Undén J, Wise MP, Cronberg T, Wassélius J, Nielsen N, Leithner C, Moseby-Knappe M. Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study. Intensive Care Med 2024; 50:1096-1107. [PMID: 38900283 PMCID: PMC11245448 DOI: 10.1007/s00134-024-07497-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE Application of standardised and automated assessments of head computed tomography (CT) for neuroprognostication after out-of-hospital cardiac arrest. METHODS Prospective, international, multicentre, observational study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Routine CTs from adult unconscious patients obtained > 48 h ≤ 7 days post-arrest were assessed qualitatively and quantitatively by seven international raters blinded to clinical information using a pre-published protocol. Grey-white-matter ratio (GWR) was calculated from four (GWR-4) and eight (GWR-8) regions of interest manually placed at the basal ganglia level. Additionally, GWR was obtained using an automated atlas-based approach. Prognostic accuracies for prediction of poor functional outcome (modified Rankin Scale 4-6) for the qualitative assessment and for the pre-defined GWR cutoff < 1.10 were calculated. RESULTS 140 unconscious patients were included; median age was 68 years (interquartile range [IQR] 59-76), 76% were male, and 75% had poor outcome. Standardised qualitative assessment and all GWR models predicted poor outcome with 100% specificity (95% confidence interval [CI] 90-100). Sensitivity in median was 37% for the standardised qualitative assessment, 39% for GWR-8, 30% for GWR-4 and 41% for automated GWR. GWR-8 was superior to GWR-4 regarding prognostic accuracies, intra- and interrater agreement. Overall prognostic accuracy for automated GWR (area under the curve [AUC] 0.84, 95% CI 0.77-0.91) did not significantly differ from manually obtained GWR. CONCLUSION Standardised qualitative and quantitative assessments of CT are reliable and feasible methods to predict poor functional outcome after cardiac arrest. Automated GWR has the potential to make CT quantification for neuroprognostication accessible to all centres treating cardiac arrest patients.
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Affiliation(s)
- Margareta Lang
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Martin Kenda
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, BIH Biomedical Innovation Academy, BIH Charité Junior Digital Clinician Scientist Program, Universitätsmedizin Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Michael Scheel
- Department of Neuroradiology, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Juha Martola
- HUS Medical Imaging Center, Radiology, University of Helsinki, Helsinki University Hospital, Helsinki, Finland
| | - Matthew Wheeler
- University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, Wales, UK
| | - Stephanie Owen
- University Hospital of Wales, Cardiff and Vale University Health Board, Cardiff, Wales, UK
| | - Mikael Johnsson
- Department of Radiology, Helsingborg Hospital, Helsingborg, Sweden
| | - Martin Annborn
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden
| | - Josef Dankiewicz
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Cardiology, Skåne University Hospital, Lund, Sweden
| | - Nicolas Deye
- Department of Medical and Toxicological Intensive Care Unit, Inserm UMR-S 942, Assistance Publique des Hopitaux de Paris, Lariboisière University Hospital, Paris, France
| | - Joachim Düring
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Skåne University Hospital, Malmö, Sweden
| | - Hans Friberg
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Skåne University Hospital, Malmö, Sweden
| | - Thomas Halliday
- Department of Operation and Intensive Care, Linköping University Hospital, Linköping, Sweden
| | - Janus Christian Jakobsen
- Department of Regional Health Research, The Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
- Copenhagen Trial Unit, Centre for Clinical Intervention Research, Capital Region of Denmark, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Jean-Baptiste Lascarrou
- Medecine Intensive Reanimation, Movement-Interactions-Performance,, Nantes Université, CHU Nantes, MIP, UR 4334, 44000, Nantes, France
| | - Helena Levin
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Research and Education, Skåne University Hospital, Lund, Sweden
| | - Gisela Lilja
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Anna Lybeck
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Skåne University Hospital, Lund, Sweden
| | - Peter McGuigan
- Regional Intensive Care Unit, Royal Victoria Hospital, Belfast, UK
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | - Christian Rylander
- Anaesthesia and Intensive Care, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Victoria Sem
- Department of Anaesthesia and Intensive Care, Central Hospital of Karlstad, Karlstad, Sweden
| | - Matthew Thomas
- Intensive Care Unit, University Hospitals Bristol and Weston, Bristol, UK
| | - Susann Ullén
- Clinical Studies Sweden‑Forum South, Skåne University Hospital, Lund, Sweden
| | - Johan Undén
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Operation and Intensive Care, Hallands Hospital Halmstad, Halmstad, Sweden
| | - Matt P Wise
- Adult Critical Care, University Hospital of Wales, Cardiff, UK
| | - Tobias Cronberg
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Johan Wassélius
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Niklas Nielsen
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden
- Department of Anaesthesia and Intensive Care, Helsingborg Hospital, Helsingborg, Sweden
| | - Christoph Leithner
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität and Humboldt-Universität Zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany
| | - Marion Moseby-Knappe
- Department of Clinical Sciences Lund, Lund University, Lund, Sweden.
- Department of Neurology, Skåne University Hospital, Lund, Sweden.
- Department of Rehabilitation, Skåne University Hospital, 22185, Lund, Sweden.
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Martini K, Jungblut L, Sartoretti T, Langhart S, Yalynska T, Nemeth B, Frauenfelder T, Euler A. Impact of radiation dose on the detection of interstitial lung changes and image quality in low-dose chest CT - Assessment in multiple dose levels from a single patient scan. Eur J Radiol 2023; 166:110981. [PMID: 37478655 DOI: 10.1016/j.ejrad.2023.110981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/01/2023] [Accepted: 07/14/2023] [Indexed: 07/23/2023]
Abstract
PURPOSE To assess image quality and detectability of interstitial lung changes using multiple radiation doses from the same chest CT scan of patients with suspected interstitial lung disease (ILD). METHOD Retrospective study of consecutive adult patients with suspected ILD receiving unenhanced chest CT as single-energy dual-source acquisition at 100 kVp (Dual-split mode). 67% and 33% of the overall tube current time product were assigned to tube A and B, respectively. 100%-dose was 2.34 ± 0.97 mGy. Five different radiation doses (100%, 67%, 45%, 39%, 33%) were reconstructed from this single acquisition using linear-blending technique. Two blinded radiologists assessed reticulations, ground-glass opacities (GGO) and honeycombing as well as subjective image noise. Percentage agreement (PA) as compared to 100%-dose were calculated. Non-parametric statistical tests were used. RESULTS A total of 228 patients were included (61.2 ± 14.6 years,146 female). PA was highest for honeycombing (>96%) and independent of dose reduction (P > 0.8). PA for reticulations and GGO decreased when reducing the radiation dose from 100% to 67% for both readers (reticulations: 83.3% and 93.9%; GGO: 87.7% and 79.8% for reader 1 and 2, respectively). Additional dose reduction did not significantly change PA for both readers (all P > 0.05). Subjective image noise increased with decreasing radiation dose (Spearman Rho of ρ = 0.34 and ρ = 0.53 for reader 1 and 2, respectively, P < 0.001). CONCLUSIONS Radiation dose reduction had a stronger impact on subtle interstitial lung changes. Detectability decreased with initial dose reduction indicating that a minimum dose is needed to maintain diagnostic accuracy in chest CT for suspected ILD.
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Affiliation(s)
- Katharina Martini
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Lisa Jungblut
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Thomas Sartoretti
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Sabinne Langhart
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Tetyana Yalynska
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - Bence Nemeth
- Department of Neuroradiology, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091 Zurich, Switzerland
| | - Thomas Frauenfelder
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland
| | - André Euler
- Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland; Department of Radiology, Kantonsspital Baden, University of Zurich, Im Ergel 1, 5404 Baden, Switzerland.
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4
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Kostyrko B, Rubarth K, Althoff C, Zibell M, Neizert CA, Poch F, Torsello GF, Gebauer B, Lehmann K, Niehues SM, Mews J, Diekhoff T, Pohlan J. Evaluation of Different Registration Algorithms to Reduce Motion Artifacts in CT-Thermography (CTT). Diagnostics (Basel) 2023; 13:2076. [PMID: 37370971 DOI: 10.3390/diagnostics13122076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023] Open
Abstract
Computed tomography (CT)-based Thermography (CTT) is currently being investigated as a non-invasive temperature monitoring method during ablation procedures. Since multiple CT scans with defined time intervals were acquired during this procedure, interscan motion artifacts can occur between the images, so registration is required. The aim of this study was to investigate different registration algorithms and their combinations for minimizing inter-scan motion artifacts during thermal ablation. Four CTT datasets were acquired using microwave ablation (MWA) of normal liver tissue performed in an in vivo porcine model. During each ablation, spectral CT volume scans were sequentially acquired. Based on initial reconstructions, rigid or elastic registration, or a combination of these, were carried out and rated by 15 radiologists. Friedman's test was used to compare rating results in reader assessments and revealed significant differences for the ablation probe movement rating only (p = 0.006; range, 5.3-6.6 points). Regarding this parameter, readers assessed rigid registration as inferior to other registrations. Quantitative analysis of ablation probe movement yielded a significantly decreased distance for combined registration as compared with unregistered data. In this study, registration was found to have the greatest influence on ablation probe movement, with connected registration being superior to only one registration process.
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Affiliation(s)
- Bogdan Kostyrko
- Department of Radiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
| | - Kerstin Rubarth
- Institute for Biometry and Clinical Epidemiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
- Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10178 Berlin, Germany
| | - Christian Althoff
- Department of Radiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
| | - Miriam Zibell
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 12203 Berlin, Germany
| | - Christina Ann Neizert
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 12203 Berlin, Germany
| | - Franz Poch
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 12203 Berlin, Germany
| | - Giovanni Federico Torsello
- Department of Radiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
| | - Bernhard Gebauer
- Department of Radiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
| | - Kai Lehmann
- Department of General and Visceral Surgery, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 12203 Berlin, Germany
| | - Stefan Markus Niehues
- Department of Radiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
| | - Jürgen Mews
- Canon Medical Systems Europe BV, Global Research & Development Center, 2718 RP Zoetermeer, The Netherlands
| | - Torsten Diekhoff
- Department of Radiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
| | - Julian Pohlan
- Department of Radiology, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10117 Berlin, Germany
- Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, Freie Universität Berlin, 10178 Berlin, Germany
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Liu Z, Wolfe S, Yu Z, Laforest R, Mhlanga JC, Fraum TJ, Itani M, Dehdashti F, Siegel BA, Jha AK. Observer-study-based approaches to quantitatively evaluate the realism of synthetic medical images. Phys Med Biol 2023; 68:10.1088/1361-6560/acc0ce. [PMID: 36863028 PMCID: PMC10411234 DOI: 10.1088/1361-6560/acc0ce] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 03/02/2023] [Indexed: 03/04/2023]
Abstract
Objective.Synthetic images generated by simulation studies have a well-recognized role in developing and evaluating imaging systems and methods. However, for clinically relevant development and evaluation, the synthetic images must be clinically realistic and, ideally, have the same distribution as that of clinical images. Thus, mechanisms that can quantitatively evaluate this clinical realism and, ideally, the similarity in distributions of the real and synthetic images, are much needed.Approach.We investigated two observer-study-based approaches to quantitatively evaluate the clinical realism of synthetic images. In the first approach, we presented a theoretical formalism for the use of an ideal-observer study to quantitatively evaluate the similarity in distributions between the real and synthetic images. This theoretical formalism provides a direct relationship between the area under the receiver operating characteristic curve, AUC, for an ideal observer and the distributions of real and synthetic images. The second approach is based on the use of expert-human-observer studies to quantitatively evaluate the realism of synthetic images. In this approach, we developed a web-based software to conduct two-alternative forced-choice (2-AFC) experiments with expert human observers. The usability of this software was evaluated by conducting a system usability scale (SUS) survey with seven expert human readers and five observer-study designers. Further, we demonstrated the application of this software to evaluate a stochastic and physics-based image-synthesis technique for oncologic positron emission tomography (PET). In this evaluation, the 2-AFC study with our software was performed by six expert human readers, who were highly experienced in reading PET scans, with years of expertise ranging from 7 to 40 years (median: 12 years, average: 20.4 years).Main results.In the ideal-observer-study-based approach, we theoretically demonstrated that the AUC for an ideal observer can be expressed, to an excellent approximation, by the Bhattacharyya distance between the distributions of the real and synthetic images. This relationship shows that a decrease in the ideal-observer AUC indicates a decrease in the distance between the two image distributions. Moreover, a lower bound of ideal-observer AUC = 0.5 implies that the distributions of synthetic and real images exactly match. For the expert-human-observer-study-based approach, our software for performing the 2-AFC experiments is available athttps://apps.mir.wustl.edu/twoafc. Results from the SUS survey demonstrate that the web application is very user friendly and accessible. As a secondary finding, evaluation of a stochastic and physics-based PET image-synthesis technique using our software showed that expert human readers had limited ability to distinguish the real images from the synthetic images.Significance.This work addresses the important need for mechanisms to quantitatively evaluate the clinical realism of synthetic images. The mathematical treatment in this paper shows that quantifying the similarity in the distribution of real and synthetic images is theoretically possible by using an ideal-observer-study-based approach. Our developed software provides a platform for designing and performing 2-AFC experiments with human observers in a highly accessible, efficient, and secure manner. Additionally, our results on the evaluation of the stochastic and physics-based image-synthesis technique motivate the application of this technique to develop and evaluate a wide array of PET imaging methods.
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Affiliation(s)
- Ziping Liu
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, United States of America
| | - Scott Wolfe
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Zitong Yu
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, United States of America
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Joyce C Mhlanga
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Tyler J Fraum
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Malak Itani
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Farrokh Dehdashti
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Barry A Siegel
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Abhinav K Jha
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, United States of America
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
- Alvin J. Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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6
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Wang G, Badal A, Jia X, Maltz JS, Mueller K, Myers KJ, Niu C, Vannier M, Yan P, Yu Z, Zeng R. Development of metaverse for intelligent healthcare. NAT MACH INTELL 2022; 4:922-929. [PMID: 36935774 PMCID: PMC10015955 DOI: 10.1038/s42256-022-00549-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 09/16/2022] [Indexed: 11/16/2022]
Abstract
The metaverse integrates physical and virtual realities, enabling humans and their avatars to interact in an environment supported by technologies such as high-speed internet, virtual reality, augmented reality, mixed and extended reality, blockchain, digital twins and artificial intelligence (AI), all enriched by effectively unlimited data. The metaverse recently emerged as social media and entertainment platforms, but extension to healthcare could have a profound impact on clinical practice and human health. As a group of academic, industrial, clinical and regulatory researchers, we identify unique opportunities for metaverse approaches in the healthcare domain. A metaverse of 'medical technology and AI' (MeTAI) can facilitate the development, prototyping, evaluation, regulation, translation and refinement of AI-based medical practice, especially medical imaging-guided diagnosis and therapy. Here, we present metaverse use cases, including virtual comparative scanning, raw data sharing, augmented regulatory science and metaversed medical intervention. We discuss relevant issues on the ecosystem of the MeTAI metaverse including privacy, security and disparity. We also identify specific action items for coordinated efforts to build the MeTAI metaverse for improved healthcare quality, accessibility, cost-effectiveness and patient satisfaction.
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Affiliation(s)
- Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Andreu Badal
- Division of Imaging, Diagnostics and Software Reliability, OSEL, CDRH, US Food and Drug Administration, Silver Spring, MD, USA
| | - Xun Jia
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Jonathan S. Maltz
- Molecular Imaging and Computed Tomography, GE Healthcare, Waukesha, WI, USA
| | - Klaus Mueller
- Computer Science Department, Stony Brook University, Stony Brook, NY, USA
| | | | - Chuang Niu
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | | | - Pingkun Yan
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Zhou Yu
- Canon Medical Research USA, Vernon Hills, IL, USA
| | - Rongping Zeng
- Division of Imaging, Diagnostics and Software Reliability, OSEL, CDRH, US Food and Drug Administration, Silver Spring, MD, USA
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7
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Kim M, Yoon K, Kim KG. Design of the Floating Hologram Method with a Reverse Pyramid Type for CT and MR Diagnosis in Clinical Room. Diagnostics (Basel) 2022; 12:diagnostics12051157. [PMID: 35626312 PMCID: PMC9140033 DOI: 10.3390/diagnostics12051157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/29/2022] [Accepted: 05/02/2022] [Indexed: 12/04/2022] Open
Abstract
In the field of medical diagnosis, big data and three-dimensional (3D) imaging diagnosis technology are being applied due to the development of these technologies. Using radiology diagnosis methods, medical staff are increasing their understanding and ability to explain symptoms to patients, but they are experiencing difficulties due to communication problems. Therefore, if the medical staff shows the lesion by providing the patient with a 3D image, the understanding of the patient can be increased. This paper proposes the design of a system to produce an inverted pyramid-shaped floating holographic image to increase the patient’s understanding. The hologram system consists of an optical source generator and a beam mirror and utilizes a technology to plot an image using a 45° refraction angle of the beam of the optical source. Selected objects for observation were liver, colon, and lung, and to observe these tissues, a Computed Tomography (CT) image was input to the hologram system through the picture archiving and communication system (PACS), and the image was displayed. Tissues observed through the mirror can be observed from the left, right, front, and back with a 360° anterior view. Therefore, it is possible to observe at the desired position by the medical staff and the patient in the treatment room, and the image is large and clear, so it is very satisfying to observe. As a holographic imaging diagnostic system, it is expected that this study can be used in clinics, medical education rooms, and operating rooms in the future.
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Affiliation(s)
- Minchan Kim
- Department School of Medicine, College of Medicine, National Cheng Kung University, Tainan City 704, Taiwan;
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 Beon-gil, Namdong-daero, Namdong-gu, Incheon 21565, Korea;
| | - Kicheol Yoon
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 Beon-gil, Namdong-daero, Namdong-gu, Incheon 21565, Korea;
- Department of Biomedical Engineering, College of Medicine, Gachon University, 38-13, 3 Beon-gil, Dokjom-ro 3, Namdong-gu, Incheon 21565, Korea
| | - Kwang Gi Kim
- Medical Devices R&D Center, Gachon University Gil Medical Center, 21, 774 Beon-gil, Namdong-daero, Namdong-gu, Incheon 21565, Korea;
- Department of Biomedical Engineering, College of Medicine, Gachon University, 38-13, 3 Beon-gil, Dokjom-ro 3, Namdong-gu, Incheon 21565, Korea
- Department of Biomedical Engineering, College of Health Science, Gachon University, 191 Hambak-moero, Yeonsu-gu, Incheon 21936, Korea
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology (GAIHST), Gachon University, 38-13, 3 Beon-gil, Dokjom-ro, Namdong-gu, Incheon 21565, Korea
- Correspondence: ; Tel.: +82-32-458-2880
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