title: Motion artifact reduction in breast dynamic infrared imaging.
abstract: Dynamic infrared imaging is a promising technique in breast oncology. In this paper, a quantum well infrared photodetector infrared camera is used to acquire a sequence of consecutive thermal images of the patient's breast for 10 s. Information on the local blood perfusion is obtained from the spectral analysis of the time series at each image pixel. Due to respiratory and motion artifacts, the direct comparison of the temperature values that a pixel assumes along the sequence becomes difficult. In fact, the small temperature changes due to blood perfusion, of the order of 10-50 mK, which constitute the signal of interest in the time domain, are superimposed onto large temperature fluctuations due to the subject's motion, which represent noise. To improve the time series S/N, and as a consequence, enhance the specificity and sensitivity of the dynamic infrared examination, it is important to realign the thermal images of the acquisition sequence, thus reducing motion artifacts. In a previous study, we demonstrated that a registration algorithm based on fiducial points is suitable to both clinical applications and research, when associated with a proper set of skin markers. In this paper, we quantitatively evaluate the performance of different marker sets by means of a model that allows for estimating the S/N increment due to registration, and we conclude that a 12-marker set is a good compromise between motion artifact reduction and the time required to prepare the patient.
authors.affiliation: Department of Electronics, Politecnico di Torino, Torino 10129, Italy. valentina.agostini@polito.it
authors.forename: Valentina
authors.initials: V
authors.lastname: Agostini
authors.forename: Marco
authors.initials: M
authors.lastname: Knaflitz
authors.forename: Filippo
authors.initials: F
authors.lastname: Molinari
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001940:Breast / Q000502:physiology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D012680:Sensitivity and Specificity; D013817:Thermography / Q000379:methods*