Low-dose chest CT of a patient with a pneumonic infiltrate of the middle lobe. Original image with 80kVp, 15 mAs, radiation dose 0.2 mGy (bottom left). Processing with Pixelshine (top left). A comparable image would be created with about 120 kVp, 150 mAs and a radiation dose of about 8 mGy. The radiation dose is reduced by more than 95% with PixelShine.
Low-dose CT of the abdomen. Original image with 120kVp, slice thickness 1.25 mm, radiation dose 1.7 mGy (bottom middle). Processing with Pixelshine (top middle). A comparable image would be created with a radiation dose of about 10 mGy. The radiation dose is reduced by more than 80% with PixelShine.
CT of the brain. Original image with 120kVp, slice thickness 0.625 mm, radiation dose 11 mGy (bottom right). Processing with Pixelshine (top right). A comparable image would be created with a radiation dose of about 40 mGy. The radiation dose is reduced by around 75% with PixelShine.
For several years, medicine has been generating significantly more radiation doses than the natural radiation from the cosmos and the earth ever did. The main reason for this is the constantly increasing radiological use of computed tomography (CT). Precisely because CT is and will remain essential for adequate patient care in almost all diagnostic areas, it is up to us to keep the long-documented radiation-induced cancer risk at the lowest possible level.
It is with great pleasure that we present PixelShine, an AI assistant for CT radiation dose reduction.
Why PixelShine matters
Both in the hospital and in the radiological institute, care is taken to ensure that each patient is only given the necessary radiation dose. However, these low-dose CT protocols almost always produce noisy images, and the CT studies are often difficult to interpret even for medical specialists. In addition, radiologists often have to read CT studies from CT machines from different vendors, which contributes to inconvenience and delays in the workflow.
PixelShine allows two things: Firstly, low-dose CT studies can be carried out for all patients in terms of optimal radiation protection, and PixelShine subsequently generates significantly improved quality from these noisy images, for example in obese patients. Secondly, the lifespan of CT scanners is extended by reducing the load on the CT tubes.
When and how to use PixelShine
PixelShine can be used for studies of any CT device age and vendor. This AI assistant improves radiological precision by homogenizing the workflow.
PixelShine enables radiologists to read noisy CT studies with a high noise level of image noise in the best possible way, and the radiological quality meets the requirements for diagnostic validity.
Furthermore, hospitals and radiological institutes can carry out low-dose CT studies as standard, integrate PixelShine in post-processing and thus achieve consistently high image quality.
Who benefits
Patients, clinicians, radiologists and the management of hospitals and radiological institutes: care for all patients by minimizing the radiation dose, clear CT images, optimal assessment and discussion of findings, money savings by extending the lifespan of CT scanners.
Our own experience at Radailogy
Our customers send us CT studies to improve image quality with PixelShine and enable optimal diagnostic results. Both in individual cases through the simple upload to Radailogy, as well as as a standard in daily cooperation with our telemedicine.
Selection of scientific publications
Hata A, Yanagawa M, Yoshida Y, et al. Combination of Deep Learning–Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation. American Journal of Roentgenology. 2020;215(6):1321-1328.
Steuwe A, Weber M, Bethge OT, et al. Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography. BJR. 2021;94(1117):20200677.
Brendlin AS, Plajer D, Chaika M, et al. AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging. Diagnostics. 2022;12(1):225.
Hasegawa A, Ishihara T, Thomas MA, Pan T. Noise reduction profile: A new method for evaluation of noise reduction techniques in CT. Medical Physics. 2022;49(1):186-200.
Nagaraj Y, de Jonge G, Andreychenko A, et al. Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting. Eur Radiol. 2022;32(9):6384-6396.
Hasegawa A, Ishihara T, Thomas MA, Pan T. Noise reduction profile: A new method for evaluation of noise reduction techniques in CT. Medical Physics. 2022;49(1):186-200.
Data to upload to Radailogy
CT studies of any CT scanner age and vendor