Literature DB >> 15107323

The addition of computer simulated noise to investigate radiation dose and image quality in images with spatial correlation of statistical noise: an example application to X-ray CT of the brain.

A J Britten1, M Crotty, H Kiremidjian, A Grundy, E J Adam.   

Abstract

This study validates a method to add spatially correlated statistical noise to an image, applied to transaxial X-ray CT images of the head to simulate exposure reduction by up to 50%. 23 patients undergoing routine head CT had three additional slices acquired for validation purposes, two at the same clinical 420 mAs exposure and one at 300 mAs. Images at the level of the cerebrospinal fluid filled ventricles gave readings of noise from a single image, with subtraction of image pairs to obtain noise readings from non-uniform tissue regions. The spatial correlation of the noise was determined and added to the acquired 420 mAs image to simulate images at 340 mAs, 300 mAs, 260 mAs and 210 mAs. Two radiologists assessed the images, finding little difference between the 300 mAs simulated and acquired images. The presence of periventricular low density lesions (PVLD) was used as an example of the effect of simulated dose reduction on diagnostic accuracy, and visualization of the internal capsule was used as a measure of image quality. Diagnostic accuracy for the diagnosis of PVLD did not fall significantly even down to 210 mAs, though visualization of the internal capsule was poorer at lower exposure. Further work is needed to investigate means of measuring statistical noise without the need for uniform tissue areas, or image pairs. This technique has been shown to allow sufficiently accurate simulation of dose reduction and image quality degradation, even when the statistical noise is spatially correlated.

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Mesh:

Year:  2004        PMID: 15107323     DOI: 10.1259/bjr/78576048

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  19 in total

1.  A computer simulation method for low-dose CT images by use of real high-dose images: a phantom study.

Authors:  Tomomi Takenaga; Shigehiko Katsuragawa; Makoto Goto; Masahiro Hatemura; Yoshikazu Uchiyama; Junji Shiraishi
Journal:  Radiol Phys Technol       Date:  2015-08-20

2.  Validation of CT dose-reduction simulation.

Authors:  Parinaz Massoumzadeh; Steven Don; Charles F Hildebolt; Kyongtae T Bae; Bruce R Whiting
Journal:  Med Phys       Date:  2009-01       Impact factor: 4.071

3.  Effect of kVp on image quality and accuracy in coronary CT angiography according to patient body size: a phantom study.

Authors:  Sang Min Lee; Whal Lee; Jin Wook Chung; Eun-Ah Park; Jae Hyung Park
Journal:  Int J Cardiovasc Imaging       Date:  2013-11-02       Impact factor: 2.357

4.  Improving low-dose blood-brain barrier permeability quantification using sparse high-dose induced prior for Patlak model.

Authors:  Ruogu Fang; Kolbeinn Karlsson; Tsuhan Chen; Pina C Sanelli
Journal:  Med Image Anal       Date:  2013-10-17       Impact factor: 8.545

5.  Tissue-specific sparse deconvolution for low-dose CT perfusion.

Authors:  Ruogu Fang; Tsuhan Chen; Pina C Sanelli
Journal:  Med Image Comput Comput Assist Interv       Date:  2013

6.  CTA-enhanced perfusion CT: an original method to perform ultra-low-dose CTA-enhanced perfusion CT.

Authors:  Elizabeth Tong; Max Wintermark
Journal:  Neuroradiology       Date:  2014-08-02       Impact factor: 2.804

7.  Low-dose preview for patient-specific, task-specific technique selection in cone-beam CT.

Authors:  Adam S Wang; J Webster Stayman; Yoshito Otake; Sebastian Vogt; Gerhard Kleinszig; A Jay Khanna; Gary L Gallia; Jeffrey H Siewerdsen
Journal:  Med Phys       Date:  2014-07       Impact factor: 4.071

8.  The quality/safety medical index: a standardized method for concurrent optimization of radiation dose and image quality in medical imaging.

Authors:  Bruce I Reiner
Journal:  J Digit Imaging       Date:  2014-12       Impact factor: 4.056

9.  Development and validation of a low dose simulator for computed tomography.

Authors:  R M S Joemai; J Geleijns; W J H Veldkamp
Journal:  Eur Radiol       Date:  2009-09-30       Impact factor: 5.315

10.  Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries.

Authors:  Ruogu Fang; Tsuhan Chen; Pina C Sanelli
Journal:  Med Image Comput Comput Assist Interv       Date:  2012
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