Third version of vendor-specific model-based iterativereconstruction (Veo 3.0): evaluation of CT image quality in the abdomen using new noise reduction presets and varied slice optimization.

OBJECTIVE To qualitatively and quantitatively compare abdominal CT images reconstructed with a newversion of model-based iterative reconstruction (Veo 3.0; GE Healthcare Waukesha, WI) utilizing varied presetsof resolution preference, noise reduction and slice optimization. METHODS This retrospective study was approved by our Institutional Review Board and was Health Insurance Portability and Accountability Act compliant. The raw datafrom 30 consecutive patients who had undergone CT abdomen scanning were used to reconstructfour clinical presets of 3.75mm axial images using Veo 3.0: 5% resolution preference (RP05n), 5%noise reduction (NR05) and 40% noise reduction (NR40) with new 3.75mm "sliceoptimization," as well as one set using RP05 with conventional 0.625mm "slice optimization" (RP05c). The images were reviewed by two independent readers in a blinded, randomized manner using a 5-point Likert scale as well as a 5-point comparative scale. Multiple two-dimensional circular regions of interest were defined for noise and contrast-to-noise ratio measurements. Line profiles were drawn across the 7 lp cm-1 bar pattern of the Catphan 600 phantom for evaluation of spatial resolution. RESULTS The NR05 image set was ranked as the best series in overall image quality (mean difference inrank 0.48, 95% CI [0.081-0.88], p = 0.01) and with specific reference to liver evaluation (meandifference 0.46, 95% CI [0.030-0.89], p = 0.03), when compared with the secondbest series ineach category. RP05n was ranked as the best for bone evaluation. NR40 was ranked assignificantly inferior across all assessed categories. Although the NR05 and RP05c image setshad nearly the same contrast-to-noise ratio and spatial resolution, NR05 was generally preferred. Image noise and spatial resolution increased along a spectrum with RP05n the highest and NR40the lowest. Compared to RP05n, the average noise was 21.01% lower for NR05, 26.88%lower for RP05c and 50.86% lower for NR40. CONCLUSION Veo 3.0 clinical presets allow for selection of image noise and spatial resolution balance; for contrast-enhanced CT evaluation of the abdomen, the 5% noise reduction preset with 3.75 mm slice optimization (NR05) was generally ranked superior qualitatively and, relative to other series, was in the middle of the spectrum with reference to image noise and spatial resolution. Advances in knowledge: To our knowledge, this is the first study of Veo 3.0 noise reduction presets and varied slice optimization. This study provides insight into the behaviour of slice optimization and documents the degree of noise reduction and spatial resolution changes that users can expect across various Veo 3.0 clinical presets. These results provide important parameters to guide preset selection for both clinical and research purposes.

[1]  M. Goodsitt,et al.  Effect of Model-Based Iterative Reconstruction on CT Number Measurements Within Small (10-29 mm) Low-Attenuation Renal Masses. , 2015, AJR. American journal of roentgenology.

[2]  Joon Koo Han,et al.  Assessment of a Model-Based, Iterative Reconstruction Algorithm (MBIR) Regarding Image Quality and Dose Reduction in Liver Computed Tomography , 2013, Investigative radiology.

[3]  K. Ohtomo,et al.  High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction. , 2016, European journal of radiology.

[4]  Atul Padole,et al.  Dose reduction in pediatric abdominal CT: use of iterative reconstruction techniques across different CT platforms , 2015, Pediatric Radiology.

[5]  Joon Koo Han,et al.  Liver Computed Tomography With Low Tube Voltage and Model-Based Iterative Reconstruction Algorithm for Hepatic Vessel Evaluation in Living Liver Donor Candidates , 2014, Journal of computer assisted tomography.

[6]  Ehsan Samei,et al.  Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology. , 2014, Medical physics.

[7]  Masaki Katsura,et al.  Model-Based Iterative Reconstruction Technique for Ultralow-Dose Chest CT: Comparison of Pulmonary Nodule Detectability With the Adaptive Statistical Iterative Reconstruction Technique , 2013, Investigative radiology.

[8]  Mannudeep K. Kalra,et al.  Comparison of Hybrid and Pure Iterative Reconstruction Techniques With Conventional Filtered Back Projection: Dose Reduction Potential in the Abdomen , 2012, Journal of computer assisted tomography.

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

[10]  Sebastian Feuerlein,et al.  New iterative reconstruction techniques for cardiovascular computed tomography: how do they work, and what are the advantages and disadvantages? , 2011, Journal of cardiovascular computed tomography.

[11]  D. Volders,et al.  Model-based iterative reconstruction and adaptive statistical iterative reconstruction techniques in abdominal CT: comparison of image quality in the detection of colorectal liver metastases. , 2013, Radiology.

[12]  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.

[13]  M. Körner,et al.  Filtered back projection, adaptive statistical iterative reconstruction, and a model-based iterative reconstruction in abdominal CT: an experimental clinical study. , 2013, Radiology.

[14]  E. Samei,et al.  Low-tube-voltage, high-tube-current multidetector abdominal CT: improved image quality and decreased radiation dose with adaptive statistical iterative reconstruction algorithm--initial clinical experience. , 2010, Radiology.

[15]  David J Manning,et al.  Ambient lighting: effect of illumination on soft-copy viewing of radiographs of the wrist. , 2007, AJR. American journal of roentgenology.

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

[17]  Keith T. Chan,et al.  Standard and reduced radiation dose liver CT images: adaptive statistical iterative reconstruction versus model-based iterative reconstruction-comparison of findings and image quality. , 2014, Radiology.

[18]  Guang Li,et al.  A noise power spectrum study of a new model‐based iterative reconstruction system: Veo 3.0 , 2016, Journal of applied clinical medical physics.

[19]  Dianna D. Cody,et al.  Performance evaluation of iterative reconstruction algorithms for achieving CT radiation dose reduction — a phantom study , 2016, Journal of applied clinical medical physics.

[20]  Varut Vardhanabhuti,et al.  Image Comparative Assessment Using Iterative Reconstructions: Clinical Comparison of Low-Dose Abdominal/Pelvic Computed Tomography Between Adaptive Statistical, Model-Based Iterative Reconstructions and Traditional Filtered Back Projection in 65 Patients , 2014, Investigative radiology.

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

[22]  Wei Wei,et al.  Evaluation of Abdominal Computed Tomography Image Quality Using a New Version of Vendor-Specific Model-Based Iterative Reconstruction , 2017, Journal of computer assisted tomography.

[23]  Jean-Baptiste Thibault,et al.  Model-based iterative reconstruction versus adaptive statistical iterative reconstruction and filtered back projection in liver 64-MDCT: focal lesion detection, lesion conspicuity, and image noise. , 2013, AJR. American journal of roentgenology.