Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
European Radiology
Published in: European Radiology 7/2020

01-07-2020 | Computed Tomography | Computed Tomography

Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study

Authors: Joël Greffier, Aymeric Hamard, Fabricio Pereira, Corinne Barrau, Hugo Pasquier, Jean Paul Beregi, Julien Frandon

Published in: European Radiology | Issue 7/2020

Login to get access

Abstract

Objectives

To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm.

Methods

Data acquisitions were performed at seven dose levels (CTDIvol : 15/10/7.5/5/2.5/1/0.5 mGy) using a standard phantom designed for image quality assessment. Raw data were reconstructed using the filtered back projection (FBP), two levels of IR (ASiR-V50% (AV50); ASiR-V100% (AV100)), and three levels of DLIR (TrueFidelity™ low, medium, high). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index (d′) was computed to model a large mass in the liver, a small calcification, and a small subtle lesion with low contrast.

Results

NPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF50% obtained with DLIR was higher than that with IR. d′ was higher with DLIR than with AV50 but lower with DLIR-L and DLIR-M than with AV100. d′ values were higher with DLIR-H than with AV100 for the small low-contrast lesion (10 ± 4%) and in the same range for the other simulated lesions.

Conclusions

New DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR.

Key Points

• This study assessed the impact on image quality and radiation dose of a new deep learning image reconstruction (DLIR) algorithm as compared with hybrid iterative reconstruction (IR) algorithm.
• The new DLIR algorithm reduced noise and improved spatial resolution and detectability without perceived alteration of the texture, commonly reported with IR.
• As compared with IR, DLIR seems to open further possibility of dose optimization.
Appendix
Available only for authorised users
Literature
1.
Brenner DJ, Hall EJ (2007) Computed tomography--an increasing source of radiation exposure. N Engl J Med 357:2277–2284CrossRef
2.
Greffier J, Pereira F, Macri F, Beregi JP, Larbi A (2016) CT dose reduction using automatic exposure control and iterative reconstruction: a chest paediatric phantoms study. Phys Med 32:582–589CrossRef
3.
Beregi JP, Greffier J (2019) Low and ultra-low dose radiation in CT: opportunities and limitations. Diagn Interv Imaging 100:63–64CrossRef
4.
5.
Willemink MJ, Noel PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29:2185–2195CrossRef
6.
Katsura M, Matsuda I, Akahane M et al (2012) Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 22:1613–1623CrossRef
7.
Yamada Y, Jinzaki M, Hosokawa T et al (2012) Dose reduction in chest CT: comparison of the adaptive iterative dose reduction 3D, adaptive iterative dose reduction, and filtered back projection reconstruction techniques. Eur J Radiol 81:4185–4195CrossRef
8.
Larbi A, Orliac C, Frandon J et al (2018) Detection and characterization of focal liver lesions with ultra-low dose computed tomography in neoplastic patients. Diagn Interv Imaging 99:311–320CrossRef
9.
Macri F, Greffier J, Khasanova E et al (2019) Minor blunt thoracic trauma in the emergency department: sensitivity and specificity of chest ultralow-dose computed tomography compared with conventional radiography. Ann Emerg Med 73:665–670CrossRef
10.
11.
Kim HG, Lee HJ, Lee SK, Kim HJ, Kim MJ (2017) Head CT: image quality improvement with ASIR-V using a reduced radiation dose protocol for children. Eur Radiol 27:3609–3617CrossRef
12.
13.
Tenant S, Pang CL, Dissanayake P et al (2017) Intra-patient comparison of reduced-dose model-based iterative reconstruction with standard-dose adaptive statistical iterative reconstruction in the CT diagnosis and follow-up of urolithiasis. Eur Radiol 27:4163–4172CrossRef
14.
Verdun FR, Racine D, Ott JG et al (2015) Image quality in CT: from physical measurements to model observers. Phys Med 31:823–843CrossRef
15.
Ott JG, Becce F, Monnin P, Schmidt S, Bochud FO, Verdun FR (2014) Update on the non-prewhitening model observer in computed tomography for the assessment of the adaptive statistical and model-based iterative reconstruction algorithms. Phys Med Biol 59:4047–4064CrossRef
16.
Samei E, Richard S (2015) Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. Med Phys 42:314–323CrossRef
17.
Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276:339–357CrossRef
18.
19.
Qi D, Hao C, Lequan Y et al (2016) Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging 35:1182–1195CrossRef
20.
Roth HR, Lu L, Seff A et al (2014) A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. Med Image Comput Comput Assist Interv 17:520–527PubMedPubMedCentral
21.
Powell S, Magnotta VA, Johnson H, Jammalamadaka VK, Pierson R, Andreasen NC (2008) Registration and machine learning-based automated segmentation of subcortical and cerebellar brain structures. Neuroimage 39:238–247CrossRef
22.
Akagi M, Nakamura Y, Higaki T et al (2019) Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29:6163–6171CrossRef
23.
JHsieh J, Liu E, Nett B, Tang J, Thibault JB, Sahney S (2019) A new era of image reconstruction: TrueFidelity™. Technical white paper on deep learning image reconstruction. GE Healthcare
24.
Wu D, Kim K, Li Q (2019) Computationally efficient deep neural network for computed tomography image reconstruction. Med Phys 46:4763–4776CrossRef
25.
Nakamura Y, Higaki T, Tatsugami F et al (2019) Deep learning–based CT image reconstruction: initial evaluation targeting hypovascular hepatic metastases. Radiol Artif Intell 1:e180011CrossRef
26.
Thibault JB, Sauer KD, Bouman CA, Hsieh J (2007) A three-dimensional statistical approach to improved image quality for multislice helical CT. Med Phys 34:4526–4544CrossRef
27.
McCollough CH, Chen GH, Kalender W et al (2012) Achieving routine submillisievert CT scanning: report from the summit on management of radiation dose in CT. Radiology 264:567–580CrossRef
28.
Solomon J, Zhang Y, Wilson J, Samei E (2018) An automated software tool for task-based image quality assessment and matching in clinical CT using the TG-233 framework. Med Phys 45:E134–E134CrossRef
29.
Samei E, Bakalyar D, Boedeker KL et al (2019) Performance evaluation of computed tomography systems: summary of AAPM task group 233. Med Phys 46:e735–e756CrossRef
30.
McCollough CH, Yu L, Kofler JM et al (2015) Degradation of CT low-contrast spatial resolution due to the use of iterative reconstruction and reduced dose levels. Radiology 276:499–506CrossRef
31.
Jensen CT, Wagner-Bartak NA, Vu LN et al (2019) Detection of colorectal hepatic metastases is superior at standard radiation dose CT versus reduced dose CT. Radiology 290:400–409CrossRef
32.
Christianson O, Chen JJ, Yang Z et al (2015) An improved index of image quality for task-based performance of CT iterative reconstruction across three commercial implementations. Radiology 275:725–734CrossRef
33.
Kwon H, Cho J, Oh J et al (2015) The adaptive statistical iterative reconstruction-V technique for radiation dose reduction in abdominal CT: comparison with the adaptive statistical iterative reconstruction technique. Br J Radiol 88:20150463CrossRef
34.
Chen LH, Jin C, Li JY et al (2018) Image quality comparison of two adaptive statistical iterative reconstruction (ASiR, ASiR-V) algorithms and filtered back projection in routine liver CT. Br J Radiol 91:20170655CrossRef
35.
Goodenberger MH, Wagner-Bartak NA, Gupta S et al (2018) Computed tomography image quality evaluation of a new iterative reconstruction algorithm in the abdomen (adaptive statistical iterative reconstruction-V) a comparison with model-based iterative reconstruction, adaptive statistical iterative reconstruction, and filtered back projection reconstructions. J Comput Assist Tomogr 42:184–190CrossRef
Metadata
Title
Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study
Authors
Joël Greffier
Aymeric Hamard
Fabricio Pereira
Corinne Barrau
Hugo Pasquier
Jean Paul Beregi
Julien Frandon
Publication date
01-07-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 7/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-06724-w

Other articles of this Issue 7/2020

European Radiology 7/2020 Go to the issue

Imaging Informatics and Artificial Intelligence

Noninvasive prediction of lymph node status for patients with early-stage cervical cancer based on radiomics features from ultrasound images

Hepatobiliary-Pancreas

Risk stratification in primary sclerosing cholangitis: comparison of biliary stricture severity on MRCP versus liver stiffness by MR elastography and vibration-controlled transient elastography

Correction

Correction to: A quantitative model based on clinically relevant MRI features differentiates lower grade gliomas and glioblastoma

Ultrasound

US detection of medial meniscus extrusion can predict the risk of developing radiographic knee osteoarthritis: a 5-year cohort study

Neuro

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

Gastrointestinal

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma