Low-Dose Lung Radiographs Facilitate Corona Diagnosis
By MedImaging International staff writers Posted on 18 May 2020 |

Image: Schematic of a device for x-ray dark field imaging (Photo courtesy of TUM)
An innovative x-ray method that involves significantly lower radiation than computed tomography (CT) can help identify COVID-19 abnormalities.
Developed at Munich Technical University (TUM; Germany), dark-field imaging visualizes x-ray scattering. While conventional x-ray imaging shows the attenuation of x-rays on their way through tissue, the dark-field method focuses on the small share of the x-ray light which is scattered, similar to long-established dark-field microscopy technologies using visible light. To do so, x-ray dark-field imaging uses gratings as optical elements; the gratings are arrangements of fine lines which alternate between different degrees of x-ray transparency.
A total of three gratings used. Because of the short wavelength of the x-rays, the lines of the gratings are only a few micrometers (thousandths of a millimeter) wide. The patient is located between the second and third grating. The x-ray detector receives a conventional x-ray image which is overlaid with a pattern of fringes. The fringe pattern is the weakest in the areas where regions with intact alveoli are visualized. Specialized software then reconstructs two separate images, a conventional x-ray image and a dark-field image in which areas with intact alveoli appear bright, and areas with damaged alveoli appear dark.
“In pneumonia of the type caused by Covid-19, structures form in the lung which initially resemble cotton wadding or spider webs, and which then spread throughout the lung and fill with fluid. The changes in the lung are associated with damage to the alveoli which could be clearly visible in dark-field images,” said Professor Franz Pfeiffer, PhD. “The scattering is particularly strong at interfaces between air and tissue. As a result, a dark-field image of the lung can clearly distinguish areas with alveoli that are intact, i.e. filled with air, from regions in which the alveoli have collapsed or are filled with fluid.”
X-ray methods can be used to identify pathological changes in the lung that can typically accompany Covid-19, but the changes caused cannot be clearly identified in a conventional two-dimensional x-ray image, and require CT. Dark-field technology would involve exposure to a significantly lower radiation dose, as it requires only a single image per patient, while CT requires a large number of individual images taken from various angles. It would also make it possible to examine large numbers of patients within a very short time, providing results immediately after the examination.
Related Links:
Munich Technical University
Developed at Munich Technical University (TUM; Germany), dark-field imaging visualizes x-ray scattering. While conventional x-ray imaging shows the attenuation of x-rays on their way through tissue, the dark-field method focuses on the small share of the x-ray light which is scattered, similar to long-established dark-field microscopy technologies using visible light. To do so, x-ray dark-field imaging uses gratings as optical elements; the gratings are arrangements of fine lines which alternate between different degrees of x-ray transparency.
A total of three gratings used. Because of the short wavelength of the x-rays, the lines of the gratings are only a few micrometers (thousandths of a millimeter) wide. The patient is located between the second and third grating. The x-ray detector receives a conventional x-ray image which is overlaid with a pattern of fringes. The fringe pattern is the weakest in the areas where regions with intact alveoli are visualized. Specialized software then reconstructs two separate images, a conventional x-ray image and a dark-field image in which areas with intact alveoli appear bright, and areas with damaged alveoli appear dark.
“In pneumonia of the type caused by Covid-19, structures form in the lung which initially resemble cotton wadding or spider webs, and which then spread throughout the lung and fill with fluid. The changes in the lung are associated with damage to the alveoli which could be clearly visible in dark-field images,” said Professor Franz Pfeiffer, PhD. “The scattering is particularly strong at interfaces between air and tissue. As a result, a dark-field image of the lung can clearly distinguish areas with alveoli that are intact, i.e. filled with air, from regions in which the alveoli have collapsed or are filled with fluid.”
X-ray methods can be used to identify pathological changes in the lung that can typically accompany Covid-19, but the changes caused cannot be clearly identified in a conventional two-dimensional x-ray image, and require CT. Dark-field technology would involve exposure to a significantly lower radiation dose, as it requires only a single image per patient, while CT requires a large number of individual images taken from various angles. It would also make it possible to examine large numbers of patients within a very short time, providing results immediately after the examination.
Related Links:
Munich Technical University
Latest Radiography News
- AI-Powered Imaging Technique Shows Promise in Evaluating Patients for PCI
- Higher Chest X-Ray Usage Catches Lung Cancer Earlier and Improves Survival
- AI-Powered Mammograms Predict Cardiovascular Risk
- Generative AI Model Significantly Reduces Chest X-Ray Reading Time
- AI-Powered Mammography Screening Boosts Cancer Detection in Single-Reader Settings
- Photon Counting Detectors Promise Fast Color X-Ray Images
- AI Can Flag Mammograms for Supplemental MRI
- 3D CT Imaging from Single X-Ray Projection Reduces Radiation Exposure
- AI Method Accurately Predicts Breast Cancer Risk by Analyzing Multiple Mammograms
- Printable Organic X-Ray Sensors Could Transform Treatment for Cancer Patients
- Highly Sensitive, Foldable Detector to Make X-Rays Safer
- Novel Breast Cancer Screening Technology Could Offer Superior Alternative to Mammogram
- Artificial Intelligence Accurately Predicts Breast Cancer Years Before Diagnosis
- AI-Powered Chest X-Ray Detects Pulmonary Nodules Three Years Before Lung Cancer Symptoms
- AI Model Identifies Vertebral Compression Fractures in Chest Radiographs
- Advanced 3D Mammography Detects More Breast Cancers
Channels
MRI
view channel
Ultra-Powerful MRI Scans Enable Life-Changing Surgery in Treatment-Resistant Epileptic Patients
Approximately 360,000 individuals in the UK suffer from focal epilepsy, a condition in which seizures spread from one part of the brain. Around a third of these patients experience persistent seizures... Read more
AI-Powered MRI Technology Improves Parkinson’s Diagnoses
Current research shows that the accuracy of diagnosing Parkinson’s disease typically ranges from 55% to 78% within the first five years of assessment. This is partly due to the similarities shared by Parkinson’s... Read more
Biparametric MRI Combined with AI Enhances Detection of Clinically Significant Prostate Cancer
Artificial intelligence (AI) technologies are transforming the way medical images are analyzed, offering unprecedented capabilities in quantitatively extracting features that go beyond traditional visual... Read more
First-Of-Its-Kind AI-Driven Brain Imaging Platform to Better Guide Stroke Treatment Options
Each year, approximately 800,000 people in the U.S. experience strokes, with marginalized and minoritized groups being disproportionately affected. Strokes vary in terms of size and location within the... Read moreUltrasound
view channel
Smart Ultrasound-Activated Immune Cells Destroy Cancer Cells for Extended Periods
Chimeric antigen receptor (CAR) T-cell therapy has emerged as a highly promising cancer treatment, especially for bloodborne cancers like leukemia. This highly personalized therapy involves extracting... Read more
Tiny Magnetic Robot Takes 3D Scans from Deep Within Body
Colorectal cancer ranks as one of the leading causes of cancer-related mortality worldwide. However, when detected early, it is highly treatable. Now, a new minimally invasive technique could significantly... Read more
High Resolution Ultrasound Speeds Up Prostate Cancer Diagnosis
Each year, approximately one million prostate cancer biopsies are conducted across Europe, with similar numbers in the USA and around 100,000 in Canada. Most of these biopsies are performed using MRI images... Read more
World's First Wireless, Handheld, Whole-Body Ultrasound with Single PZT Transducer Makes Imaging More Accessible
Ultrasound devices play a vital role in the medical field, routinely used to examine the body's internal tissues and structures. While advancements have steadily improved ultrasound image quality and processing... Read moreNuclear Medicine
view channel
Novel PET Imaging Approach Offers Never-Before-Seen View of Neuroinflammation
COX-2, an enzyme that plays a key role in brain inflammation, can be significantly upregulated by inflammatory stimuli and neuroexcitation. Researchers suggest that COX-2 density in the brain could serve... Read more
Novel Radiotracer Identifies Biomarker for Triple-Negative Breast Cancer
Triple-negative breast cancer (TNBC), which represents 15-20% of all breast cancer cases, is one of the most aggressive subtypes, with a five-year survival rate of about 40%. Due to its significant heterogeneity... Read moreGeneral/Advanced Imaging
view channel
AI-Powered Imaging System Improves Lung Cancer Diagnosis
Given the need to detect lung cancer at earlier stages, there is an increasing need for a definitive diagnostic pathway for patients with suspicious pulmonary nodules. However, obtaining tissue samples... Read more
AI Model Significantly Enhances Low-Dose CT Capabilities
Lung cancer remains one of the most challenging diseases, making early diagnosis vital for effective treatment. Fortunately, advancements in artificial intelligence (AI) are revolutionizing lung cancer... Read moreImaging IT
view channel
New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible
Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Global AI in Medical Diagnostics Market to Be Driven by Demand for Image Recognition in Radiology
The global artificial intelligence (AI) in medical diagnostics market is expanding with early disease detection being one of its key applications and image recognition becoming a compelling consumer proposition... Read moreIndustry News
view channel
GE HealthCare and NVIDIA Collaboration to Reimagine Diagnostic Imaging
GE HealthCare (Chicago, IL, USA) has entered into a collaboration with NVIDIA (Santa Clara, CA, USA), expanding the existing relationship between the two companies to focus on pioneering innovation in... Read more
Patient-Specific 3D-Printed Phantoms Transform CT Imaging
New research has highlighted how anatomically precise, patient-specific 3D-printed phantoms are proving to be scalable, cost-effective, and efficient tools in the development of new CT scan algorithms... Read more
Siemens and Sectra Collaborate on Enhancing Radiology Workflows
Siemens Healthineers (Forchheim, Germany) and Sectra (Linköping, Sweden) have entered into a collaboration aimed at enhancing radiologists' diagnostic capabilities and, in turn, improving patient care... Read more