Can conclusions drawn from phantom‐based image noise assessments be generalized to in vivo studies for the nonlinear model‐based iterative reconstruction method?. Issue 2 (13th January 2016)

• Record Type: Journal Article
• Authors: Gomez‐Cardona, Daniel; Li, Ke; Hsieh, Jiang; Lubner, Meghan G.; Pickhardt, Perry J.; Chen, Guang‐Hong
• Is Part Of: Medical physics. Volume 43:Issue 2(2016)
• Page Start: 687
• Page End: 695
• Publication Date: 2016-01-13
• Subjects: computerised tomography -- image reconstruction -- iterative methods -- least squares approximations -- liver -- medical image processing -- phantoms -- regression analysis; Computed tomography -- Reconstruction -- Numerical approximation and analysis -- Probability theory, stochastic processes, and statistics -- General statistical methods; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; CT -- model based iterative reconstruction (MBIR) -- radiation dose reduction -- noise variance -- mAs; Computed tomography -- Medical image noise -- Liver -- Medical image reconstruction -- Image quality assessment -- Data analysis -- Medical image contrast -- Germanium; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4939257
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Phantom‐based objective image quality assessment methods are widely used in the medical physics community. For a filtered backprojection (FBP) reconstruction‐based linear or quasilinear imaging system, the use of this methodology is well justified. Many key image quality metrics acquired with phantom studies can be directly applied to in vivo human subject studies. Recently, a variety of image quality metrics have been investigated for model‐based iterative image reconstruction (MBIR) methods and several novel characteristics have been discovered in phantom studies. However, the following question remains unanswered: can certain results obtained from phantom studies be generalized to in vivo animal studies and human subject studies? The purpose of this paper is to address this question. Methods: One of the most striking results obtained from phantom studies is a novel power‐law relationship between noise variance of MBIR ( σ 2 ) and tube current‐rotation time product (mAs): σ 2 ∝ (mAs) −0.4 [K. Li et al., "Statistical model based iterative reconstruction (MBIR) in clinical CT systems: Experimental assessment of noise performance, " Med. Phys.41, 041906 (15pp.) (2014)]. To examine whether the same power‐law works for in vivo cases, experimental data from two types of in vivo studies were analyzed in this paper. All scans were performed with a 64‐slice diagnostic CT scanner (Discovery CT750 HD, GE Healthcare) and reconstructed with both FBP and a MBIR method (Veo, GE Healthcare). An Institutional Animal Care and Use Committee‐approved in vivo animal study was performed with an adult swine at six mAs levels (10–290). Additionally, human subject data (a total of 110 subjects) acquired from an IRB‐approved clinical trial were analyzed. In this clinical trial, a reduced‐mAs scan was performed immediately following the standard mAs scan; the specific mAs used for the two scans varied across human subjects and were determined based on patient size and clinical indications. The measurements of σ 2 were performed at different mAs by drawing regions‐of‐interest (ROIs) in the liver and the subcutaneous fat. By applying a linear least‐squares regression, the β values in the power‐law relationship σ 2 ∝ (mAs) − β were measured for the in vivo data and compared with the value found in phantom experiments. Results: For the in vivo swine study, an exponent of β = 0.43 was found for MBIR, and the coefficient of determination ( R 2 ) for the corresponding least‐squares power‐law regression was 0.971. As a reference, the β and R 2 values for FBP were found to be 0.98 and 0.997, respectively, from the same study, which are consistent with the well‐known σ 2 ∝ (mAs) −1.0 relationship for linear CT systems. For the human subject study, the measured β values for the MBIR images were 0.41 ± 0.12 in the liver and 0.37 ± 0.12 in subcutaneous fat. In comparison, the β values for the FBP images were 1.04 ± 0.10 in the liver and 0.97 ± 0.12 in subcutaneous fat. The β values of MBIR and FBP obtained from the in vivo studies were found to be statistically equivalent to the corresponding β values from the phantom study within an equivalency interval of [− 0.1, 0.1] ( p < 0.05); across MBIR and FBP, the difference in β was statistically significant ( p < 0.05). Conclusions: Despite the nonlinear nature of the MBIR method, the power‐law relationship, σ 2 ∝ (mAs) −0.4, found from phantom studies can be applied to in vivo animal and human subject studies.