Purpose: To investigate a novel locally adaptive projection space denoising algorithm for low-dose CT data. Methods: The denoising algorithm is based on bilateral filtering, which smooths values using a weighted average in a local neighborhood, with weights determined according to both spatial proximity and intensity similarity between the center pixel and the neighboring pixels. This filtering is locally adaptive and can preserve important edge information in the sinogram, thus maintaining high spatial resolution. A CT noise model that takes into account the bowtie filter and patientspecific automatic exposure control effects is also incorporated into the denoising process. The authors evaluated the noise-resolution properties of bilateral filtering incorporating such a CT noise model in phantom studies and preliminary patient studies with contrast-enhanced abdominal CT exams. Results: On a thin wire phantom, the noise-resolution properties were significantly improved with the denoising algorithm compared to commercial reconstruction kernels. The noise-resolution properties on low-dose ͑40 mA s͒ data after denoising approximated those of conventional reconstructions at twice the dose level. A separate contrast plate phantom showed improved depiction of low-contrast plates with the denoising algorithm over conventional reconstructions when noise levels were matched. Similar improvement in noise-resolution properties was found on CT colonography data and on five abdominal low-energy ͑80 kV͒ CT exams. In each abdominal case, a board-certified subspecialized radiologist rated the denoised 80 kV images markedly superior in image quality compared to the commercially available reconstructions, and denoising improved the image quality to the point where the 80 kV images alone were considered to be of diagnostic quality. Conclusions:The results demonstrate that bilateral filtering incorporating a CT noise model can achieve a significantly better noise-resolution trade-off than a series of commercial reconstruction kernels. This improvement in noise-resolution properties can be used for improving image quality in CT and can be translated into substantial dose reduction.

Peer‐reviewed journals and conference proceedings publish hundreds of papers that describe new medical imaging algorithms, including, for example, techniques for computer‐aided diagnosis or characterization, segmentation, image registration, image reconstruction, and radiomics. It is difficult, if not impossible, to fairly compare the performance of these algorithms as investigators must either use different data sets, or if using the same data, use different implementations of competing algorithms. Grand Challenges facilitate the fair comparison of algorithms by providing a common data set to all participants and by having each participant be responsible for implementation of their own algorithm. The dissemination of findings from Grand Challenges provides important information to the scientific community and helps to determine which approaches have the greatest promise for successful translation to clinical practice. In this session we will review the outcomes and lessons learned from the 2015 SPIE‐AAPM‐NCI Lung Nodule Classification Challenge. We will then turn to the 2016 NIH‐AAPM‐Mayo Clinic Low Dose CT Grand Challenge, providing an overview of denoising and iterative reconstruction approaches and a description of the Challenge. The top 3 performing participants will be announced, and each will give a short presentation on their technique. Understand the role of Grand Challenges in the field of medical imaging Be able to summarize the outcomes of the 2015 lung nodule classification challenge Be able to review the primary types of noise reduction techniques used in CT Be familiar with a library of patient CT projection data available to researchers Learn which techniques performed best in the Low Dose CT Grand Challenge Pelc: GE Healthcare, Philips Healthcare; McCollough: Research grant, Siemens Healthcare; Low Dose CT Grand Challenge supported by the AAPM Science Council and NIH (grant EB 017185), and hosted by the Mayo Clinic; Giger: stockholder R2 technology/Hologic, royalties from Hologic, GE Medical Systems, MEDIAN Technologies, Riverain Medical, Mitsubishi/Toshiba. Cofounder/stockholder Quantitative Insights.

Interstitial Randall’s plaques and collecting duct plugs are distinct forms of renal calcification thought to provide sites for stone retention within the kidney. Here we assessed kidney stone precursor lesions in a random cohort of stone formers undergoing percutaneous nephrolithotomy. Each accessible papilla was endoscopically mapped following stone removal. The percent papillary surface area covered by plaque and plug were digitally measured using image analysis software. Stone composition was determined by micro-computed tomography and infrared analysis. A representative papillary tip was biopsied. Twenty-four hour urine collections were used to measure supersaturation and crystal growth inhibition. The vast majority (99%) of stone formers had Randall’s plaque on at least 1 papilla, while significant tubular plugging (over 1% of surface area) was present in about one-fifth of patients. Among calcium oxalate stone formers the amount of Randall’s plaque correlated with higher urinary citrate levels. Tubular plugging correlated positively with pH and brushite supersaturation but negatively with citrate excretion. Lower urinary crystal growth inhibition predicted the presence of tubular plugging but not plaque. Thus, tubular plugging may be more common than previously recognized among patients with all types of stones, including some with idiopathic calcium oxalate stones.

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CT colonography performed with multi-detector row CT significantly improved the demonstration of colonic distention and depicted fewer respiratory artifacts compared with single-detector row CT. No significant differences in the depiction of polyps larger than 10 mm were demonstrated between single- and multi-detector row CT for a small number of polyps. Studies with a larger prevalence of clinically important polyps are needed for further evaluation of differences in polyp detection.