کاهش اثر تخریبی سیگنا‌ل‌های اشیا فلزات در تصاویر ناحیه دهان به‌وسیله شبکه عصبی بهینه به‌منظور بهبود کیفیت درمان به‌هنگام پرتودهی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی هسته‌ای، مهندسی پرتوپزشکی، دانشگاه آزاد اسلامی واحد علوم و تحقیقات، تهران

2 گروه فیزیک پزشکی، دانشکده پزشکی، دانشگاه علوم پزشکی ایران، تهران

چکیده

این پژوهش می‌تواند در پرتودرمانی بیماران مبتلا به انواع مختلف سرطان با پروتزهای فلزی به‌منظور افزایش کیفیت تصاویر سی‌تی‌اسکن برای تشخیص بهتر ناحیه درمان و کاهش دز دریافتی، صورت گیرد. در این پژوهش به‌منظور کاهش اثر سیگنال اشیا فلزی ایجاد شده در تصاویر سی‌تی‌اسکن ناحیه دهان، پارامترهای کیفیت تصاویر سی‌تی‌اسکن سر و گردن 20 بیمار مبتلا به سرطان سر و گردن دارای سیگنال اشیا فلزی بررسی شده و میزان بهبود کیفیت تصاویر بیماران با تصاویر پس از اصلاح سیگنال اشیا فلزی مقایسه شده است، هم‌چنین میزان دز دریافتی بیماران نیز بررسی شده است. بدین منظور تصاویر اصلاح شده‌ای به‌وسیله دو مدل شبکه عصبی ساخته شده تا عملکرد شبکه‌های عصبی به‌وسیله پارامترهای کیفیت تصاویر ارزیابی گردد تا شبکه عصبی مطلوب پیدا شود. در شبکه عصبی مولد تخاصمی، در برخی نقاط مانند غدد بزاقی و اطراف دندان دارای فلز تا %61/94 بهبود کیفیت تصویر صورت گرفته که در مقایسه با شبکه عصبی لایه‌به‌لایه تا حدود %36/72 عملکرد بهتری داشته است.

کلیدواژه‌ها


عنوان مقاله [English]

Decrease in destructive effects of metal artifacts in mouth area images by optimized neural network to improve treatment quality through radiotherapy

نویسندگان [English]

  • Goli Khaleghi 1
  • Mohammad Hosntalab 1
  • Mahdi Sadeghi 2
  • Reza Reiazi 2
  • Seyed Rabie Mahdavi 2
چکیده [English]

In this study, in order to reduce the effects of metal artifacts caused by metal objects in CT scan images of the mouth area, we investigated the quality parameters of head and neck CT scan images of patients before and after the presence of artifacts and evaluated the changes in quality image parameters to improve the quality of radiotherapy after modifying the images. For this purpose, first, we provided CT scan images of 20 patients with head and neck cancers with and without metal objects in the mouth area and compared the absorbed dose in patients with and without metal objects. Then, in order to prevent the destructive effects of images with artifacts in diagnosis and treatment process in radiotherapy, we created modified images by two different neural network models and evaluated the performance of neural networks by image quality parameters to find the effective neural network. By generative adversarial neural network, in some places around salivary glands and teeth with metal, up to 94% improvement has been achieved in image quality metrics, which is up to 70% better than the convolutional neural network. This study can be done to improve the quality of treatment in radiotherapy on patients with different types of cancer, especially with metal prostheses, in order to improve the quality of CT scan images for better diagnosis and contouring of the therapeutic area and reduce the dose received by patients through radiotherapy.

کلیدواژه‌ها [English]

  • Metal artifacts
  • Neural networks
  • bucal area dose
  • Radiotherapy
  • CT Scan
  • Quality image metrics
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