Summary: New software called CT-Derived Functional Imaging (CTFI) offers a faster, more reliable alternative for diagnosing lung problems without contrast dye, benefiting patients with allergies, COPD, and cancer by providing accurate lung volume estimations and aiding in precise radiation therapy.

Key Takeaways:

  1. The CT-Derived Functional Imaging (CTFI) software offers a faster and more reliable alternative for diagnosing serious lung issues without the need for contrast dye, benefiting patients with allergies or restrictions.
  2. CTFI improves the accuracy of lung volume measurements and helps in targeting radiation therapy more precisely, reducing harm to healthy lung tissue and aiding in better treatment outcomes for patients with lung issues like COPD and cancer.
  3. The software can predict disease progression in COPD patients over a 10-year period, improving patient outcomes and treatment plans, with ongoing research funded by a $1.38 million NIH grant to enhance the technology with AI.

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For up to 30% of patients allergic to medical contrast dye or restricted from it due to other health issues, getting a diagnosis for serious lung problems like pulmonary embolism can take longer. This is because imaging methods without contrast dye are less accurate and take more time.

Improves Lung Volume Estimation and Radiation Targeting

Now, new imaging software developed by pulmonologist Girish Nair, MD, with Corewell Health William Beaumont University Hospital in Royal Oak, Michigan, and biomedical engineer Edward Castillo, PhD, with The University of Texas at Austin is offering a faster, more reliable alternative. The software, called CT-Derived Functional Imaging (CTFI), uses advanced math to quickly calculate changes in lung volume and blood mass as a patient breathes. This allows doctors to get consistent data and make better diagnoses without using contrast dye.

CTFI not only helps those who can’t use contrast dye but also benefits all patients with lung issues like COPD and cancer. It ensures accurate lung volume estimations and helps in targeting radiation therapy more precisely, reducing harm to healthy lung tissue.

This software can detect pulmonary embolism by measuring blood mass changes with a simple CT scan. This method avoids the need for observational treatment or nuclear scans, which are more invasive and time-consuming.

Improves Long-Term COPD Treatment Plans

Additionally, the software can predict disease progression in COPD patients over a 10-year period, improving patient outcomes and treatment plans. Castillo and Nair are leading a research team with a $1.38 million NIH grant to further enhance this technology with AI, aiming to improve COPD survival rates.

“Ultimately, the goal is to ensure doctors have the best tools to treat patients,” Castillo says. “AI can significantly advance our work to improve patient health and save lives.”