All CT scans were acquired using a Brilliance Big Bore multislice CT scanner (Philips Healthcare, Andover, MA). For 4D CT, the respiratory signal was acquired using a pneumatic belt and the following scanning parameters were used consistently: 120 kVp, 120 mA, and 2 mm slice. Although breathing guidance (e.g., audiovisual biofeedback) was not used in this study, we provided verbal instruction to patients to maintain regular breathing. The B (standard) algorithm was used for image reconstruction. The phase-based method was used for 4D CT sorting. Patients were positioned using the same patient-specific immobilization devices throughout all CT scans to reduce variability in positioning.
Brilliance big bore multislice ct scanner
Manufactured by Philips
The Brilliance Big Bore multislice CT scanner is a medical imaging device manufactured by Philips. It is designed to capture high-quality images of the human body using advanced computed tomography (CT) technology. The core function of this equipment is to provide detailed, three-dimensional images of the internal structures, allowing healthcare professionals to make informed diagnoses and treatment decisions.
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Lab products found in correlation
2 protocols using brilliance big bore multislice ct scanner
1
4D CT Imaging Protocol for Respiratory Monitoring
2
4D CT-Based Ventilation Imaging Workflow
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CT ventilation imaging is based on (1) deformable image registration (DIR) of 4D CT images, and (2) quantitative image analysis for regional volume change, a surrogate for ventilation. 4D CT scans were acquired using a Brilliance Big Bore multislice CT scanner (Philips Healthcare, Andover, MA). The respiratory signal was acquired using a pneumatic belt. The following standard 4D CT scan parameters were used: 120 kVp, 120 mA, and 2 mm slice thickness. DIR was performed for spatial mapping of the peak-inhalation 4D CT image (moving image) to the peak-exhalation image (fixed image). We employed a volumetric elastic DIR method that minimizes both a similarity function (sum of squared difference) and a regularization term (elastic regularization) [24] . The DIR method has been previously evaluated thoroughly [24] , [25] , [26] and demonstrated to achieve sub-voxel target registration errors on average [24] . Regional volume change was quantified using the Hounsfield unit (HU)-based metric. See Appendix B for further details on the HU-based metric.
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