RESUMEN
Artifacts induced during patient monitoring are a main limitation for near-infrared spectroscopy (NIRS) as a non-invasive method of cerebral hemodynamic monitoring. There currently does not exist a robust "gold-standard" method for artifact management for these signals. The objective of this review is to comprehensively examine the literature on existing artifact management methods for cerebral NIRS signals recorded in animals and humans. A search of five databases was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. The search yielded 806 unique results. There were 19 articles from these results that were included in this review based on the inclusion/exclusion criteria. There were an additional 36 articles identified in the references of select articles that were also included. The methods outlined in these articles were grouped under two major categories: (1) motion and other disconnection artifact removal methods; (2) data quality improvement and physiological/other noise artifact filtering methods. These were sub-categorized by method type. It proved difficult to quantitatively compare the methods due to the heterogeneity of the effectiveness metrics and definitions of artifacts. The limitations evident in the existing literature justify the need for more comprehensive comparisons of artifact management. This review provides insights into the available methods for artifact management in cerebral NIRS and justification for a homogenous method to quantify the effectiveness of artifact management methods. This builds upon the work of two existing reviews that have been conducted on this topic; however, the scope is extended to all artifact types and all NIRS recording types. Future work by our lab in cerebral NIRS artifact management will lie in a layered artifact management method that will employ different techniques covered in this review (including dynamic thresholding, autoregressive-based methods, and wavelet-based methods) amongst others to remove varying artifact types.
RESUMEN
Regional cerebral oxygen saturation (rSO2), a method of cerebral tissue oxygenation measurement, is recorded using non-invasive near-infrared Spectroscopy (NIRS) devices. A major limitation is that recorded signals often contain artifacts. Manually removing these artifacts is both resource and time consuming. The objective was to evaluate the applicability of using wavelet analysis as an automated method for simple signal loss artifact clearance of rSO2 signals obtained from commercially available devices. A retrospective observational study using existing populations (healthy control (HC), elective spinal surgery patients (SP), and traumatic brain injury patients (TBI)) was conducted. Arterial blood pressure (ABP) and rSO2 data were collected in all patients. Wavelet analysis was determined to be successful in removing simple signal loss artifacts using wavelet coefficients and coherence to detect signal loss artifacts in rSO2 signals. The removal success rates in HC, SP, and TBI populations were 100%, 99.8%, and 99.7%, respectively (though it had limited precision in determining the exact point in time). Thus, wavelet analysis may prove to be useful in a layered approach NIRS signal artifact tool utilizing higher-frequency data; however, future work is needed.
RESUMEN
BACKGROUND AND PURPOSE: Artefacts caused by dental amalgam implants present a common challenge in computed tomography (CT) and therefore treatment planning dose calculations. The goal was to perform a quantitative image quality analysis of our Artifact Management for Proton Planning (AMPP) algorithm which used gantry tilts for managing metal artefacts on Head and Neck (HN) CT scans and major vendors' commercial approaches. MATERIALS AND METHODS: Metal artefact reduction (MAR) algorithms were evaluated using an anthropomorphic phantom with a removable jaw for the acquisition of images with and without (baseline) metal artifacts. AMPP made use of two angled CT scans to generate one artifact-reduced image set. The MAR algorithms from four vendors were applied to the images with artefacts and the analysis was performed with respective baselines. Planar HU difference maps and volumetric HU differences were analyzed. RESULTS: AMPP algorithm outperformed all vendors' commercial approaches in the elimination of artefacts in the oropharyngeal region, showing the lowest percent of pixels outside +- 20 HU criteria, 4%; whereas those in the MAR-corrected images ranged from 26% to 67%. In the region of interest within the affected slices, the commercial MAR algorithms showed inconsistent performance, whereas the AMPP algorithm performed consistently well throughout the phantom's posterior region. CONCLUSIONS: A novel MAR algorithm was evaluated and compared to four commercial algorithms using an anthropomorphic phantom. Unanimously, the analysis showed the AMPP algorithm outperformed vendors' commercial approaches, showing the potential to be broadly implemented, improve visualizations in patient anatomy and provide accurate HU information.
RESUMEN
Respiratory motion artifacts may affect interpretation of whole-body 18F-FDG PET/CT scans, especially when lesions are localized between the liver and the lung. We report a case of a patient with breast cancer who underwent PET/CT after therapy and in whom focal 18F-FDG uptake of equivocal interpretation was observed between the liver and the pleura. A subsequent imaging acquisition of the right lateral decubitus showed that the lesion had a pleural location, thus improving the diagnostic accuracy of the PET/CT finding.