Browsing by Author "Wilson, Joshua"

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    ItemOpen Access
    Evaluation of the impact of a novel denoising algorithm on image quality in dual-energy abdominal CT of obese patients.
    (European radiology, 2023-04) Schwartz, Fides R; Clark, Darin P; Rigiroli, Francesca; Kalisz, Kevin; Wildman-Tobriner, Benjamin; Thomas, Sarah; Wilson, Joshua; Badea, Cristian T; Marin, Daniele

    Objectives

    Evaluate a novel algorithm for noise reduction in obese patients using dual-source dual-energy (DE) CT imaging.

    Methods

    Seventy-nine patients with contrast-enhanced abdominal imaging (54 women; age: 58 ± 14 years; BMI: 39 ± 5 kg/m2, range: 35-62 kg/m2) from seven DECT (SOMATOM Flash or Force) were retrospectively included (01/2019-12/2020). Image domain data were reconstructed with the standard clinical algorithm (ADMIRE/SAFIRE 2), and denoised with a comparison (ME-NLM) and a test algorithm (rank-sparse kernel regression). Contrast-to-noise ratio (CNR) was calculated. Four blinded readers evaluated the same original and denoised images (0 (worst)-100 (best)) in randomized order for perceived image noise, quality, and their comfort making a diagnosis from a table of 80 options. Comparisons between algorithms were performed using paired t-tests and mixed-effects linear modeling.

    Results

    Average CNR was 5.0 ± 1.9 (original), 31.1 ± 10.3 (comparison; p < 0.001), and 8.9 ± 2.9 (test; p < 0.001). Readers were in good to moderate agreement over perceived image noise (ICC: 0.83), image quality (ICC: 0.71), and diagnostic comfort (ICC: 0.6). Diagnostic accuracy was low across algorithms (accuracy: 66, 63, and 67% (original, comparison, test)). The noise received a mean score of 54, 84, and 66 (p < 0.05); image quality 59, 61, and 65; and the diagnostic comfort 63, 68, and 68, respectively. Quality and comfort scores were not statistically significantly different between algorithms.

    Conclusions

    The test algorithm produces quantitatively higher image quality than current standard and existing denoising algorithms in obese patients imaged with DECT and readers show a preference for it.

    Clinical relevance statement

    Accurate diagnosis on CT imaging of obese patients is challenging and denoising algorithms can increase the diagnostic comfort and quantitative image quality. This could lead to better clinical reads.

    Key points

    • Improving image quality in DECT imaging of obese patients is important for accurate and confident clinical reads, which may be aided by novel denoising algorithms using image domain data. • Accurate diagnosis on CT imaging of obese patients is especially challenging and denoising algorithms can increase quantitative and qualitative image quality. • Image domain algorithms can generalize well and can be implemented at other institutions.
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    ItemOpen Access
    Organ doses from CT localizer radiographs: Development, validation, and application of a Monte Carlo estimation technique
    (MEDICAL PHYSICS, 2019-11-01) Hoye, Jocelyn; Sharma, Shobhit; Zhang, Yakun; Fu, Wanyi; Ria, Francesco; Kapadia, Anuj; Segars, W Paul; Wilson, Joshua; Samei, Ehsan
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    ItemOpen Access
    Organ Doses from CT Localizer Radiographs: Development, Validation, and Application of a Monte Carlo Estimation Technique.
    (Medical physics, 2019-08-23) Hoye, Jocelyn; Sharma, Shobhit; Zhang, Yakun; Fu, Wanyi; Ria, Francesco; Kapadia, Anuj; Segars, W Paul; Wilson, Joshua; Samei, Ehsan
    PURPOSE:The purpose of this study was to simulate and validate organ doses from different CT localizer radiograph geometries using Monte Carlo methods for a population of patients. METHODS:A Monte Carlo method was developed to estimate organ doses from CT localizer radiographs using PENELOPE. The method was validated by comparing dosimetry estimates with measurements using an anthropomorphic phantom imbedded with thermoluminescent dosimeters (TLDs) scanned on a commercial CT system (Siemens SOMATOM Flash). The Monte Carlo simulation platform was then applied to conduct a population study with fifty-seven adult computational phantoms (XCAT). In the population study, clinically relevant chest localizer protocols were simulated with the x-ray tube in anterior-posterior (AP), right lateral, and PA positions. Mean organ doses and associated standard deviations (in mGy) were then estimated for all simulations. The obtained organ doses were studied as a function of patient chest diameter. Organ doses for breast and lung were compared across different views and represented as a percentage of organ doses from rotational CT scans. RESULTS:The validation study showed an agreement between the Monte Carlo and physical TLD measurements with a maximum percent difference of 15.5% and a mean difference of 3.5% across all organs. The XCAT population study showed that breast dose from AP localizers was the highest with a mean value of 0.24 mGy across patients, while the lung dose was relatively consistent across different localizer geometries. The organ dose estimates were found to vary across the patient population, partially explained by the changes in the patient chest diameter. The average effective dose was 0.18 mGy for AP, 0.09 mGy for lateral, and 0.08 mGy for PA localizer. CONCLUSION:A platform to estimate organ doses in CT localizer scans using Monte Carlo methods was implemented and validated based on comparison with physical dose measurements. The simulation platform was applied to a virtual patient population, where the localizer organ doses were found to range within 0.4-8.6% of corresponding organ doses for a typical CT scan, 0.2-3.3% of organ doses for a CT pulmonary angiography scan, and 1.1-20.8% of organ doses for a low dose lung cancer screening scan.