MGM Healthcare Malar, Adyar Advances Precision Imaging with Low-Dose CT and Deep-Learning Technology
Chennai, September 22, 2026: Marking a milestone in bringing advanced radiology closer to patients, MGM Healthcare Malar-Adyar, has introduced a set of innovations in CT imaging that enable early detection of cancer and heart disease. Combining deep-learning technology, advanced CT protocols and radiology expertise, these innovations enable CT examinations to be performed with substantially lower radiation doses while preserving or enhancing diagnostic information.
The hospital has combined ultra-low-dose CT chest screening, and low-dose CT coronary angiography with optimised clinical protocols to reduce radiation exposure while maintaining diagnostic quality. A key component of its CT imaging workflow is Delta, a deep-learning-based image reconstruction technology that enables diagnostically useful images to be reconstructed from scans acquired at substantially lower radiation doses and, in selected applications, with lower volumes of contrast. The innovations are primarily in clinical protocol design, workflow integration, image acquisition, reconstruction, quantitative analysis and the way advanced technologies are applied to patient care.
The hospital has performed 133 coronary CT examinations over a 10-day period using approximately 15 mL of contrast per examination, along with a low-radiation acquisition strategy. The approach is aimed at reducing both radiation exposure and contrast requirements while maintaining the image quality needed to assess the coronary arteries. The ultra-low-dose chest CT programme has been used for 260 patients. Of these, 179 subsequently underwent biopsy based on imaging findings, with 54 cancers identified in the screened cohort.
Speaking about the innovations, Dr. Samuel Reefath J, Senior Consultant & Lead Radiologist, said, “Every time we perform a CT examination, we have to ask two questions: Can we obtain the information the clinician needs? And can we do it with the least possible burden to the patient? That burden includes radiation, iodinated contrast, cost, time and sometimes even the anxiety associated with invasive investigations. We therefore wanted to move from a conventional approach of simply acquiring images towards precision imaging, obtaining the maximum clinically useful information with the minimum necessary exposure. This philosophy has driven our low-dose coronary CT, ultra-low-dose chest CT and our work in quantitative and AI-assisted imaging. We are not abandoning established CT principles. We work within accepted radiology and cardiovascular CT standards, while optimizing acquisition parameters for individual clinical situations.”
He added, “Modern CT and MRI scanners are capable of generating enormous volumes of highly detailed information. Deep Learning assists radiologists in analysing large volumes of imaging data and drawing attention to findings that may require closer evaluation. Deep Learning-based tools can support the identification of subtle abnormalities, assist in image analysis, and help organise and quantify information, allowing the radiologist to focus more closely on the clinical interpretation of the findings. The integration of these technologies is aimed at making radiology not only faster, but also more consistent, quantitative and clinically useful.”
Commenting on the innovations, Mr. Venugopal Bhat, Chief Operating Officer and Group Vice President – Strategic Initiatives, said, “Our vision is to move towards precision healthcare rather than simply more healthcare technology. Advanced imaging should ultimately answer three questions: Can we detect disease earlier? Can we characterise it more accurately? And can we do it more safely? We want MGM Healthcare Malar-Adyar to function not merely as a place where sophisticated scans are performed, but as a centre where advanced imaging is translated into earlier diagnosis and better clinical decision-making.”
He added that ultimately, the patient should gain earlier diagnosis, safer diagnosis and more precise diagnosis. “If we can detect cancer before symptoms develop, that potentially creates an opportunity for earlier treatment. If we can perform a coronary CT with substantially less contrast and radiation, we reduce the burden of the investigation. And if quantitative imaging and AI can help us extract information that is difficult to appreciate visually, we can potentially provide clinicians with more objective information. So, the final measure of innovation is not how sophisticated the scanner is. The final measure is whether the patient benefits,” he said.