ArticleThe British journal of radiology2014
Real-time image-processing algorithm for markerless tumour tracking using X-ray fluoroscopic imaging.
Article in The British journal of radiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 13 citations in OpenAlex.
- Investigation of Deconvolution Method with Adaptive Point Spread Function Based on Scintillator Thickness in Wavelet Domain.Bioengineering (Basel, Switzerland) · 2024Article
- Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.Radiological physics and technology · 2018Article
- The Emerging Role of Carbon-Ion Radiotherapy.Frontiers in oncology · 2016Article
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1 author.
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Abstract
objectiveTo ensure accuracy in respiratory-gating treatment, X-ray fluoroscopic imaging is used to detect tumour position in real time. Detection accuracy is strongly dependent on image quality, particularly positional differences between the patient and treatment couch. We developed a new algorithm to improve the quality of images obtained in X-ray fluoroscopic imaging and report the preliminary results.
methodsTwo oblique X-ray fluoroscopic images were acquired using a dynamic flat panel detector (DFPD) for two patients with lung cancer. The weighting factor was applied to the DFPD image in respective columns, because most anatomical structures, as well as the treatment couch and port cover edge, were aligned in the superior-inferior direction when the patient lay on the treatment couch. The weighting factors for the respective columns were varied until the standard deviation of the pixel values within the image region was minimized. Once the weighting factors were calculated, the quality of the DFPD image was improved by applying the factors to multiframe images.
resultsApplying the image-processing algorithm produced substantial improvement in the quality of images, and the image contrast was increased. The treatment couch and irradiation port edge, which were not related to a patient's position, were removed. The average image-processing time was 1.1 ms, showing that this fast image processing can be applied to real-time tumour-tracking systems.
conclusionThese findings indicate that this image-processing algorithm improves the image quality in patients with lung cancer and successfully removes objects not related to the patient. ADVANCES IN KNOWLEDGE: Our image-processing algorithm might be useful in improving gated-treatment accuracy.
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