Computed Tomography (CT) has become an essential diagnostic imaging technology in modern healthcare because of its rapid acquisition, excellent spatial resolution, and numerous clinical applications. However, patient exposure to ionizing radiation and the long-term biological dangers associated with it have become major issues due to the growing use of Multidetector Computed Tomography (MDCT). With the goal of achieving the "As Low As Reasonably Achievable" (ALARA) concept while preserving diagnostic image quality, radiation dose optimization has thus emerged as a top priority in radiology practice ^[1].

The recent developments in patient radiation dose reduction methods in MDCT are examined in this review paper, with an emphasis on technological breakthroughs, reconstruction algorithms, and optimization techniques. The study examines how dose management is affected by scanner design enhancements such automatic exposure control (AEC), tube current modulation, spectrum imaging, high-pitch acquisition, and detector efficiency increase. Modern image reconstruction techniques that allow for significant dose reduction without sacrificing image quality are highlighted, such as filtered back projection (FBP), iterative reconstruction (IR), model-based iterative reconstruction (MBIR), and artificial intelligence (AI)-based deep learning reconstruction algorithms ^[2-3].

Protocol optimization techniques designed for various clinical applications and patient demographics, such as paediatric, cardiac, trauma, and oncologic imaging, are also highlighted in the review. As useful strategies for reducing needless radiation exposure, other factors like patient placement, scan length restriction, shielding procedures, low-kVp imaging, and contrast optimization are covered. Additionally, the function of quality assurance programs, diagnostic reference levels (DRLs), and dose monitoring systems in ensuring patient safety is investigated.