Previously we developed a single-shot quantitative x-ray imaging (SSQI) method to perform material decomposition in x-ray imaging by combining the use of a primary modulator (PM) and dual-layer (DL) detector, where the PM removes scatter from DL images while the DL provides dual energy (DE) images for material decomposition (MD), which further removes beam hardening resulting from the partially attenuated regions of the PM. We have demonstrated the concept of SSQI using simulation and further tested its efficacy on chest phantom studies performed on our tabletop system.
In this work, we further explored the clinical value of SSQI in interventional guidance, specifically focusing on investigating its potential in real-time x-ray image guidance. We integrated the SSQI on a C-arm system and designed two studies to evaluate its performance for iodine quantification in both static and dynamic imaging using anthropomorphic phantoms. Compared to direct MD without scatter correction, the RMSE in material-specific images was reduced by 38-64% with SSQI when compared to ground truth. For the dynamic study, the SSQI estimated iodine mass was in close agreement with the amount from a ground truth acquisition. The results in this work further expand the potential of SSQI for real-time image guidance.
We explore noninvasive biomarkers of microvascular invasion (mVI) in patients with hepatocellular carcinoma (HCC) using quantitative and semantic image features extracted from contrast-enhanced, triphasic computed tomography (CT). Under institutional review board approval, we selected 28 treatment-naive HCC patients who underwent surgical resection. Four radiologists independently selected and delineated tumor margins on three axial CT images and extracted computational features capturing tumor shape, image intensities, and texture. We also computed two types of “delta features,” defined as the absolute difference and the ratio computed from all pairs of imaging phases for each feature. 717 arterial, portal-venous, delayed single-phase, and delta-phase features were robust against interreader variability (concordance correlation≥0.8). An enhanced cross-validation analysis showed that combining robust single-phase and delta features in the arterial and venous phases identified mVI (AUC 0.76±0.18). Compared to a previously reported semantic feature signature (AUC 0.47 to 0.58), these features in our cohort showed only slight to moderate agreement (Cohen’s kappa range: 0.03 to 0.59). Though preliminary, quantitative analysis of image features in arterial and venous phases may be potential surrogate biomarkers for mVI in HCC. Further study in a larger cohort is warranted.
The purpose of this study is to investigate the utility of obtaining “core samples” of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers’ segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.
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