Friday, August 12, 2022

Two Monumental Milestones Achieved in CT Imaging

Conventional chest CT image (left side) of the human airways compared to the new and improved PCD-CT system (right side). The image produced with the PCD-CT system shows better delineation of the bronchial walls. Preliminary studies showed that the PCD-CT system allowed radiologists to see smaller airways than with standard CT systems.
Image credit: Cynthia McCollough, Mayo Clinic, Rochester, Minnesota.

Two biomedical imaging technologies developed with support from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) have been cleared for clinical use by the Food and Drug Administration (FDA). Both technologies offer advances in computed tomography (CT).

In one of these developments, project lead Cynthia McCollough, Ph.D., director of Mayo Clinic’s CT Clinical Innovation Center and her team helped develop the first photon-counting detector (PCD)-CT system, which is superior to current CT technology. CT imaging has been an immense clinical asset for diagnosing many diseases and injuries. However, since its introduction into the clinic in 1971, the way that the CT detector converts x-rays to electrical signals has remained essentially the same. Photon-counting detectors operate using a fundamentally different mechanism than any prior CT detector ever has.

“This is the first major imaging advancement cleared by the FDA for CT in a decade,” stated Behrouz Shabestari, Ph.D., director of the division of Health Informatics Technologies. “The impact of this development will be far-reaching and provide clinicians with more detailed information for medical diagnoses.”

A CT scan is obtained when an x-ray beam rotates around a patient, allowing x-rays to pass through the patient. As the x-rays leave the patient a picture is taken by a detector and the information is transmitted to a computer for further processing. “Standard CT detectors use a two-step process, where x-rays are turned into light and then light is converted to an electrical signal,” explained Cynthia McCollough. “The photon-counting detector uses a one-step process where the x-ray is immediately transformed into an electrical signal.”

Conventional CT (left side) image of an inner ear prosthesis compared to PCD-CT system (right side). The structures of the inner ear are composed of tiny bones that are difficult to visualize. Clinicians can get much clearer inner ear images with PCD-CT, which helps with cochlear implants for children. Read more about how NIBIB-funded researchers are using imaging to diagnose inner ear infections. 
Image credit: Cynthia McCollough, Mayo Clinic, Rochester, Minnesota.

In the two-step CT process, the energy of the x-ray is not recorded but with the one-step process, individual x-rays are recorded along with their energy level. As the x-rays rapidly enter the detector they are counted, hence the name photon-counting, and sorted according to their energy. Based on physics principles, this helps identify different materials (e.g., iodinated blood, soft tissue, bone) more easily.

This specific type of imaging is called multi-energy CT imaging. “Dual-energy CT requires specialized equipment and is limited to two energies, but with this new detector we have more “buckets” to sort x-ray energies into and that gives us the opportunity to better depict the differences in materials.” McCollough described.

In clinical studies reported in Radiology, up to 47 percent noise reduction was achieved with the new PCD-CT systems1. “Noise” refers to the random nature of x-ray signals, which makes the images more difficult to interpret because of the associated speckle pattern that overlies the true structures in the image.

The new system also lowers the amount of contrast agent needed for CT imaging. CT imaging is effective at visualizing blood vessels and tumors, but contrast agents must be used to accomplish this. Contrast agents are safe for most patients but in certain cases it would be best to give a lower dose, if possible. Since there is more signal from the contrast agent with the PCD-CT system, study participants required thirty percent less contrast agent to achieve the same image quality as with conventional CT systems2.

The PCD-CT systems also have better spatial resolution when compared to the conventional systems. In studies, the system achieved the best reported resolution for a clinical CT system2. McCollough compared it to a digital camera – the higher the number of megapixels the finer details one can see. “The significantly improved resolution and increased signal are the groundbreaking advantages this CT system has over standard CT,” said Shabestari.

McCollough and her team at Mayo Clinic in Rochester, Minnesota have been working on this project with Siemens Healthineers in Forchheim, Germany, and Siemens Medical Solutions in Malvern, Pennsylvania for the last 10 years. Siemens had been working on a prototype PCD-CT system and with McCollough’s funding from NIBIB the team was able to start scanning patients under Institutional Review Board approval. Over 1100 patients have been scanned in these studies, first on a conventional CT system and then with the new PCD-CT scanner, to show the advantages of the new system. The device is the first of its kind on the market.

McCollough’s also trying to find other ways to improve CT for clinicians using artificial intelligence (AI). Her group is applying deep learning (DL) methods to reduce noise (and radiation dose) without altering the anatomy of the image. She noted, “You can see more with AI processing – it makes a big difference.”

Using AI to develop software for improved CT brain scans

Original (left side) compared to reconstructed (right side) brain image produced from CT scans with low radiation dose. Reconstruction method improves image quality for accurate diagnosis.
Image credit: Danny JJ Wang, Hura Imaging, INC, Los Angeles.

NIBIB-funded researcher Danny JJ Wang, PhD, director of imaging technology innovation at the University of Southern California Mark and Mary Stevens Neuroimaging and Informatics Institute and his team at the start-up company Hura Imaging, Inc. have also been using AI to lower the dose of radiation given to a patient when they are undergoing a conventional CT scan.

While CT imaging is very safe, patients are exposed to small doses of radiation. Patients that need to receive repeat scans to monitor their health status or a specialized CT exam, CT perfusion (CTP), are exposed to higher doses of radiation. Radiologists must balance using lower dose CT scans, which produce lower quality images, with using a higher dose to obtain better quality images for making diagnosis and treatment decisions.

CTP is used to evaluate damage to the brain in a suspected stroke patient. To reduce the high radiation dose in CTP, Wang and his colleagues turned to AI and developed software to assist in image reconstruction. The software uses an algorithm called K-space Weighted Image Average that reduces the noise of CTP images that subsequently lowers the radiation dose administered to the patient without compromising image quality or processing speed.

Studies showed that the software reduces CTP radiation dose by 50-75% when compared to standard CTP methods4. Other advantages to using the method include zero disruptions to the standard clinical workflow and upgrades or alterations to current CT hardware are not needed. The software has received FDA 510(k) clearance and can be adopted into clinical practice. “Currently this is only applicable to time series CT data like CT perfusion and dynamic CT angiography, but we are developing deep learning-based algorithms so in the future the software can be applied to all forms of CT images,” Wang stated.

Wang received his second Small Business Innovation Research award from NIBIB to develop the software from use in the lab to commercialization for a broader impact. “This is a prime example of a NIBIB-supported technology that translated into an FDA cleared product and can be used in clinical practice,” stated Qi Duan Ph.D., director of the program in Image Processing, Visual Perception and Display.

Citations:

1Rajendran K, Petersilka M, Henning A, Shanblatt E, Marsh J Jr, Thorne J, Schmidt B, Flohr T, Fletcher J, McCollough C, Leng S. Full field-of-view, high-resolution, photon-counting detector CT: technical assessment and initial patient experience. Phys Med Biol. 2021 Oct 27;66(20):10.1088/1361-6560/ac155e. doi: 10.1088/1361-6560/ac155e. PMID: 34271558; PMCID: PMC8551012.

2Kishore Rajendran, Martin Petersilka, André Henning, Elisabeth R. Shanblatt, Bernhard Schmidt, Thomas G. Flohr, Andrea Ferrero, Francis Baffour, Felix E. Diehn, Lifeng Yu, Prabhakar Rajiah, Joel G. Fletcher, Shuai Leng, and Cynthia H. McCollough First Clinical Photon-counting Detector CT System: Technical Evaluation Radiology 2022 303:1, 130-138.

3Benson JC, Rajendran K, Lane JI, Diehn FE, Weber NM, Thorne JE, Larson NB, Fletcher JG, McCollough CH, Leng S. A New Frontier in Temporal Bone Imaging: Photon-Counting Detector CT Demonstrates Superior Visualization of Critical Anatomic Structures at Reduced Radiation Dose. AJNR Am J Neuroradiol. 2022 Apr;43(4):579-584. doi: 10.3174/ajnr.A7452. Epub 2022 Mar 24. PMID: 35332019; PMCID: PMC8993187.

4Zhao C, Martin T, Shao X, Alger JR, Duddalwar V, Wang DJJ. Low Dose CT Perfusion With K-Space Weighted Image Average (KWIA). IEEE Trans Med Imaging. 2020 Dec;39(12):3879-3890. doi: 10.1109/TMI.2020.3006461. Epub 2020 Nov 30. PMID: 32746131; PMCID: PMC7704693.

Source/Credit: National Institute of Biomedical Imaging and Bioengineering

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