R ARIVANANTHAM
CHENNAI, SEPT 22
Can advanced imaging help doctors detect disease earlier while reducing the burden of diagnosis on patients? MGM Healthcare Malar-Adyar is seeking to address this challenge through innovations in low-dose CT imaging, deep-learning technology and quantitative analysis, with a focus on the early detection of lung cancer and the assessment of coronary artery disease.
The hospital has introduced a combination of ultra-low-dose CT chest screening and low-dose CT coronary angiography, integrating advanced image reconstruction and optimised clinical protocols to reduce radiation exposure while preserving diagnostically useful information.
- Ultra-low-dose chest CT programme screens 260 patients, with 54 cancers identified in the screened cohort
- Imaging findings prompted 179 patients to undergo biopsy, highlighting the role of CT in further clinical evaluation
- Low-dose coronary CT angiography combines optimised acquisition with reduced radiation and contrast exposure
- 133 coronary CT examinations performed over 10 days using approximately 15 mL of contrast per examination
- Deep-learning reconstruction and quantitative imaging aim to improve consistency, objectivity and diagnostic usefulness
- Hospital says the ultimate benchmark of innovation is earlier, safer and more precise diagnosis that benefits patients
The initiative reflects a shift towards precision imaging, in which the emphasis is not merely on acquiring detailed scans, but on obtaining clinically meaningful information with the minimum necessary exposure to radiation and contrast agents.
From advanced scans to actionable insights
The hospital’s 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.
The figures highlight the role of imaging in identifying abnormalities that warrant further investigation. However, the findings from this screened group do not, by themselves, establish the effectiveness of population-wide screening or demonstrate that every cancer detected was at an early stage.
In cardiac imaging, the hospital has performed 133 coronary CT examinations over a 10-day period, using approximately 15 mL of contrast per examination alongside a low-radiation acquisition strategy.
The approach is intended to reduce radiation and contrast requirements while retaining the image quality needed to assess the coronary arteries.
Deep learning adds another dimension
A key component of the hospital’s CT imaging workflow is Delta, a deep-learning-based image reconstruction technology. It 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 extend beyond the imaging equipment itself, encompassing clinical protocol design, workflow integration, image acquisition, reconstruction and quantitative analysis.
By integrating artificial intelligence-assisted tools into radiology, the hospital aims to support more consistent and objective assessment of abnormalities, helping radiologists identify findings that may require closer evaluation.
‘Maximum clinically useful information, minimum exposure’
Explaining the philosophy behind the initiative, Dr Samuel Reefath J, Senior Consultant and Clinical Lead, Radiology Department, 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.”
On the growing role of artificial intelligence in medical imaging, Dr Reefath 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.”
‘The final measure is whether the patient benefits’
Venugopal Bhat, Chief Operating Officer and Group Vice President – Strategic Initiatives, said the hospital’s objective was to move towards precision healthcare rather than simply introducing more technology.
“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.”
Emphasising the importance of patient outcomes, Bhat added that the ultimate objective was to enable earlier, safer 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.
Precision imaging, with the patient at the centre
The hospital’s latest initiative brings together advanced CT protocols, deep-learning reconstruction and radiology expertise in an effort to make diagnostic imaging more patient-conscious.
While the reported screening and coronary imaging numbers provide an indication of the programme’s clinical activity, its broader significance will depend on how these approaches perform across patient groups, including their diagnostic accuracy, safety and impact on subsequent treatment decisions.
The underlying message is clear: the value of medical imaging lies not simply in what a scanner can capture, but in how reliably that information helps clinicians make decisions while minimising unnecessary burdens on patients.
