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Carrino JA, Ibad H, Lin Y, Ghotbi E, Klein J, Demehri S, Del Grande F, Bogner E, Boesen MP, Siewerdsen JH. CT in musculoskeletal imaging: still helpful and for what? Skeletal Radiol 2024; 53:1711-1725. [PMID: 38969781 DOI: 10.1007/s00256-024-04737-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 06/17/2024] [Accepted: 06/17/2024] [Indexed: 07/07/2024]
Abstract
Computed tomography (CT) is a common modality employed for musculoskeletal imaging. Conventional CT techniques are useful for the assessment of trauma in detection, characterization and surgical planning of complex fractures. CT arthrography can depict internal derangement lesions and impact medical decision making of orthopedic providers. In oncology, CT can have a role in the characterization of bone tumors and may elucidate soft tissue mineralization patterns. Several advances in CT technology have led to a variety of acquisition techniques with distinct clinical applications. These include four-dimensional CT, which allows examination of joints during motion; cone-beam CT, which allows examination during physiological weight-bearing conditions; dual-energy CT, which allows material decomposition useful in musculoskeletal deposition disorders (e.g., gout) and bone marrow edema detection; and photon-counting CT, which provides increased spatial resolution, decreased radiation, and material decomposition compared to standard multi-detector CT systems due to its ability to directly translate X-ray photon energies into electrical signals. Advanced acquisition techniques provide higher spatial resolution scans capable of enhanced bony microarchitecture and bone mineral density assessment. Together, these CT acquisition techniques will continue to play a substantial role in the practices of orthopedics, rheumatology, metabolic bone, oncology, and interventional radiology.
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Affiliation(s)
- John A Carrino
- Weill Cornell Medicine, New York, NY, USA.
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
| | - Hamza Ibad
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Yenpo Lin
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | - Elena Ghotbi
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Joshua Klein
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Shadpour Demehri
- Musculoskeletal Radiology, The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 N. Caroline Street, JHOC 5165, Baltimore, MD, 21287, USA
| | - Filippo Del Grande
- Clinic of Radiology, Imaging Institute of Southern Switzerland (IIMSI), Ente Ospedaliero Cantonale (EOC), Via Tesserete 46, 6900, Lugano, Switzerland
- Faculty of Biomedical Sciences, Università Della Svizzera Italiana (USI), Via G. Buffi 13, 6904, Lugano, Switzerland
| | - Eric Bogner
- Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Mikael P Boesen
- Department of Radiology, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Nielsine Nielsens Vej 5, Entrance 7A, 3Rd Floor, 2400, Copenhagen, NV, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeffrey H Siewerdsen
- Department of Imaging Physics, Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Zhou Z, Gong H, Hsieh S, McCollough CH, Yu L. Image quality evaluation in deep-learning-based CT noise reduction using virtual imaging trial methods: Contrast-dependent spatial resolution. Med Phys 2024; 51:5399-5413. [PMID: 38555876 PMCID: PMC11321944 DOI: 10.1002/mp.17029] [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: 08/30/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Deep-learning-based image reconstruction and noise reduction methods (DLIR) have been increasingly deployed in clinical CT. Accurate image quality assessment of these methods is challenging as the performance measured using physical phantoms may not represent the true performance of DLIR in patients since DLIR is trained mostly on patient images. PURPOSE In this work, we aim to develop a patient-data-based virtual imaging trial framework and, as a first application, use it to measure the spatial resolution properties of a DLIR method. METHODS The patient-data-based virtual imaging trial framework consists of five steps: (1) insertion of lesions into projection domain data using the acquisition geometry of the patient exam to simulate different lesion characteristics; (2) insertion of noise into projection domain data using a realistic photon statistical model of the CT system to simulate different dose levels; (3) creation of DLIR-processed images from projection or image data; (4) creation of ensembles of DLIR-processed patient images from a large number of noise and lesion realizations; and (5) evaluation of image quality using ensemble DLIR images. This framework was applied to measure the spatial resolution of a ResNet based deep convolutional neural network (DCNN) trained on patient images. Lesions in a cylindrical shape and different contrast levels (-500, -100, -50, -20, -10 HU) were inserted to the lower right lobe of the liver in a patient case. Multiple dose levels were simulated (50%, 25%, 12.5%). Each lesion and dose condition had 600 noise realizations. Multiple reconstruction and denoising methods were used on all the noise realizations, including the original filtered-backprojection (FBP), iterative reconstruction (IR), and the DCNN method with three different strength setting (DCNN-weak, DCNN-medium, and DCNN-strong). Mean lesion signal was calculated by performing ensemble averaging of all the noise realizations for each lesion and dose condition and then subtracting the lesion-present images from the lesion absent images. Modulation transfer functions (MTFs) both in-plane and along the z-axis were calculated based on the mean lesion signals. The standard deviations of MTFs at each condition were estimated with bootstrapping: randomly sampling (with replacement) all the DLIR/FBP/IR images from the ensemble data (600 samples) at each condition. The impact of varying lesion contrast, dose levels, and denoising strengths were evaluated. Statistical analysis with paired t-test was used to compare the z-axis and in-plane spatial resolution of five algorithms for five different contrasts and three dose levels. RESULTS The in-plane and z-axis spatial resolution degradation of DCNN becomes more severe as the contrast or radiation dose decreased, or DCNN denoising strength increased. In comparison with FBP, a 59.5% and 4.1% reduction of in-plane and z-axis MTF (in terms of spatial frequencies at 50% MTF), respectively, was observed at low contrast (-10 HU) for DCNN with the highest denoising strength at 25% routine dose level. When the dose level reduces from 50% to 12.5% of routine dose, the in-plane and z-axis MTFs reduces from 92.1% to 76.3%, and from 98.9% to 95.5%, respectively, at contrast of -100 HU, using FBP as the reference. For most conditions of contrasts and dose levels, significant differences were found among the five algorithms, with the following relationship in both in-plane and cross-plane spatial resolution: FBP > DCNN-Weak > IR > DCNN-Medium > DCNN-Strong. The spatial resolution difference among algorithms decreases at higher contrast or dose levels. CONCLUSIONS A patient-data-based virtual imaging trial framework was developed and applied to measuring the spatial resolution properties of a DCNN noise reduction method at different contrast and dose levels using real patient data. As with other non-linear image reconstruction and post-processing techniques, the evaluated DCNN method degraded the in-plane and z-axis spatial resolution at lower contrast levels, lower radiation dose, and higher denoising strength.
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Affiliation(s)
| | - Hao Gong
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | - Scott Hsieh
- Department of Radiology, Mayo Clinic, Rochester, MN, US
| | | | - Lifeng Yu
- Department of Radiology, Mayo Clinic, Rochester, MN, US
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Park M, Lim S, Kim H, Kim JY, Lee Y. Optimization of smoothing parameter for block matching and 3D filtering algorithm in low-dose chest and abdominal computed tomography images. Appl Radiat Isot 2024; 210:111374. [PMID: 38805985 DOI: 10.1016/j.apradiso.2024.111374] [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: 11/24/2023] [Revised: 04/29/2024] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
Computed tomography (CT), known for its exceptionally high accuracy, is associated with a substantial dose of ionizing radiation. Low-dose protocols have been devised to address this issue; however, a reduction in the radiation dose can lead to a deficiency in the number of photons, resulting in quantum noise. Thus, the aim of this study was to optimize the smoothing parameter (σ-value) of the block matching and 3D filtering (BM3D) algorithm to effectively reduce noise in low-dose chest and abdominal CT images. Acquired images were subsequently analyze using quantitative evaluation metrics, including contrast to noise ratio (CNR), coefficient of variation (CV), and naturalness image quality evaluator (NIQE). Quantitative evaluation results demonstrated that the optimal σ-value for CNR, CV, and NIQE were 0.10, 0.11, and 0.09 in low-dose chest CT images respectively, whereas those in abdominal images were 0.12, 0.11, and 0.09, respectively. The average of the optimal σ-values, which produced the most improved results, was 0.10, considering both visual and quantitative evaluations. In conclusion, we demonstrated that the optimized BM3D algorithm with σ-value is effective for noise reduction in low-dose chest and abdominal CT images indicating its feasibility of in the clinical field.
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Affiliation(s)
- Minji Park
- Department of Health Science, General Graduate School of Gachon University, Incheon, Republic of Korea
| | - Sewon Lim
- Department of Health Science, General Graduate School of Gachon University, Incheon, Republic of Korea
| | - Hajin Kim
- Department of Health Science, General Graduate School of Gachon University, Incheon, Republic of Korea
| | - Jae-Young Kim
- Department of Biochemistry, School of Dentistry, IHBR, Kyungpook National University, Daegu, Republic of Korea
| | - Youngjin Lee
- Department of Radiological Science, Gachon University, Incheon, Republic of Korea.
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Cote MP, Lubowitz JH. Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications. Arthroscopy 2024; 40:1033-1038. [PMID: 38300189 DOI: 10.1016/j.arthro.2023.12.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 12/30/2023] [Indexed: 02/02/2024]
Abstract
Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available-ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting.
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Khoriati AA, Shahid Z, Fok M, Frank RM, Voss A, D'Hooghe P, Imam MA. Artificial intelligence and the orthopaedic surgeon: A review of the literature and potential applications for future practice: Current concepts. J ISAKOS 2024; 9:227-233. [PMID: 37949113 DOI: 10.1016/j.jisako.2023.10.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 10/28/2023] [Accepted: 10/30/2023] [Indexed: 11/12/2023]
Affiliation(s)
- Al-Achraf Khoriati
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Zuhaib Shahid
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK.
| | - Margaret Fok
- Department of Orthopaedics and Traumatology, Queen Mary Hospital, The University of Hong Kong, Pok Fu Lam Rd, High West, Hong Kong, China; Asia Pacific Orthopaedic Association, 57000, Malaysia.
| | - Rachel M Frank
- Department of Orthopaedic Surgery, Joint Preservation Program, University of Colorado School of Medicine, 12631 E 17th Ave, Mail Stop B202, Aurora, CO 80045, USA.
| | - Andreas Voss
- Sporthopaedicum Regensburg, Street, Hildegard-von-Bingen-Straße 1, 93053, Regensburg, Germany.
| | - Pieter D'Hooghe
- Aspetar Orthopedic and Sports Medicine Hospital, Aspire Zone, Sportscity Street 1, P.O. Box 29222, Doha, Qatar
| | - Mohamed A Imam
- Rowley Bristow Orthopaedic Centre, Ashford and St Peter's NHS Foundation Trust, Chertsey, KT106PZ, UK; Smart Health Centre, University of East London, University Way, London, E16 2RD, United Kingdom.
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Martín-Noguerol T, López-Úbeda P, Luna A. Imagine there is no paperwork… it's easy if you try. Br J Radiol 2024; 97:744-746. [PMID: 38335929 PMCID: PMC11027242 DOI: 10.1093/bjr/tqae035] [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: 10/28/2023] [Revised: 01/11/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024] Open
Abstract
Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowadays, there are AI applications developed to facilitate the diagnostic process of radiologists without directly influencing (or replacing) the proper diagnostic decision step. These tools may help to reduce administrative workload, in different scenarios ranging from assisting in scheduling, study prioritization, or report communication, to helping with patient follow-up, including recommending additional exams. These are just a few of the highly time-consuming tasks that radiologists have to deal with every day in their routine workflow. These tasks hinder the time that radiologists should spend evaluating images and caring for patients, which will have a direct and negative impact on the quality of reports and patient attention, increasing the delay and waiting list of studies pending to be performed and reported. These types of AI applications should help to partially face this worldwide shortage of radiologists.
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Affiliation(s)
| | | | - Antonio Luna
- MRI Unit, Radiology Department, HT medica, Jaén 23007, Spain
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Wrazidlo R, Walder L, Estler A, Gutjahr R, Schmidt B, Faby S, Fritz J, Nikolaou K, Horger M, Hagen F. Radiation Dose Reduction in Contrast-Enhanced Abdominal CT: Comparison of Photon-Counting Detector CT with 2nd Generation Dual-Source Dual-Energy CT in an oncologic cohort. Acad Radiol 2023; 30:855-862. [PMID: 35760710 DOI: 10.1016/j.acra.2022.05.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 05/30/2022] [Accepted: 05/30/2022] [Indexed: 11/28/2022]
Abstract
RATIONAL AND OBJECTIVES Comparison of radiation dose and image quality in routine abdominal and pelvic contrast-enhanced computed tomography (CECT) between a photon-counting detector CT (PCD-CT) and a dual energy dual source CT (DSCT). MATERIALS AND METHODS 70 oncologic patients (mean age 66 ± 12 years, 29 females) were prospectively enrolled between November 2021 and February 2022. Abdominal CECT were clinically indicated and performed first on a 2nd-generation DSCT and at follow-up on a 1st-generation dual-source PCD-CT. The same contrast media (Imeron 350, Bracco imaging) and pump protocol was used for both scans. For both scanners, polychromatic images were reconstructed with 3mm slice thickness and comparable kernel (I30f[DSCT] and Br40f[PCD-CT]); for PCD-CT data from all counted events above the lowest energy threshold at 20 keV ("T3D") were used. Results were compared in terms of radiation dose metrics of CT dose index (CTDIvol), dose length product (DLP) and size-specific dose estimation (SSDE), objective and subjective measurements of image quality were scored by two emergency radiologists including lesion conspicuity. RESULTS Median time interval between the scans was 4 months (IQR: 3-6). CNRvessel and SNRvessel of T3D reconstructions from PCD-CT were significantly higher than those of DSCT (all, p < 0.05). Qualitative image noise analysis from PCD-CT and DSCT yielded a mean of 4 each. Lesion conspicuity was rated significantly higher in PCD-CT (Q3 strength) compared to DSCT images. CTDI, DLP and SSDE mean values for PCD-CT and DSCT were 7.98 ± 2.56 mGy vs. 14.11 ± 2.92 mGy, 393.13 ± 153.55 mGy*cm vs. 693.61 ± 185.76 mGy*cm and 9.98 ± 2.41 vs. 14.63 ± 1.63, respectively, translating to a dose reduction of around 32% (SSDE). CONCLUSION PCD-CT enables oncologic abdominal CT with a significantly reduced dose while keeping image quality similar to 2nd-generation DSCT.
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Affiliation(s)
- Robin Wrazidlo
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Lukas Walder
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Arne Estler
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Ralf Gutjahr
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Bernhard Schmidt
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Sebastian Faby
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Jan Fritz
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
| | - Marius Horger
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.).
| | - Florian Hagen
- Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University, 72076 Tübingen, Germany (R.W., L.W., A.E., K.N., M.H., F.H.); Siemens Healthcare GmbH, 91052 Erlangen, Germany (R.G., B.S., S.F.); NYU Grossman School of Medicine, Department of Radiology, New York, NY 10016, USA (J.F.)
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Verhaegen F, Butterworth KT, Chalmers AJ, Coppes RP, de Ruysscher D, Dobiasch S, Fenwick JD, Granton PV, Heijmans SHJ, Hill MA, Koumenis C, Lauber K, Marples B, Parodi K, Persoon LCGG, Staut N, Subiel A, Vaes RDW, van Hoof S, Verginadis IL, Wilkens JJ, Williams KJ, Wilson GD, Dubois LJ. Roadmap for precision preclinical x-ray radiation studies. Phys Med Biol 2023; 68:06RM01. [PMID: 36584393 DOI: 10.1088/1361-6560/acaf45] [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: 05/12/2022] [Accepted: 12/30/2022] [Indexed: 12/31/2022]
Abstract
This Roadmap paper covers the field of precision preclinical x-ray radiation studies in animal models. It is mostly focused on models for cancer and normal tissue response to radiation, but also discusses other disease models. The recent technological evolution in imaging, irradiation, dosimetry and monitoring that have empowered these kinds of studies is discussed, and many developments in the near future are outlined. Finally, clinical translation and reverse translation are discussed.
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Affiliation(s)
- Frank Verhaegen
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
- SmART Scientific Solutions BV, Maastricht, The Netherlands
| | - Karl T Butterworth
- Patrick G. Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Anthony J Chalmers
- School of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, United Kingdom
| | - Rob P Coppes
- Departments of Biomedical Sciences of Cells & Systems, Section Molecular Cell Biology and Radiation Oncology, University Medical Center Groningen, University of Groningen, 9700 AD Groningen, The Netherlands
| | - Dirk de Ruysscher
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sophie Dobiasch
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine and Klinikum rechts der Isar, Germany
- Department of Medical Physics, Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Germany
| | - John D Fenwick
- Department of Medical Physics & Biomedical Engineering University College LondonMalet Place Engineering Building, London WC1E 6BT, United Kingdom
| | | | | | - Mark A Hill
- MRC Oxford Institute for Radiation Oncology, University of Oxford, ORCRB Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Constantinos Koumenis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Kirsten Lauber
- Department of Radiation Oncology, University Hospital, LMU München, Munich, Germany
- German Cancer Consortium (DKTK), Partner site Munich, Germany
| | - Brian Marples
- Department of Radiation Oncology, University of Rochester, NY, United States of America
| | - Katia Parodi
- German Cancer Consortium (DKTK), Partner site Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching b. Munich, Germany
| | | | - Nick Staut
- SmART Scientific Solutions BV, Maastricht, The Netherlands
| | - Anna Subiel
- National Physical Laboratory, Medical Radiation Science Hampton Road, Teddington, Middlesex, TW11 0LW, United Kingdom
| | - Rianne D W Vaes
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Ioannis L Verginadis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jan J Wilkens
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine and Klinikum rechts der Isar, Germany
- Physics Department, Technical University of Munich (TUM), Germany
| | - Kaye J Williams
- Division of Pharmacy and Optometry, University of Manchester, Manchester, United Kingdom
| | - George D Wilson
- Department of Radiation Oncology, Beaumont Health, MI, United States of America
- Henry Ford Health, Detroit, MI, United States of America
| | - Ludwig J Dubois
- The M-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
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Monteith S, Glenn T, Geddes J, Whybrow PC, Achtyes E, Bauer M. Expectations for Artificial Intelligence (AI) in Psychiatry. Curr Psychiatry Rep 2022; 24:709-721. [PMID: 36214931 PMCID: PMC9549456 DOI: 10.1007/s11920-022-01378-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/15/2022] [Indexed: 01/29/2023]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. RECENT FINDINGS For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.
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Affiliation(s)
- Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, 49684, USA.
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - John Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Eric Achtyes
- Michigan State University College of Human Medicine, Grand Rapids, MI, 49684, USA
- Network180, Grand Rapids, MI, USA
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus Medical Faculty, Technische Universität Dresden, Dresden, Germany
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Radiation Dose Reduction Opportunities in Vascular Imaging. Tomography 2022; 8:2618-2638. [PMID: 36287818 PMCID: PMC9607049 DOI: 10.3390/tomography8050219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
Computed tomography angiography (CTA) has been the gold standard imaging modality for vascular imaging due to a variety of factors, including the widespread availability of computed tomography (CT) scanners, the ease and speed of image acquisition, and the high sensitivity of CTA for vascular pathology. However, the radiation dose experienced by the patient during imaging has long been a concern of this image acquisition method. Advancements in CT image acquisition techniques in combination with advancements in non-ionizing radiation imaging techniques including magnetic resonance angiography (MRA) and contrast-enhanced ultrasound (CEUS) present growing opportunities to reduce total radiation dose to patients. This review provides an overview of advancements in imaging technology and acquisition techniques that are helping to minimize radiation dose associated with vascular imaging.
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Tin-filtered 100 kV Ultra-low-dose Abdominal CT for Calculi Detection in the Urinary Tract: A Comparative Study of 510 Cases. Acad Radiol 2022; 30:1033-1038. [PMID: 35963837 DOI: 10.1016/j.acra.2022.07.013] [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: 06/21/2022] [Revised: 07/14/2022] [Accepted: 07/14/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVES For detection of urinary calculi, unenhanced low-dose computed tomography is the method of choice, outperforming radiography and ultrasound. This retrospective monocentric study aims to compare a clinically established, dedicated low-dose imaging protocol for detection of urinary calculi with an ultra-low-dose protocol employing tin prefiltration at a standardized tube voltage of 100 kVp. METHODS Two study arms included a total of 510 cases. The "low-dose group" was comprised of 290 individuals (96 women; age 49 ± 16 years; BMI 27.23 ± 5.60 kg/m2). The "ultra-low-dose group" with Sn100 kVp consisted of 220 patients (84 women; age 47 ± 17 years; BMI 26.82 ± 5.62 kg/m2). No significant difference was ascertained for comparison of age (p = 0.132) and BMI (p = 0.207) between cohorts. For quantitative assessment of image quality, image noise was assessed. RESULTS No significant difference regarding frequency of calculi detection was found between groups (p = 0.596). Compared to the low-dose protocol (3.08 mSv; IQR 2.22-4.02 mSv), effective dose was reduced by 62.35% with the ultra-low-dose protocol employing spectral shaping (1.16 mSv; IQR 0.89-1.54 mSv). Image noise was calculated at 18.90 (IQR 17.39-21.20) for the low-dose protocol and at 18.69 (IQR 17.30-21.62) for the ultra-low-dose spectral shaping protocol. No significant difference was ascertained for comparison between groups (p = 0.793). CONCLUSION For urinary calculi detection, ultra-low-dose scans utilizing spectral shaping by means of tin prefiltration at 100 kVp allow for considerable dose reduction of up to 62% over conventional low-dose CT without compromising image quality.
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