Analysing the usefulness of motion correction software in myocardial perfusion imaging

Introduction Myocardial Perfusion Imaging (MPI) is a very important tool in the assessment of Coronary Artery Disease ( CAD ) patient s and worldwide data demonstrate an increasingly wider use and clinical acceptance. Nevertheless, it is a complex process and it is quite vulnerable concerning the amount and type of possible artefacts, some of them affecting seriously the overall quality and the clinical utility of the obtained data. One of the most in convenient artefacts , but relatively frequent ( 20% of the cases ) , is relate d with patient motion during image acquisition . Mostly, in those situations, specific data is evaluated and a decisi on is made between A) accept the results as they are , consider ing that t he “noise” so introduced does not affect too seriously the final clinical information, or B) to repeat the acquisition process . Another possib ility could be to use the “ Motion Correcti on Software” provided within the software package included in any actual gamma camera. The aim of this study is to compare the quality of the final images , obtained after the application of motion correction software and after the repetition of image acqui sition. Material and Methods Thirty cases of MPI affected by Motion Artefacts and repeated , were used. A group of three, independent (blinded for the differences of origin) expert Nuclear Medicine Clinicians had been invited to evaluate the 30 sets of thre e images - one set for each patient - being ( A) original image , motion uncorrected , (B) original image, motion corrected, and (C) second acquisition image, without motion . The results so obtained were statistically analysed . Results and Conclusion Results obtained demonstrate that the use of the Motion Correction Software is useful essentiall y if the amplitude of movement is not too important (with this specific quantification found hard to define precisely , due to discrepancies between clinicians and other factors , namely between one to another brand); when that is not the case and the amplitude of movement is too important , the n the percentage of agreement between clinicians is much higher and the repetition of the examination is unanimously considered ind ispensable.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.