Highlights
- •A method to measure low-contrast high-noise SSP of nonlinear CT images was developed.
- •Low-contrast plastic sheet is used as a weak impulsive test object.
- •Accurate SSP is obtainable even when a test object is indistinct against noise.
- •Each different peculiar behavior of low-CNR SSP is disclosed for three IR methods.
Abstract
Noise reduction features of iterative reconstruction (IR) methods in computed tomography
might accompany the sacrifice of the longitudinal resolution, or slice sensitivity
profile (SSP), at low contrast-to-noise ratio (CNR) conditions. To assess the benefit
of IR methods correctly, the difference of SSP between IR methods and filtered-backprojection
(FBP) must be taken into account. Therefore, SSP measurement under low-CNR conditions
is necessary. Although edge methods are predominantly used, their performance under
low-CNR conditions appears to be not fully established. We developed a method that
is compatible with extremely low-CNR conditions. Thin plastic disk-shaped sheets embedded
in acrylic resin were used as low-contrast test objects. The lowest peak contrast
used was approximately 17 [HU]. We assessed the performance of our method by using
FBP images. We identified a source of measurement instability aside from noise: the
measured thin-slice SSP is dependent on the orbital phase of helical scan, presumably
because of cone–beam artifacts. This impediment to high accuracy is manageable using
phase-controlled scans. We confirmed that table position repeatability is much better
than the value of the specifications, and therefore the ensemble-averaged images of
multiple scans can be used for SSP measurement. Accurate measurement of SSP under
extremely low-CNR conditions is possible, even when the test object is visually indiscernible
from the noisy background. Low-contrast SSP behavior is elucidated for IR methods
(AIDR-3D, FIRST, and AiSR-V) by using this measurement method.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Physica Medica: European Journal of Medical PhysicsAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
American Association of Physicists in Medicine. Phantoms for Performance Evaluation and Quality Assurance of CT Scanners. AAPM Report No. 1, New York, 1977.
- Measurement of slice sensitivity profiles in spiral CT.Med. Phys. 1994; 21: 133-140
- Ramp test objects for slice sensitivity profile measurement in spiral CT.Br. J. Radiol. 1997; 70: 942-945
- Toward standardized quantitative image quality (IQ) assessment in computed tomography (CT): a comprehensive framework for automated and comparative IQ analysis based on ICRU Report 87.Phys. Med. 2016; 32: 104-115
- The noise power spectrum of CT images.Phys. Med. Biol. 1987; 32: 565-575
- The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence.Eur. Radiol. 2018; (accessed Feb 2019)https://doi.org/10.1007/s00330-018-5810-7
- Towards task-based assessment of CT performance: system and object MTF across different reconstruction algorithms.Med. Phys. 2012; 39: 4115-4122
- Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology.Med. Phys. 2015; 42: 314-323
- Noise properties of maximum likelihood reconstruction with edge-preserving regularization in transmission tomography.Proc. Fully. 2009; 3D: 263-266
- Non-linear Nature of Recent CT Images and Image Quality Evaluation.Bulletin of School of Health Sciences Tohoku University, 2013: 7-24 (in Japanese)
- Noise level and contrast dependent behavior of MTF in iterative reconstruction CT imaging.Med. Phys. 2012; 39: 3636-3637
- Correlation between a 2D channelized Hotelling observer and human observers in a low-contrast detection task with multislice reading in CT.Med Phys. 2017; 44: 3990-3999
- Objective assessment of low contrast detectability in computed tomography with channelized hotelling observer.Phys. Med. 2016; 32: 76-83
- Adaptive iterative dose reduction 3D versus filtered back projection in CT: evaluation of image quality.Am. J. Roentgenol. 2013; 201: 1291-1297
- Deriving the modulation transfer function of CT from extremely noisy edge profiles.Radiol. Phys. Technol. 2009; 2: 22-32
- Patient-specific quantification of image quality: an automated method for measuring spatial resolution in clinical CT images.Med. Phys. 2016; 43: 5330-5338
- Object shape dependency of in-plane resolution for iterative reconstruction of computed tomography.Phys. Med. 2017; 33: 146-151
- Tilted-wire method for measuring resolution properties of CT images under extremely low-contrast and high-noise conditions.Radiol. Phys. Technol. 2018; 11: 125-137
- Spatial resolution measurement for iterative reconstruction by use of image-averaging techniques in computed tomography.Radiol. Phys. Technol. 2014; 7: 358-366
- Technical Note: Measuring contrast- and noise-dependent spatial resolution of an iterative reconstruction method in CT using ensemble averaging.Med. Phys. 2015; 42: 2261-2267
- A limit on dose reduction possible with CT reconstruction algorithms without prior knowledge of the scan subject.Med. Phys. 2016; 43: 1361-1368
- Assessment of volumetric noise and resolution performance for linear and nonlinear CT reconstruction methods.Med. Phys. 2014; 41 (071909)
- Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance.Med. Phys. 2014; 41: 071911https://doi.org/10.1118/1.4884038
- A local shift-variant Fourier model and experimental validation of circular cone–beam computed tomography artifacts.Med. Phys. 2009; 36: 500-512
- A new method for evaluation of slice sensitivity profiles (SSPz) for spatial variation in 64-channel MSCT.Proc. SPIE. 2007; 6510: 65104N
- Measurement of modulation transfer function in Z-axis for multi-slice spiral CT using the micro-disk method: comparison with the bead method and examination of geometric influence.Jpn. J. Radiol. Technol. 2003; 59 (in Japanese): 1391-1398
- Measurement of three-dimensional point spread functions in multidetector-row CT. Proc. SPIE 6913, Medical Imaging.Phys. Med. Imag. 2008; https://doi.org/10.1117/12.770861
- Diagnostic performance and dose comparison of filtered back projection and adaptive iterative dose reduction three-dimensional CT enterography in children and young adults.Radiology. 2015; 276: 233-242
- The feasibility of Forward-projected model-based Iterative Reconstruction SoluTion (FIRST) for coronary 320-row computed tomography angiography: a pilot study.J. Cardiovasc. Comput. Tomogr. 2017; 11: 40-45
- Visualization of simulated small vessels on computed tomography using a model-based iterative reconstruction technique.Data Brief. 2017; 13: 437-443
- Forward-projected model-based iterative reconstruction in screening low-dose chest CT: comparison with adaptive iterative dose reduction 3D.Am. J. Roentgenol. 2018; 211: 548-556
- 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. 2015; 88 (20150463)
- A third-generation adaptive statistical iterative reconstruction technique: phantom study of image noise, spatial resolution, lesion detectability, and dose reduction potential.Am. J. Roentgenol. 2018; 210: 1301-1308
- New adaptive statistical iterative reconstruction ASiR-V: assessment of noise performance in comparison to ASiR.J. Appl. Clin. Med. Phys. 2018 Mar; 19: 275-286
Wolfram Research, Inc., Mathematica version 10.3, Wolfram Research, Inc., Champaign, Illinois, 2015.
Article info
Publication history
Published online: March 30, 2019
Accepted:
March 11,
2019
Received in revised form:
March 3,
2019
Received:
May 27,
2018
Identification
Copyright
© 2019 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.