X-Ray Dark-Field Imaging of Lung Cancer in Mice
Bölükbas, Deniz LU and Wagner, Darcy LU
(2019)
- Abstract
- Lung cancer accounts for 1.6 million deaths per year worldwide. The majority of patients are diagnosed at advanced stages of the disease and often present with metastasis. Thus, the 5-year survival rate of lung cancer remains around 15%. Early diagnosis of lung cancer allows for better control of the disease with 5-year survival rates up to around 70%. Chest radiography is the most common technique for visualizing lungs. However, small lesions in the lung are often missed by conventional X-ray radiography. New technological advances, such as grating-based imaging, allow for better contrast in soft tissue. Grating-based imaging depends on the interactions between the specimen and the X-rays while they pass through, resulting in interference... (More)
- Lung cancer accounts for 1.6 million deaths per year worldwide. The majority of patients are diagnosed at advanced stages of the disease and often present with metastasis. Thus, the 5-year survival rate of lung cancer remains around 15%. Early diagnosis of lung cancer allows for better control of the disease with 5-year survival rates up to around 70%. Chest radiography is the most common technique for visualizing lungs. However, small lesions in the lung are often missed by conventional X-ray radiography. New technological advances, such as grating-based imaging, allow for better contrast in soft tissue. Grating-based imaging depends on the interactions between the specimen and the X-rays while they pass through, resulting in interference and refraction of the beam. Contrast acquisition from these interactions are categorized as interferometric methods. X-ray dark-field imaging relies on quantification of small-angle scattering of the X-rays during this traverse and has shown success in obtaining enhanced contrast from soft tissues such as the lung. In in vivo models, dark-field imaging has been shown to be superior to conventional radiography for visualization of pulmonary diseases including lung cancer. In this chapter, we summarize applications of this technology for imaging of lung cancer in small animals and discuss its future perspectives and potential challenges in translation. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f397adc7-3c61-4a12-ac99-6944be54eabc
- author
- Bölükbas, Deniz
LU
and Wagner, Darcy
LU
- organization
- publishing date
- 2019-04-24
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Lung Imaging and CADx
- editor
- El-Baz, Ayman and Suri, Jasjit S.
- edition
- 1
- pages
- 22 pages
- publisher
- CRC Press
- ISBN
- 9780429055959
- DOI
- 10.1201/9780429055959
- language
- English
- LU publication?
- yes
- id
- f397adc7-3c61-4a12-ac99-6944be54eabc
- date added to LUP
- 2020-04-16 18:32:38
- date last changed
- 2020-04-17 09:21:15
@inbook{f397adc7-3c61-4a12-ac99-6944be54eabc,
abstract = {{Lung cancer accounts for 1.6 million deaths per year worldwide. The majority of patients are diagnosed at advanced stages of the disease and often present with metastasis. Thus, the 5-year survival rate of lung cancer remains around 15%. Early diagnosis of lung cancer allows for better control of the disease with 5-year survival rates up to around 70%. Chest radiography is the most common technique for visualizing lungs. However, small lesions in the lung are often missed by conventional X-ray radiography. New technological advances, such as grating-based imaging, allow for better contrast in soft tissue. Grating-based imaging depends on the interactions between the specimen and the X-rays while they pass through, resulting in interference and refraction of the beam. Contrast acquisition from these interactions are categorized as interferometric methods. X-ray dark-field imaging relies on quantification of small-angle scattering of the X-rays during this traverse and has shown success in obtaining enhanced contrast from soft tissues such as the lung. In in vivo models, dark-field imaging has been shown to be superior to conventional radiography for visualization of pulmonary diseases including lung cancer. In this chapter, we summarize applications of this technology for imaging of lung cancer in small animals and discuss its future perspectives and potential challenges in translation.}},
author = {{Bölükbas, Deniz and Wagner, Darcy}},
booktitle = {{Lung Imaging and CADx}},
editor = {{El-Baz, Ayman and Suri, Jasjit S.}},
isbn = {{9780429055959}},
language = {{eng}},
month = {{04}},
publisher = {{CRC Press}},
title = {{X-Ray Dark-Field Imaging of Lung Cancer in Mice}},
url = {{http://dx.doi.org/10.1201/9780429055959}},
doi = {{10.1201/9780429055959}},
year = {{2019}},
}