Statistical model based iterative reconstruction (MBIR) in clinical CT systems. Part II. Experimental assessment of spatial resolution performance.

PURPOSE Statistical model based iterative reconstruction (MBIR) methods have been introduced to clinical CT systems and are being used in some clinical diagnostic applications. The purpose of this paper is to experimentally assess the unique spatial resolution characteristics of this nonlinear reconstruction method and identify its potential impact on the detectabilities and the associated radiation dose levels for specific imaging tasks. METHODS The thoracic section of a pediatric phantom was repeatedly scanned 50 or 100 times using a 64-slice clinical CT scanner at four different dose levels [CTDIvol =4, 8, 12, 16 (mGy)]. Both filtered backprojection (FBP) and MBIR (Veo(®), GE Healthcare, Waukesha, WI) were used for image reconstruction and results were compared with one another. Eight test objects in the phantom with contrast levels ranging from 13 to 1710 HU were used to assess spatial resolution. The axial spatial resolution was quantified with the point spread function (PSF), while the z resolution was quantified with the slice sensitivity profile. Both were measured locally on the test objects and in the image domain. The dependence of spatial resolution on contrast and dose levels was studied. The study also features a systematic investigation of the potential trade-off between spatial resolution and locally defined noise and their joint impact on the overall image quality, which was quantified by the image domain-based channelized Hotelling observer (CHO) detectability index d'. RESULTS (1) The axial spatial resolution of MBIR depends on both radiation dose level and image contrast level, whereas it is supposedly independent of these two factors in FBP. The axial spatial resolution of MBIR always improved with an increasing radiation dose level and/or contrast level. (2) The axial spatial resolution of MBIR became equivalent to that of FBP at some transitional contrast level, above which MBIR demonstrated superior spatial resolution than FBP (and vice versa); the value of this transitional contrast highly depended on the dose level. (3) The PSFs of MBIR could be approximated as Gaussian functions with reasonably good accuracy. (4) Thez resolution of MBIR showed similar contrast and dose dependence. (5) Noise standard deviation assessed on the edges of objects demonstrated a trade-off with spatial resolution in MBIR. (5) When both spatial resolution and image noise were considered using the CHO analysis, MBIR led to significant improvement in the overall CT image quality for both high and low contrast detection tasks at both standard and low dose levels. CONCLUSIONS Due to the intrinsic nonlinearity of the MBIR method, many well-known CT spatial resolution and noise properties have been modified. In particular, dose dependence and contrast dependence have been introduced to the spatial resolution of CT images by MBIR. The method has also introduced some novel noise-resolution trade-off not seen in traditional CT images. While the benefits of MBIR regarding the overall image quality, as demonstrated in this work, are significant, the optimal use of this method in clinical practice demands a thorough understanding of its unique physical characteristics.

[1]  E L Nickoloff,et al.  A simplified approach for modulation transfer function determinations in computed tomography. , 1985, Medical physics.

[2]  H H Barrett,et al.  Addition of a channel mechanism to the ideal-observer model. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[3]  Ken D. Sauer,et al.  A local update strategy for iterative reconstruction from projections , 1993, IEEE Trans. Signal Process..

[4]  Jeffrey A. Fessler,et al.  Ieee Transactions on Image Processing: to Appear Globally Convergent Algorithms for Maximum a Posteriori Transmission Tomography , 2022 .

[5]  Alfred O. Hero,et al.  Ieee Transactions on Image Processing: to Appear Penalized Maximum-likelihood Image Reconstruction Using Space-alternating Generalized Em Algorithms , 2022 .

[6]  Craig K. Abbey,et al.  Observer signal-to-noise ratios for the ML-EM algorithm , 1996, Medical Imaging.

[7]  Ken D. Sauer,et al.  A unified approach to statistical tomography using coordinate descent optimization , 1996, IEEE Trans. Image Process..

[8]  A E Burgess,et al.  Visual signal detectability with two noise components: anomalous masking effects. , 1997, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  Matt A. King,et al.  Channelized hotelling and human observer correlation for lesion detection in hepatic SPECT imaging. , 2000, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[10]  A. Burgess,et al.  Human observer detection experiments with mammograms and power-law noise. , 2001, Medical physics.

[11]  K. Hoffmann,et al.  Generalizing the MTF and DQE to include x-ray scatter and focal spot unsharpness: application to a new microangiographic system. , 2005, Medical physics.

[12]  Jean-Baptiste Thibault,et al.  A three-dimensional statistical approach to improved image quality for multislice helical CT. , 2007, Medical physics.

[13]  Jie Tang,et al.  Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets. , 2008, Medical physics.

[14]  J Yorkston,et al.  Task-based modeling and optimization of a cone-beam CT scanner for musculoskeletal imaging. , 2011, Medical physics.

[15]  Zhou Yu,et al.  Fast Model-Based X-Ray CT Reconstruction Using Spatially Nonhomogeneous ICD Optimization , 2011, IEEE Transactions on Image Processing.

[16]  David H. Kim,et al.  Abdominal CT with model-based iterative reconstruction (MBIR): initial results of a prospective trial comparing ultralow-dose with standard-dose imaging. , 2012, AJR. American journal of roentgenology.

[17]  Guang-Hong Chen,et al.  Characterization of statistical prior image constrained compressed sensing. I. Applications to time-resolved contrast-enhanced CT. , 2012, Medical physics.

[18]  Ehsan Samei,et al.  Relevance of MTF and NPS in quantitative CT: towards developing a predictable model of quantitative performance , 2012, Medical Imaging.

[19]  Ehsan Samei,et al.  Towards task-based assessment of CT performance: System and object MTF across different reconstruction algorithms. , 2012, Medical physics.

[20]  Jie Tang,et al.  Prior image constrained compressed sensing: implementation and performance evaluation. , 2011, Medical physics.

[21]  Ehsan Samei,et al.  Are uniform phantoms sufficient to characterize the performance of iterative reconstruction in CT? , 2013, Medical Imaging.

[22]  Shuai Leng,et al.  Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: impact of radiation dose and reconstruction algorithms. , 2013, Medical physics.

[23]  M. Robins,et al.  Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR). , 2013, Medical physics.

[24]  Guang-Hong Chen,et al.  Spatial resolution characterization of differential phase contrast CT systems via modulation transfer function (MTF) measurements , 2013, Physics in medicine and biology.

[25]  Guang-Hong Chen,et al.  Characterization of statistical prior image constrained compressed sensing (PICCS): II. Application to dose reduction. , 2013, Medical physics.

[26]  Masaki Katsura,et al.  Model-based iterative reconstruction for reduction of radiation dose in abdominopelvic CT: comparison to adaptive statistical iterative reconstruction , 2013, SpringerPlus.

[27]  M. Goodsitt,et al.  Model-based iterative reconstruction: effect on patient radiation dose and image quality in pediatric body CT. , 2013, Radiology.

[28]  Marc Kachelrieß,et al.  Effects of ray profile modeling on resolution recovery in clinical CT. , 2014, Medical physics.

[29]  Ke Li,et al.  Statistical model based iterative reconstruction (MBIR) in clinical CT systems: experimental assessment of noise performance. , 2014, Medical physics.