Mạng nơ-ron tích chập cho việc kiểm soát chất lượng hình ảnh tự động và tính tuân thủ EARL của hình ảnh PET

Elisabeth Pfaehler1, Daniela Euba1, Andreas Rinscheid2, Otto S. Hoekstra3, Josée M. Zijlstra3, Joyce van Sluis4, Adrienne H. Brouwers4, Constantin Lapa1, Ronald Boellaard4
1Nuclear Medicine, Medical Faculty, University of Augsburg, Augsburg, Germany
2Medical Physics and Radiation Protection, University Hospital Augsburg, Augsburg, Germany
3Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, the Netherlands
4Department of Nuclear Medicine and Molecular Imaging, Medical Imaging Center, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands

Tóm tắt

Tóm tắt Nền tảng

Các nghiên cứu học máy đòi hỏi một số lượng lớn hình ảnh thường được thu thập trên các máy quét PET khác nhau. Khi kết hợp các hình ảnh này, việc sử dụng hình ảnh hài hòa theo tiêu chuẩn EARL là điều cần thiết. Tuy nhiên, khi bao gồm các hình ảnh hồi cứu, việc cấp chứng chỉ EARL có thể chưa được thực hiện. Mục tiêu của nghiên cứu này là phát triển một mạng nơ-ron tích chập (CNN) có khả năng xác định liệu một hình ảnh có tuân thủ EARL hay không và liệu nó có đáp ứng các tiêu chuẩn EARL cũ hơn hay mới hơn.

Từ khóa

#Machine Learning #Hình ảnh PET #EARL #Mạng nơ-ron tích chập #Kiểm soát chất lượng hình ảnh

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