Improving blood cancer diagnosis through morphological insights using quantitative phase imaging

Abstract

Blood cancer remains a major global health challenge, emphasizing the critical need of early diagnosis, for effective treatment and improved patient outcomes. Recently, quantitative phase imaging (QPI) based study of cancerous cell morphology, viability and proliferation, attracts the attention of the pathologist and researchers. In this research article, we have introduced customized QPI based imaging tool for investigation of malignant blood cells for the early detection of cancer. The proposed tool enables the measurement of optical path length variations which gives the provision of labelfree, high-resolution imaging of blood cells, allowing for the precise quantification of cellular parameters such as volume, thickness, and dry mass. The proposed low-cost configuration referred as self-referencing QPI system, makes use of the laser beam for generation of the interferograms. Moreover, this technique has the advantage of numerical focusing, and it is not necessary to place the imaging device at the image plane of the magnifying lens. Therefore, efficient autofocusing feature is designed that ensures the efficacious detection, omitting human error and declining the time-consumption. Moreover, the precision of early cancer diagnosis is enhanced through the integration of convolutional neural network (CNN) and QPI technique, which reduces the likelihood of inaccurate imaging. The non-invasive nature of proposed imaging system minimizes patient discomfort and enables real-time monitoring of disease progression. The methodology demonstrates promising results in the early detection of blood cancer and impassive the need of stained sample preparation. This research contributes to the advancement of personalized medicine and underscores the importance of leveraging quantitative phase imaging for early intervention and improved management of blood cancer.