Frontiers in Medicine · 2019 · 12 citations · 18 references
MedicineBioengineeringRadiologyDiagnostic ImagingDiagnostic RadiologyThoracic ImagingComputed TomographyLung VolumesMagnetic Resonance ImagingMedical ImagingPulmonary Nodule EvaluationVolume SegmentationComputed Tomography VolumetryLung Volume AssessmentLung Volume QuantificationPilot Study DesignPulmonary Nodule DiagnosisSignal-to-noise Ratio ThresholdingSlice-based SegmentationThresholding
Introduction Computed tomography (CT) is currently the reference modality for the detection and follow-up of pulmonary nodules. While 2D measurements are commonly used in clinical practice to assess growth, increasingly 3D volume measurements are being recommended. The goal of this pilot study was to evaluate preliminarily the capabilities of 3D MRI using ultra-short echo time for lung nodule volumetry, as it would provide a radiation-free modality for this task. Material and methods Artificial nodules were manufactured out of Agar and measured using an ultra-short echo time MRI sequence. CT data were also acquired as a reference. Image segmentation was carried out using an algorithm based on signal intensity thresholding (SIT). For comparison purposes, we also performed manual slice by slice segmentation. Volumes obtained with MRI and CT were compared. Finally, the volumetry of a lung nodule was evaluated in one human subject in comparison with CT. Results Using the SIT technique, minimal bias was observed between CT and MRI across the entire range of volumes (2%) with limits of agreement below 14%. Comparison of manually segmented MRI and CT resulted in a larger bias (8%) and wider limits of agreement ([-23% 40%]). In vivo, nodule volume differed of less than 16% between modalities with the SIT technique. Conclusion This pilot study showed very good concordance between CT and UTE-MRI to quantify lung nodule volumes, in both a phantom and human setting. Our results enhance the potential of MRI to quantify pulmonary nodule volume with similar performance to CT.
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3D Slicer as an image computing platform for the Quantitative Imaging Network
Andriy Fedorov, Reinhard Beichel, Jayashree Kalpathy–Cramer et al. · Magnetic Resonance Imaging · 2012 · 8.9K citations · Full text
Arts, Computer Science, Radiology +12
Management of Lung Nodules Detected by Volume CT Scanning
Rob J. van Klaveren, Matthijs Oudkerk, Mathias Prokop et al. · New England Journal of Medicine · 2009 · 822 citations · Full text