In the field of hardware, whole-body dynamic imaging with continuous bed motion (CBM) PET CT is a recently developed technique that has the ability to produce whole-body dynamic data, enabling simultaneous measurement of standardized uptake value with quantitative information such as metabolic rates of glucose or uptake rates throughout the body. The work on acquiring whole-body dynamic scan data has been in progress for the past many years, but the earlier employed step-and-shoot technique resulted in artifacts related to overlapping bed positions and a longer acquisition time. The newer technique of CBM scanning allows simpler workflow, has the potential to eliminate overlapping bed related artifacts, and an overall shorter scanning time of approximately 15 minutes. Many commercial CBM PET CT imaging platforms are being released in recent years with more robust whole-body dynamic imaging protocols, and we hope to make use of it in future for better assessment of the human body.
Quality assurance in diagnostic imaging
In the face of burgeoning infrastructure costs and cut-throat competition, the onus of maintaining a profitable institution lies not only on the management but also on the reporting doctors and the support staff. The market is rife with cheaper alternatives, which thrive on bulk business; thus to ensure the viability of an enterprise, overall patient experience, satisfaction of the referring clinician, and faster turnaround time, all have to be taken into account. Turnaround time (TAT) literally means the time elapsed between the entry of a patient to the department and the preparation of the final report. Somewhere in between, the standard of reporting has to be maintained, the clinical questions answered, and all available data analyzed to come to a final conclusion. It is the so-called nemesis of all private diagnostic centers, whether stand-alone or functioning within a hospital set up. Also, for a first timer, looking for a diagnostic center, cost is the only factor which is of utmost importance and thus a cheaper and faster alternative always looks rosier. To add to the confusion, there are unforeseen mishaps and unavoidable factors such as machinery breakdown, software failure, and dose delivery issues, to name a few, which act as outliers, skewing the statistical graph in the process. To ascertain quality assurance in the face of such daunting adversaries is sometimes a tedious and thankless exercise.
Should we, as reporting physicians, be more concerned about the turnaround time, or should we concentrate on trying to do our job as responsibly as possible. This moral and ethical dilemma needs to be addressed. Although one can generate more volume of work by taking short cuts or relying on standardized formats, however, in this era of information overdrive, each and every action has potential medico-legal implications. In order to avoid this, we have to safeguard ourselves by spending more time and effort on the actual reporting rather than trying to ensure a fast delivery record.
The future ahead – AI and deep machine learning
Unlike in the past, when more focus was laid on development of more advanced hardware, in the present times, the industry is focusing on improving software techniques for more efficient and improved quality images with faster acquisition and interpretation. The mega revolution in the form of artificial intelligence (AI) and deep machine learning is contributing exponentially to achieve this end. AI basically is a computer system that shows cognitive skills similar to humans in trying to understand, interpret, perform tasks, and troubleshoot to create solutions in times of crisis. AI can help in classifying and stratifying data and deep machine learning, which has the ability to learn from experience and can detect patterns and anomalies in various physiological and pathological states at speeds far greater than humanly possible. Thus, AI has the potential to take us to greater heights of knowledge and understanding by overriding human error and better time management; however, instead of relying completely on man-made machines, we should acquire skills greater than the machine in order to keep a supervisory eye.