Surreal illustration of lung cancer detection through advanced CT technology

Breathe Easier: How Advanced CT Scans are Revolutionizing Lung Cancer Detection

Elliot Brynn in Health & Wellness August 2025 • 4 min read.

"Uncover the life-saving potential of cone-beam computed tomography (CBCT) and learn how it improves accuracy in detecting mobile lung tumors."

In the realm of medical breakthroughs, imaging technology plays a pivotal role, particularly in the early and accurate diagnosis of diseases like cancer. Among the arsenal of diagnostic tools, Computed Tomography (CT) scans stand out as essential for screening and staging various cancers. In radiotherapy, CT imaging guides the way, outlining tumors and critical structures to ensure precise treatment planning. However, patient motion can blur the clarity of CT images, posing challenges to accurate diagnoses and treatment.

Traditional methods to counter motion artifacts include rapid gantry rotations and motion correction techniques. Four-dimensional CT (4D CT) scans have also emerged, sorting projections into respiratory motion phases to reconstruct clearer images. Now, a promising innovation has entered the scene: Cone-Beam Computed Tomography (CBCT). CBCT employs high-resolution and sensitive flat-panel detectors, offering a robust approach to volumetric tomography, especially beneficial for visualizing tumors that move during respiration.

CBCT is rapidly becoming a vital tool for image-guided radiation therapy (IGRT) as an increasing number of radiation therapy machines are equipped with kV on-board imaging (OBI) systems. However, like other CT methods, CBCT is also susceptible to motion-induced image degradation, which can obscure tumors and critical structures. Researchers are actively working to refine this technology to ensure accuracy and reliability, and CBCT is an important advancement that could change the way lung cancer is detected and treated.

Understanding Cone-Beam CT (CBCT) and Mobile Lung Tumors

Surreal illustration of lung cancer detection through advanced CT technology

To address the challenge of motion artifacts in CBCT imaging, a recent study has focused on quantitatively assessing and modeling variations in CT number distributions for mobile targets. This research involved acquiring CBCT images of water-equivalent gel targets inserted into a mobile thorax phantom, which mimics respiratory motion. By controlling the phantom’s cyclic motion in one dimension (superior-inferior), researchers simulated the movement of lung tumors during breathing.

The study involved obtaining CT number distribution profiles for both static and mobile targets using CBCT images. A mathematical model was developed to predict how motion parameters affect these distributions in one-dimensional movement. The results indicated significant variations in CT number distributions depending on motion parameters.

Key findings from the study include:

Motion Amplitude: The extension of CT number distribution increased linearly with motion amplitude, with maximum target elongation reaching twice the motion amplitude.
CT Number Levels: Mobile targets exhibited smeared CT number levels over a broader distribution. For example, the CT number level for a 20 mm target dropped by nearly 30% with a 20 mm motion amplitude.
Motion Frequency: The frequency of motion significantly influenced spatial and level variations in CT number distributions. Higher motion frequencies led to decreased CT number profile levels for the medium target.

The developed mathematical model accurately reproduced measured CT number distributions and predicted their dependence on target size and motion parameters such as speed, amplitude, frequency, and phase. This model corrected the CT number distribution retrospective to CT image reconstruction, employing a first-order linear relationship between the number of projections collected in the imaging window of a mobile voxel to obtain the cumulative CT number. This approach offers a quantitative characterization of motion artifacts in CBCT, which is crucial for validating CT numbers and ensuring the accuracy of localization and volume measurement of tumors in diagnostic imaging and interventional applications like radiotherapy.

The Future of CBCT in Lung Cancer Treatment

This innovative model holds significant promise for enhancing the accuracy of tumor detection and treatment planning. By understanding and correcting for motion artifacts, medical professionals can improve the precision of radiotherapy and other interventional applications, ultimately leading to better outcomes for patients with mobile lung tumors. Further research and refinement of these techniques will pave the way for more effective and personalized cancer treatments.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1120/jacmp.v16i1.5067, Alternate LINK

Title: Modeling And Measurement Of The Variations Of Ct Number Distributions For Mobile Targets In Cone-Beam Computed Tomographic Imaging

Subject: Radiology, Nuclear Medicine and imaging

Journal: Journal of Applied Clinical Medical Physics

Publisher: Wiley

Authors: Imad Ali, Nesreen Alsbou, Salahuddin Ahmad

Published: 2015-01-01

Everything You Need To Know

How does Cone-Beam CT (CBCT) specifically address the issue of motion artifacts when imaging mobile lung tumors?

Cone-beam computed tomography (CBCT) addresses motion artifacts, a significant challenge when visualizing tumors that move during respiration. A recent study focused on quantitatively assessing and modeling variations in CT number distributions for mobile targets using a mobile thorax phantom. By simulating the movement of lung tumors during breathing, researchers developed a mathematical model to predict how motion parameters affect these distributions, offering a means to correct for motion artifacts and improve accuracy in tumor localization and volume measurement.

What were the key findings of the study regarding motion amplitude, CT number levels, and motion frequency when using CBCT to image mobile lung tumors?

The key findings from the study on mobile lung tumors include the extension of CT number distribution increasing linearly with motion amplitude, mobile targets exhibiting smeared CT number levels over a broader distribution, and the frequency of motion significantly influencing spatial and level variations in CT number distributions. For instance, a 20 mm target's CT number level dropped by nearly 30% with a 20 mm motion amplitude. Higher motion frequencies also led to decreased CT number profile levels for the medium target.

How does the developed mathematical model correct for motion artifacts in CBCT images, and why is this correction important?

The mathematical model developed in the study corrects the CT number distribution retrospective to CT image reconstruction, employing a first-order linear relationship between the number of projections collected in the imaging window of a mobile voxel to obtain the cumulative CT number. This approach provides a quantitative characterization of motion artifacts in CBCT, which is crucial for validating CT numbers and ensuring the accuracy of localization and volume measurement of tumors in diagnostic imaging and interventional applications like radiotherapy.

What are some limitations or missing pieces of information in this discussion about CBCT and lung cancer detection?

While the article focuses on the advantages of CBCT and motion correction models in lung cancer detection, it does not go into detail regarding specific lung cancer screening protocols, such as those recommended by professional medical organizations. Nor does it discuss the comparative radiation doses of CBCT versus other CT methods like 4D CT, a key consideration in medical imaging. Also absent is a discussion of the costs associated with implementing CBCT technology versus traditional methods, which could impact accessibility.

What are the broader implications of using this innovative model for enhancing the accuracy of tumor detection and treatment planning in lung cancer cases?

The innovative model, which accurately reproduces measured CT number distributions and predicts their dependence on target size and motion parameters, is significant because it enhances the accuracy of tumor detection and treatment planning. By understanding and correcting for motion artifacts, medical professionals can improve the precision of radiotherapy and other interventional applications, ultimately leading to better outcomes for patients with mobile lung tumors. This is especially important as it moves towards more effective and personalized cancer treatments.