About Adaptive Iterative Reconstruction
As a researcher in the field of head and neck radiology, Dr. Gul Moonis of Harvard Medical School has published in such peer-reviewed periodicals as the American Journal of Neuroradiology and the Journal of Magnetic Resonance Imaging. Gul Moonis, MD, also stands out as a senior author of a 2013 article on the use of adaptive statistical iterative reconstruction (ASIR) to reduce radiation dose in soft tissue neck scans.
Under the basic algorithms of computerized topography (CT) scans, radiologists frequently must choose between high reconstruction speeds and low image noise. In response to this challenge, a technique called statistical iterative reconstruction has arisen that uses multiple reconstruction phases to reduce noise. Because this technique is effective at reducing noise but detrimental to reconstruction time, radiologists have developed a way of beginning this reconstruction after the first round of filtered back projection (FBP) reconstruction.
Known as adaptive statistical iterative reconstruction (ASIR), this technique reduces noise in a way that allows radiologists to increase the operator-selected noise index (mA) during scanning. This in turn supports a lowering of the patient radiology dose without compromising diagnostic accuracy. Clinicians can also adapt the technique to account for differences in patient size and area of the body to be evaluated, as the methodology achieves noise reduction through effective modeling of the scanned tissues.