Prospective intra-individual comparison of standard dose versus reduced-dose thoracic CT using hybrid and pure iterative reconstruction in a follow-up cohort of pulmonary nodules—Effect of detectability of pulmonary nodules with lowering dose based on nodule size, type and body mass index

Vardhanabhuti, Varut; Pang, Chun; Tenant, Sean; Taylor, James B.; Hyde, Christopher; Roobottom, Carl

doi:10.1016/j.ejrad.2017.04.006

Cited by 21 publications

(11 citation statements)

References 28 publications

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“…[ 16 – 18 ] For this reason, van Klaveren et al [ 15 ] defined growth on serial CT scans as an increase in measured volume of over 25%. Limits of agreement observed in our study are within this measure, and consistent with, or tighter than [ 21 – 23 ] prior limited reports regarding observed differences between LDCT and ULDCT imaging. As nodule size decreased, we observed more outliers, which is most likely due to measurement variability which differentially affects smaller nodules.…”

Section: Discussionsupporting

confidence: 92%

Ultralow dose CT for follow-up of solid pulmonary nodules

Paks¹,

Leong

Einsiedel

et al. 2018

Medicine

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Solid pulmonary nodules are a common finding requiring serial computed tomography (CT) imaging. We sought to explore the detection and measurement accuracy of an ultralow-dose CT (ULDCT) protocol compared with our standard low-dose CT (LDCT) nodule follow-up protocol.In this pragmatic single-center pilot prospective cohort study, patients scheduled for clinically indicated CT surveillance of 1 or more known solid pulmonary nodules >2 mm underwent ULDCT immediately after routine LDCT. The Bland-Altman 95% limits of agreement for diameter and volumetry were calculated.In all, 57 patients underwent 60 imaging episodes, with 170 evaluable nodules. ULDCT detected all known solid pulmonary nodules >2 mm. Bland-Altman analyses demonstrated clinically agreement for both nodule diameter and volume, both of which fell within prespecified limits.This single-center pilot study suggests that ULDCT may be of use in surveillance of known solid pulmonary nodules >2 mm.

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Section: Discussionsupporting

confidence: 92%

Ultralow dose CT for follow-up of solid pulmonary nodules

Paks¹,

Leong

Einsiedel

et al. 2018

Medicine

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“…Previous studies on ULD CT also excluded participants with obesity (28,29). Vardhanabhuti et al (30) found that the detectability of pulmonary nodules was reduced in individuals with higher BMIs due to higher image noise, and they concluded that ULD CT may not be suitable for overweight people. In our study, seven of 226 candidates had a BMI higher than 30 kg/m 2 .…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra–Low-Dose Chest CT

Jiang

Shi

et al. 2022

Radiology

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To compare the image quality and lung nodule detectability of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-V (ASIR-V) in ULD CT. Materials and Methods:Patients who underwent noncontrast ULD CT (performed at 0.07 or 0.14 mSv, similar to a single chest radiograph) and contrast-enhanced chest CT (CECT) from April to June 2020 were included in this prospective study. ULD CT images were reconstructed with filtered back projection (FBP), ASIR-V, and DLIR. Three-dimensional segmentation of lung tissue was performed to evaluate image noise. Radiologists detected and measured nodules with use of a deep learning-based nodule assessment system and recognized malignancy-related imaging features. Bland-Altman analysis and repeated-measures analysis of variance were used to evaluate the differences between ULD CT images and CECT images.

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“…Peer reviewed literature characterization: Quantitative measurements are improved and the noise power spectrum shifts towards lower frequencies compared to both filtered back projection and ASiR (37,(56)(57)(58). Hounsfield units are shifted to lower values at low doses (59).…”

Section: Veomentioning

confidence: 99%

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

Jensen

Hagemo

Tingberg

et al. 2020

J Comput Assist Tomogr

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I would like to thank my main supervisor, Anne Catrine Martinsen, for facilitating the opportunity to start and complete this PhD project. Without you this would not have been possible. Thank you for all the ideas, valuable feedback, discussions and support. Even though your schedule is more than full, you have always had time when I have needed help. Your optimism is invigorating and the engagement and initiative you have had for medical physicists and physics in radiology are impressive. It has been interesting to follow the tasks you have decided to pursue, and now you will pursue something quite different. Good luck and the best wishes! Thank you to my co-supervisors, Anders Tingberg and Erik Fosse. Your different insights and experiences in medical technology, imaging and research are valuable, and I have appreciated your feedback both on content, structure and writing. You have also been encouraging and supportive all the way, and I have always felt uplifted after meetings. Also, a big thank you to Örjan Smedby who introduced me to visual grading regression and STATA, and to Atle Bjørnerud who stepped in as supervisor at the last minute.In addition to my supervisors I have had help with scanning phantoms and writing articles. To have access to firsthand knowledge on CT scanners, anatomy and the radiology field makes my day more interesting. Thank you to all my co-authors and scanning help. Ingrid Helen Hauge and Øystein Bech Gadmar, thank you for proofreading of articles and this dissertation. Your comments have been very helpful.Lunch and coffee breaks are important for thinking, and I am very grateful for having so many nice colleagues always making these breaks fun and enjoyable. A special thanks to Hilde Kjernlie Andersen for facilitating my workday so I could finish this thesis, and to all the physicists at the Department of diagnostic physics. No matter what problems there are, you are always willing to help, and I am grateful to work with you.Thank you to my parents and brother who made my childhood safe, yet still eventful, and gave me the prerequisites to utilize the opportunities brought by life. And to my closest, Marius, Joakim and Hedda; I look forward to every day with you. List of papers1. Comparing five different iterative reconstruction algorithms for computed tomography in an ROC study.

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Prospective intra-individual comparison of standard dose versus reduced-dose thoracic CT using hybrid and pure iterative reconstruction in a follow-up cohort of pulmonary nodules—Effect of detectability of pulmonary nodules with lowering dose based on nodule size, type and body mass index

Cited by 21 publications

References 28 publications

Ultralow dose CT for follow-up of solid pulmonary nodules

Ultralow dose CT for follow-up of solid pulmonary nodules

Deep Learning Reconstruction Shows Better Lung Nodule Detection for Ultra–Low-Dose Chest CT

Evaluation of Image Quality for 7 Iterative Reconstruction Algorithms in Chest Computed Tomography Imaging: A Phantom Study

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