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Bos D, Demircioğlu A, Neuhoff J, Haubold J, Zensen S, Opitz MK, Drews MA, Li Y, Styczen H, Forsting M, Nassenstein K. Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans. Sci Rep 2024; 14:11810. [PMID: 38782976 PMCID: PMC11116440 DOI: 10.1038/s41598-024-62394-4] [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: 01/30/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024] Open
Abstract
In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.
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Affiliation(s)
- Denise Bos
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany.
| | - Aydin Demircioğlu
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Julia Neuhoff
- Faculty of Medicine, University Duisburg-Essen, Hufelandstraße 55, 45122, Essen, Germany
| | - Johannes Haubold
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Sebastian Zensen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Marcel K Opitz
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Marcel A Drews
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Yan Li
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Hanna Styczen
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Michael Forsting
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
| | - Kai Nassenstein
- Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University Duisburg-Essen, Hufelandstraße 55, 45147, Essen, Germany
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Rabinowich A, Shendler G, Ben-Sira L, Shiran SI. Pediatric low-dose head CT: Image quality improvement using iterative model reconstruction. Neuroradiol J 2023; 36:555-562. [PMID: 36897057 PMCID: PMC10569199 DOI: 10.1177/19714009231163559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/11/2023] Open
Abstract
PURPOSE To evaluate the differences in pediatric non-contrast low-dose head computed tomography (CT) between filtered-back projection and iterative model reconstruction using objective and subjective image quality evaluation. METHODS A retrospective study evaluated children undergoing low-dose non-contrast head CT. All CT scans were reconstructed using both filtered-back projection and iterative model reconstruction. Objective image quality analysis was performed using contrast and signal-to-noise ratios for the supra- and infratentorial brain regions of identical regions of interest on the two reconstruction methods. Two experienced pediatric neuroradiologists evaluated subjective image quality, visibility of structures, and artifacts. RESULTS We evaluated 233 low-dose brain CT scans of 148 pediatric patients. There was a ∼2-fold improvement in the contrast-to-noise ratio between gray and white matter in the infra- and supratentorial regions (p < 0.001) using iterative model reconstruction compared to filtered-back projection. The white and gray matter signal-to-noise ratio improved more than 2-fold using iterative model reconstruction (p < 0.001). Furthermore, radiologists graded anatomical details, gray-white matter differentiation, beam hardening artifacts, and image quality using iterative model reconstructions as superior to filtered-back projection reconstructions. CONCLUSION Iterative model reconstructions had better contrast-to-noise and signal-to-noise ratios with fewer artifacts in pediatric CT brain scans using low-dose radiation protocols. This image quality improvement was demonstrated in the supra- and infratentorial regions. This method thus comprises an important tool for reducing children's exposure while maintaining diagnostic capability.
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Affiliation(s)
- Aviad Rabinowich
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Genady Shendler
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Liat Ben-Sira
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Shelly I Shiran
- Department of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Dresing K, Kraus R, Fernandez F, Schmittenbecher P, Dresing K, Strohm P, Spering C. [Imaging after trauma in clinics and practice for children and adolescents : Part 1 of the results of a nationwide online survey of the Pediatric Traumatology Section of the German Trauma Society]. UNFALLCHIRURGIE (HEIDELBERG, GERMANY) 2023; 126:34-41. [PMID: 34918189 PMCID: PMC9842554 DOI: 10.1007/s00113-021-01115-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 11/23/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND The indication for radiography should strictly follow the ALARA (as low as reasonably achievable) principle in pediatric and adolescent trauma patients. The effect of radiation on the growing sensitive tissue of these patients should not be disregarded. QUESTION The Pediatric Traumatology Section (SKT) of the German Trauma Society (DGU) wanted to clarify how the principle is followed in trauma care. METHODS An online survey was open for 10 weeks. Target groups were trauma surgeons, pediatric surgeons, general surgeons, and orthopedic surgeons. RESULTS From Nov. 15, 2019, to Feb. 29, 2020, 788 physicians participated: branch office 20.56%, MVZ 4.31%, hospital 75.13%; resident 16.62%, senior 38.07%, chief 22.59%. By specialist qualification, the distribution was: 38.34% surgery, 33.16% trauma surgery, 36.66% special trauma surgery, 70.34% orthopedics and trauma surgery, 18.78% pediatric surgery. Frequency of contact with fractures in the above age group was reported as 37% < 10/month, 27% < 20/M, 36% > 20/M. About 52% always request radiographs in 2 planes after acute trauma. X-ray of the opposite side for unclear findings was rejected by 70%. 23% use sonography regularly in fracture diagnosis. In polytrauma children and adolescents, whole-body CT is never used in 18%, rarely in 50%, and standard in 14%. DISCUSSION The analysis shows that there is no uniform radiological management of children and adolescents with fractures among the respondents. CONCLUSION Comparing the results of the survey with the consensus findings of the SKT recently published in this journal, persuasion is still needed to change the use of radiography in primary diagnosis.
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Affiliation(s)
- Klaus Dresing
- Klinik für Unfallchirurgie, Orthopädie und Plastische Chirurgie, Universitätsmedizin Göttingen, Robert-Koch-Str. 40, 37099, Göttingen, Deutschland.
| | - Ralf Kraus
- Klinik für Unfallchirurgie und Orthopädie, Klinikum Bad Hersfeld, Bad Hersfeld, Deutschland
| | - Francisco Fernandez
- Kindertraumatologie, Klinikum Stuttgart Olgahospital, Stuttgart, Deutschland
| | | | - Kaya Dresing
- Darmstädter Kinderkliniken Prinzessin Margaret, Darmstadt, Deutschland
| | - Peter Strohm
- Klinik für Orthopädie und Unfallchirurgie, Klinikum Bamberg, Bamberg, Deutschland
| | - Christopher Spering
- Klinik für Unfallchirurgie, Orthopädie und Plastische Chirurgie, Universitätsmedizin Göttingen, Robert-Koch-Str. 40, 37099, Göttingen, Deutschland
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Lee N, Cho HH, Lee SM, You SK. Adaptation of Deep Learning Image Reconstruction for Pediatric Head CT: A Focus on the Image Quality. JOURNAL OF THE KOREAN SOCIETY OF RADIOLOGY 2023; 84:240-252. [PMID: 36818715 PMCID: PMC9935960 DOI: 10.3348/jksr.2021.0073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/06/2021] [Accepted: 05/27/2022] [Indexed: 11/18/2022]
Abstract
Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.
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Affiliation(s)
- Nim Lee
- Department of Radiology, Medical Research Institute, College of Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
- Department of Radiology, Research Institute of Radiological Science, Severance Hospital, Yonsei University, College of Medicine, Seoul, Korea
| | - Hyun-Hae Cho
- Department of Radiology, Medical Research Institute, College of Medicine, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - So Mi Lee
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Korea
| | - Sun Kyoung You
- Department of Radiology, Chungnam National University Hospital, Chungnam National University College of Medicine, Daejeon, Korea
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Xu J, Hu X, Zhang Y, Xu Z, Wu H, Luo K. Application of Different Levels of Advanced Modeling Iterative Reconstruction in Brain CT Scanning. Curr Med Imaging 2022; 18:1362-1368. [PMID: 35578865 DOI: 10.2174/1573405618666220516121722] [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: 11/04/2021] [Revised: 03/17/2022] [Accepted: 03/19/2022] [Indexed: 01/25/2023]
Abstract
BACKGROUND Advanced Modeling Iterative Reconstruction (ADMIRE) algorithm has five intensity levels; it is important to study which algorithm is better for brain CT scanning. OBJECTIVE The aim of the study is to compare the influence of different strength levels of ADMIRE and traditional Filtered Back Projection (FBP) on image quality in brain CT scanning. METHODS 60 patients were retrospectively selected, and the data from each of these patients' brains were reconstructed by four different reconstruction methods (FBP, ADMIRE1, ADMIRE3, and ADMIRE5). A five-point Likert Scale was implemented to evaluate the subjective image quality. Image noise, CT value of brain tissue , signal-to-noise ratio (SNR) of gray white matter, contrast-to-noise ratio (CNR), and beam hardening artifact index (AI) of the posterior fossa, were measured for evaluating the objective image quality. Finally, the differences between the subjective and objective evaluations were compared. RESULTS There were no statistical differences observed in CT values of gray matter and white matter between the four groups (all P >0.05). The image noise gradually decreased with the increase of ADMIRE algorithm level. The AI exhibited no statistical difference between the four groups (F =0.793, P =0.499), but it tended to decrease slightly with the increase of ADMIRE algorithm level. Compared to other groups (all p <0.001), the ADMIRE5 group demonstrated the best objective image quality. Nevertheless, the highest subjective score was observed in the ADMIRE3 group, which exhibited significant differences with other images (all P <0.001). CONCLUSION ADMIRE algorithm can clearly improve image quality, but it cannot significantly improve the linear sclerosis artifacts in the posterior cranial fossa. Based on the subjective evaluation of image quality, ADMIRE3 algorithm is recommended in brain CT scanning.
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Affiliation(s)
- Jun Xu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xiaoli Hu
- Department of Radiology, Wuhan Asian Heart Hospital, 430022 Wuhan, China
| | - Youxin Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Zhihan Xu
- Siemens Healthineers, 430022 Wuhan, China
| | - Hongying Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Kun Luo
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
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Hodges H, Epstein KN, Retrouvey M, Wang SS, Richards AA, Lima D, Revels JW. Pitfalls in the interpretation of pediatric head CTs: what the emergency radiologist needs to know. Emerg Radiol 2022; 29:729-742. [PMID: 35394570 DOI: 10.1007/s10140-022-02042-4] [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: 01/18/2022] [Accepted: 03/29/2022] [Indexed: 11/28/2022]
Abstract
Pediatric radiology studies can be some of the most anxiety-inducing imaging examinations encountered in practice. This can be in part due to the wide range of normal anatomic appearances inherent to the pediatric population that create potential interpretive pitfalls for radiologists. The pediatric head is no exception; for instance, the inherent greater water content within the neonatal brain compared to older patients could easily be mistaken for cerebral edema, and anatomic variant calvarial sutures can be mistaken for skull fractures. This article reviews potential pitfalls emergency radiologists may encounter in practice when interpreting pediatric head CTs, including trauma, extra-axial fluid collections, intra-axial hemorrhage, and ventriculoperitoneal shunt complications.
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Affiliation(s)
- Hannah Hodges
- Department of Radiology, University of New Mexico, MSC 10 5530, 1, Albuquerque, NM, 87131, USA
| | - Katherine N Epstein
- Department of Radiology, University of New Mexico, MSC 10 5530, 1, Albuquerque, NM, 87131, USA
| | - Michele Retrouvey
- Department of Radiology, Eastern Virginia Medical School, Diagnostic Radiology, P.O. Box 1980, Norfolk, VA, 23501, USA
| | - Sherry S Wang
- Department of Radiology and Imaging Sciences, University of Utah, 30 North 1900 East #1A71, Salt Lake City, UT, 84132, USA
| | - Allyson A Richards
- Department of Radiology, University of New Mexico, MSC 10 5530, 1, Albuquerque, NM, 87131, USA
| | - Dustin Lima
- Department of Radiology, University of New Mexico, MSC 10 5530, 1, Albuquerque, NM, 87131, USA
| | - Jonathan W Revels
- Department of Radiology, University of New Mexico, MSC 10 5530, 1, Albuquerque, NM, 87131, USA.
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Worrall M, MacDonald N, Gillen R, Hince A, Hampson L, Duguid R, McCallum S, Gentle D. The optimisation of paediatric CT examinations in Scotland: phase one; benchmarking current performance. JOURNAL OF RADIOLOGICAL PROTECTION : OFFICIAL JOURNAL OF THE SOCIETY FOR RADIOLOGICAL PROTECTION 2021; 41:902-919. [PMID: 33862611 DOI: 10.1088/1361-6498/abf901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 04/16/2021] [Indexed: 06/12/2023]
Abstract
To benchmark the dose from paediatric head and chest examinations on computed tomography (CT) scanners throughout Scotland, to identify scanners that may require optimisation and to provide optimisation advice based on the protocols from better performing scanners. Anthropomorphic phantoms corresponding to 1, 5 and 10 year olds were sent to 50 CT scanners around Scotland. Head and chest examinations were undertaken by local staff using local techniques on each scanner with each phantom, and details of the protocols used were recorded. Computed tomography dose index (CTDI)voland dose length product (DLP) were recorded post-scan. There is a significant variation in performance throughout Scotland. For head examinations, the highest DLP is 13 times the lowest for an equivalent sized phantom. For chest examinations, the highest is 128 times the lowest for an equivalent sized phantom. The wide range of CT dose measurements indicates the potential for variation in image quality across Scotland. Feedback has been provided to all participating sites on their individual results compared to the national data set. Specific feedback was provided where relevant on potential considerations for optimisation. Scanners that may be undertaking paediatric CT head and chest examinations in a sub-optimal manner throughout Scotland have been identified along with those aspects of a scan protocol that are most likely to lead to sub-optimal performance.
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Affiliation(s)
- Mark Worrall
- Department of Medical Physics, Ninewells Hospital and Medical School, Ninewells Avenue, Dundee, DD1 9SY, United Kingdom
| | - Nicola MacDonald
- Department of Medical Physics, Royal Infirmary of Edinburgh, 51 Little France Crescent, Edinburgh, EH16 4SA, United Kingdom
| | - Rebecca Gillen
- Department of Clinical Physics and Bioengineering, Gartnavel Hospital, 1053 Great Western Road, Glasgow, G12 0YN, United Kingdom
| | - Andrew Hince
- Department of Medical Physics and Bioengineering, Raigmore Hospital, Old Perth Road, Inverness, IV2 3UJ, United Kingdom
| | - Lee Hampson
- Department of Medical Physics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, AB25 2ZN, United Kingdom
| | - Rebecca Duguid
- Department of Medical Physics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, AB25 2ZN, United Kingdom
| | - Stephen McCallum
- Department of Medical Physics, Aberdeen Royal Infirmary, Foresterhill, Aberdeen, AB25 2ZN, United Kingdom
| | - David Gentle
- Department of Clinical Physics and Bioengineering, Gartnavel Hospital, 1053 Great Western Road, Glasgow, G12 0YN, United Kingdom
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Sun J, Li H, Wang B, Li J, Li M, Zhou Z, Peng Y. Application of a deep learning image reconstruction (DLIR) algorithm in head CT imaging for children to improve image quality and lesion detection. BMC Med Imaging 2021; 21:108. [PMID: 34238229 PMCID: PMC8268450 DOI: 10.1186/s12880-021-00637-w] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 06/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To evaluate the performance of a Deep Learning Image Reconstruction (DLIR) algorithm in pediatric head CT for improving image quality and lesion detection with 0.625 mm thin-slice images. METHODS Low-dose axial head CT scans of 50 children with 120 kV, 0.8 s rotation and age-dependent 150-220 mA tube current were selected. Images were reconstructed at 5 mm and 0.625 mm slice thickness using Filtered back projection (FBP), Adaptive statistical iterative reconstruction-v at 50% strength (50%ASIR-V) (as reference standard), 100%ASIR-V and DLIR-high (DL-H). The CT attenuation and standard deviation values of the gray and white matters in the basal ganglia were measured. The clarity of sulci/cisterns, boundary between white and gray matters, and overall image quality was subjectively evaluated. The number of lesions in each reconstruction group was counted. RESULTS The 5 mm FBP, 50%ASIR-V, 100%ASIR-V and DL-H images had a subjective score of 2.25 ± 0.44, 3.05 ± 0.23, 2.87 ± 0.39 and 3.64 ± 0.49 in a 5-point scale, respectively with DL-H having the lowest image noise of white matter at 2.00 ± 0.34 HU; For the 0.625 mm images, only DL-H images met the diagnostic requirement. The 0.625 mm DL-H images had similar image noise (3.11 ± 0.58 HU) of the white matter and overall image quality score (3.04 ± 0.33) as the 5 mm 50% ASIR-V images (3.16 ± 0.60 HU and 3.05 ± 0.23). Sixty-five lesions were recognized in 5 mm 50%ASIR-V images and 69 were detected in 0.625 mm DL-H images. CONCLUSION DL-H improves the head CT image quality for children compared with ASIR-V images. The 0.625 mm DL-H images improve lesion detection and produce similar image noise as the 5 mm 50%ASIR-V images, indicating a potential 85% dose reduction if current image quality and slice thickness are desired.
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Affiliation(s)
- Jihang Sun
- Imaging center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | - Haoyan Li
- Imaging center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | - Bei Wang
- Imaging center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China
| | | | | | - Zuofu Zhou
- Department of Radiology, Fujian Provincial Maternity and Children's Hospital, Affiliated Hospital of Fujian Medical University, No. 18 Daoshan Road, Gulou District, Fujian, 350000, China.
| | - Yun Peng
- Imaging center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China.
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Goo HW. Hydrocephalus: Ventricular Volume Quantification Using Three-Dimensional Brain CT Data and Semiautomatic Three-Dimensional Threshold-Based Segmentation Approach. Korean J Radiol 2020; 22:435-441. [PMID: 33169552 PMCID: PMC7909866 DOI: 10.3348/kjr.2020.0671] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 06/15/2020] [Accepted: 06/22/2020] [Indexed: 01/13/2023] Open
Abstract
OBJECTIVE To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus. MATERIALS AND METHODS Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations. RESULTS Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm³, 249.0 ± 150.8 cm³, and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948-1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity. CONCLUSION The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.
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Affiliation(s)
- Hyun Woo Goo
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Kim I, Kang H, Yoon HJ, Chung BM, Shin NY. Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V). Neuroradiology 2020; 63:905-912. [PMID: 33037503 DOI: 10.1007/s00234-020-02574-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 09/29/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement. RESULTS There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR. CONCLUSION On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.
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Affiliation(s)
- Injoong Kim
- Department of Radiology, Veterans Health Service Medical Center, 53 Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul, 05368, South Korea
| | - Hyunkoo Kang
- Department of Radiology, Veterans Health Service Medical Center, 53 Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul, 05368, South Korea
| | - Hyun Jung Yoon
- Department of Radiology, Veterans Health Service Medical Center, 53 Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul, 05368, South Korea
| | - Bo Mi Chung
- Department of Radiology, Veterans Health Service Medical Center, 53 Jinhwangdo-ro 61-gil, Gangdong-gu, Seoul, 05368, South Korea
| | - Na-Young Shin
- Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, South Korea.
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Sheridan DC, Pettersson D, Newgard CD, Selden NR, Jafri MA, Lin A, Rowell S, Hansen ML. Can QuickBrain MRI replace CT as first-line imaging for select pediatric head trauma? J Am Coll Emerg Physicians Open 2020; 1:965-973. [PMID: 33145547 PMCID: PMC7593443 DOI: 10.1002/emp2.12113] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/15/2020] [Accepted: 05/01/2020] [Indexed: 01/27/2023] Open
Abstract
OBJECTIVE The current standard of care for initial neuroimaging in injured pediatric patients suspected of having traumatic brain injury is computed tomography (CT) that carries risks associated with radiation exposure. The primary objective of this trial was to evaluate the ability of a QuickBrain MRI (qbMRI) protocol to detect clinically important traumatic brain injuries in the emergency department (ED). The secondary objective of this trial was to compare qbMRI to CT in identifying radiographic traumatic brain injury. METHODS This was a prospective study of trauma patients less than 15 years of age with suspected traumatic brain injury at a level 1 pediatric trauma center in Portland, Oregon between August 2017 and March 2019. All patients in whom a head CT was deemed clinically necessary were approached for enrollment to also obtain a qbMRI in the acute setting. Clinically important traumatic brain injury was defined as the need for neurological surgery procedure, intubation, pediatric intensive care unit stay greater than 24 hours, a total hospital length of stay greater than 48 hours, or death. RESULTS A total of 73 patients underwent both CT and qbMRI. The median age was 4 years (interquartile range [IQR] = 1-10 years). Twenty-two patients (30%) of patients had a clinically important traumatic brain injury, and of those, there were 2 deaths (9.1%). QbMRI acquisition time had a median of 4 minutes and 52 seconds (IQR = 3 minutes 49 seconds-5 minutes 47 seconds). QbMRI had sensitivity for detecting clinically important traumatic brain injury of 95% (95% confidence interval [CI] = 77%-99%). For any radiographic injury, qbMRI had a sensitivity of 89% (95% CI = 78%-94%). CONCLUSION Our results suggest that qbMRI has good sensitivity to detect clinically important traumatic brain injuries. Further multi-institutional, prospective trials are warranted to either support or refute these findings.
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Affiliation(s)
- David C Sheridan
- Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health & Science University Portland Oregon USA
| | - David Pettersson
- Department of Radiology Division for Neuroradiology Oregon Health & Science University Portland Oregon USA
| | - Craig D Newgard
- Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health & Science University Portland Oregon USA
| | - Nathan R Selden
- Department of Neurological Surgery Division of Pediatric Neurosurgery Oregon Health & Science University Portland Oregon USA
| | - Mubeen A Jafri
- Department of Surgery Division of Pediatric Surgery Oregon Health & Science University Portland Oregon USA
| | - Amber Lin
- Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health & Science University Portland Oregon USA
| | - Susan Rowell
- Department of Surgery Division of Trauma Surgery Oregon Health & Science University Portland Oregon USA
| | - Matthew L Hansen
- Center for Policy and Research in Emergency Medicine Department of Emergency Medicine Oregon Health & Science University Portland Oregon USA
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Kharita MH, Al-Naemi H, Arru C, Omar AJ, Aly A, Tsalafoutas I, Alkhazzam S, Singh R, Kalra MK. Relation between age and CT radiation doses: Dose trends in 705 pediatric head CT. Eur J Radiol 2020; 130:109138. [PMID: 32619755 DOI: 10.1016/j.ejrad.2020.109138] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 05/18/2020] [Accepted: 06/12/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE To evaluate the relationship between patient age and radiation doses associated with routine pediatric head CT performed with automatic tube potential selection and tube current modulation techniques. METHODS We obtained patient demographics, scan parameters, and radiation dose descriptors (CT dose index volume -CTDIvol and dose length product -DLP) associated with consecutive routine head CT in 705 children (mean age 6.9 ± 5 years). Children were scanned on one of the three multidetector-row CTs (64-128 slices, Siemens) over 6 months period in a tertiary hospital. All head CT exams were performed in helical scan mode using automatic tube potential selection (Care kV) and automatic tube current modulation (Care Dose 4D) techniques. The information was obtained from a radiation dose monitoring software. Data were analyzed using linear correlation and analysis of variance. RESULTS Most age-wise median CTDIvol (9-27 mGy; 703/705 pediatric head CT, >99 %) from our institution were lower than the European Diagnostic Reference Levels (EDRL, CTDIvol 24-50 mGy) but median DLP (151-586 mGy cm) from 201/705 children (28 %) was higher than the EDRL (DLP 300-650 mGy cm). Unlike the age-stratified EDRL, a combination of automatic tube potential selection and tube current modulation for pediatric head results in a significant linear correlation between radiation doses and patient age (r2 = 0.66, p < 0.001). CONCLUSIONS Radiation doses for head CT change linearly with children's age. Despite lower CTDIvol and DLP for most children, longer scan length resulted in higher DLP for some pediatric head CT compared to the corresponding EDRL; this result underscores the need to promote clear guidelines for technologists operating CT.
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Affiliation(s)
| | | | - Chiara Arru
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | | | - Antar Aly
- Hamad Medical Corporation, Doha, Qatar
| | | | | | - Ramandeep Singh
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Mannudeep K Kalra
- Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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Bathla G, Priya S, Samaniego E, Deo SK, Fain NH, Soni N, Ward C, Derdeyn CP. Cerebral computed tomographic angiography using third-generation reconstruction algorithm provides improved image quality with lower contrast and radiation dose. Neuroradiology 2020; 62:965-970. [PMID: 32277245 DOI: 10.1007/s00234-020-02406-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 03/13/2020] [Indexed: 02/03/2023]
Abstract
PURPOSE We hypothesized that cerebral CT angiogram performed using third-generation reconstruction algorithm and lower contrast dose-low-kVp technique (LD-CTA) will provide better image quality when compared with regular contrast dose CTA at 120 kVp using a sinogram-affirmed iterative reconstruction algorithm (ND-CTA). METHODS Retrospective imaging review of 100 consecutive patients (50 each in LD- and ND-CTA groups). Two readers independently assessed the subjective image quality across multiple vascular segments on a Likert-like scale. Differences in contrast dose, CT dose index (CTDI), and dose length product (DLP) were compared using Mann-Whitney U test. Fisher's exact test was used to compare subjective image quality. Similarly, contrast- and signal-to-noise ratios (CNR and SNR) were compared in the mid-M1 MCA vessels bilaterally and the mid-basilar artery using Mann-Whitney U test. Interclass correlation coefficient (ICC) was calculated for the SNR/CNR values. RESULTS Both observers showed excellent correlation in subjective image quality (mean percentage agreement of 95.2% for group 1 versus 89.2% for group 2). LD-CTA group showed better SNR and CNR (p < 0.0001) for both MCA vessels and the mid-basilar artery. Interclass correlation coefficient showed moderate correlation (0.51-0.63) between readers. LD-CTA group also used lower contrast (49 cc versus 97 cc in ND-CTA) and had lower radiation exposure (DLP/CTDI for both groups 268.3/80.7 vs 519.5/36.08, both < 0.0001). CONCLUSION Next-generation reconstruction algorithm and low-kV scanning significantly improved image quality on cerebral CTA images despite lower contrast dose and, in addition, have lower radiation exposure.
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Affiliation(s)
- Girish Bathla
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA.
| | - Sarv Priya
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Edgar Samaniego
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, USA
| | - Simmi K Deo
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Nicholas H Fain
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Neetu Soni
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
| | - Caitlin Ward
- Department of Biostatistics, University of Iowa, Iowa City, IA, USA
| | - Colin P Derdeyn
- Department of Radiology, University of Iowa Hospitals and Clinics, 200 Hawkins Drive, Iowa City, IA, 52242, USA
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