The purpose of this study was to reduce the radiation dosage associated with computed tomography (CT) lung cancer screening while maintaining overall diagnostic image quality and definition of ground‐glass opacities (GGOs). A lung screening phantom and a multipurpose chest phantom were used to quantitatively assess the performance of two iterative image reconstruction algorithms (adaptive statistical iterative reconstruction (ASIR) and model‐based iterative reconstruction (MBIR)) used in conjunction with reduced tube currents relative to a standard clinical lung cancer screening protocol (51 effective mAs (3.9 mGy) and filtered back‐projection (FBP) reconstruction). To further assess the algorithms' performances, qualitative image analysis was conducted (in the form of a reader study) using the multipurpose chest phantom, which was implanted with GGOs of two densities. Our quantitative image analysis indicated that tube current, and thus radiation dose, could be reduced by 40% or 80% from ASIR or MBIR, respectively, compared with conventional FBP, while maintaining similar image noise magnitude and contrast‐to‐noise ratio. The qualitative portion of our study, which assessed reader preference, yielded similar results, indicating that dose could be reduced by 60% (to 20 effective mAs (1.6 mGy)) with either ASIR or MBIR, while maintaining GGO definition. Additionally, the readers' preferences (as indicated by their ratings) regarding overall image quality were equal or better (for a given dose) when using ASIR or MBIR, compared with FBP. In conclusion, combining ASIR or MBIR with reduced tube current may allow for lower doses while maintaining overall diagnostic image quality, as well as GGO definition, during CT lung cancer screening.PACS numbers: 87.57.Q‐, 87.57.nf

The effect of a patient's size on the HU values of lung and bone tissues can be significant. The accuracy of those HU values was substantially improved by the correction method that was developed for and employed in this study.

The software appears promising for objectively and automatically identifying suboptimal images in a clinical imaging operation. The results can be used to establish local quality consistency ranges and action limits per facility preferences.

Purpose: The purpose of this study was to determine the quantitative variability of diffusion weighted imaging and apparent diffusion coefficient values across a large fleet of MR systems. Using a NIST traceable magnetic resonance imaging diffusion phantom, imaging was reproducible and the measurements were quantitatively compared to known values. Methods: A fleet of 23 clinical MRI scanners was investigated in this study. A NIST/QIBA DWI phantom was imaged with protocols provided with the phantom. The resulting images were analyzed and ADC maps were generated. User-directed region-of-interests on each of the different vials provided ADC measurements among a wide range of known ADC values. Results: Three diffusion phantoms were used in this study and compared to one another. From the one-way analysis of the variance, the mean and standard deviation of the percent errors from each phantom were not significantly different from one another. The low ADC vials showed larger errors and variation and appear directly related to SNR. Across all the MR systems and data, the coefficient of variation was calculated and Bland-Altman analysis was performed. ADC measurements were similar to one another except for the vials with the lower ADC values, which had a higher coefficient of variation. Conclusion: ADC values among the three phantoms showed good agreement and were not significantly different from one another. The large percent errors seen primarily at the low ADC values were shown to be a consequence of the SNR dependence and very little bias was observed between magnetic strengths and manufacturers. ADC values between diffusion phantoms were not statistically significant. Future investigations will be performed to study differences in magnetic field strength, vendor, MR system models, gradients, and bore size. More data across different MR platforms would facilitate quantitative measurements for multi-platform and multi-site imaging studies. With the increasing usage of diffusion weighted imaging in the clinic, the characterization of ADC variability for MR systems provides an improved quality control over the MR systems.

Cardiac imaging is a promising application for combined PET/MR imaging. However, current MR imaging protocols for whole‐body attenuation correction can produce spatial mismatch between PET and MR‐derived attenuation data owing to a disparity between the two modalities' imaging speeds. We assessed the feasibility of using a respiration‐averaged MR (AMR) method for attenuation correction of cardiac PET data in PET/MR images. First, to demonstrate the feasibility of motion imaging with MR, we used a 3T MR system and a two‐dimensional fast spoiled gradient‐recalled echo (SPGR) sequence to obtain AMR images of a moving phantom. Then, we used the same sequence to obtain AMR images of a patient's thorax under free‐breathing conditions. MR images were converted into PET attenuation maps using a three‐class tissue segmentation method with two sets of predetermined CT numbers, one calculated from the patient‐specific (PS) CT images and the other from a reference group (RG) containing 54 patient CT datasets. The MR‐derived attenuation images were then used for attenuation correction of the cardiac PET data, which were compared to the PET data corrected with average CT (ACT) images. In the myocardium, the voxel‐by‐voxel differences and the differences in mean slice activity between the AMR‐corrected PET data and the ACT‐corrected PET data were found to be small (less than 7%). The use of AMR‐derived attenuation images in place of ACT images for attenuation correction did not affect the summed stress score. These results demonstrate the feasibility of using the proposed SPGR‐based MR imaging protocol to obtain patient AMR images and using those images for cardiac PET attenuation correction. Additional studies with more clinical data are warranted to further evaluate the method.PACS number: 87.57.uk