Developing novel contrast agents for multi-energy photon-counting (PC)CT will require a clear translation pathway from preclinical validation to clinical applications. To begin this development, we have used a clinical PCCT scanner (Siemens NAEOTOM Alpha) to study the spectral separation of a few contrast elements (Iodine, I; Gadolinium, Gd; Hafnium, Hf; Tantalum, Ta; Bismuth, Bi; Calcium, Ca) with currently available scanning protocols (fixed: 120 kVp, 20 and 65 keV thresholds). We also explored the capabilities of clinical and preclinical PCCT to image mice with sarcoma tumors injected with nanoparticles (NP). Our results indicate that Ta or Hf are complementary to I or Gd, providing excellent spectral separation for future multi-agent studies. Based on preclinical PCCT with four energy thresholds, we also conclude that additional energy thresholds will benefit clinical PCCT. Furthermore, we demonstrate the role that multi-channel denoising and reconstruction algorithms will play in pushing the bounds of spatial and spectral resolution with clinical PCCT. Performing co-clinical research will facilitate the translation of novel imaging algorithms and NP contrast agents for PCCT
Purpose: Medical management after endovascular treatment (EVT) for acute ischemic stroke involves blood pressure management and antithrombotic medications which are strongly influenced by hemorrhagic complications of the procedure. In this study our aim is to modify a commercially available c-arm angiographic system to assess feasibility of distinguishing between blood and iodinated contrast using dual-energy CT technology. Methods: An angiographic C-arm system was manipulated with the addition of a flat sheet of tin (Sn) filtration with corresponding calibration. Two flat-panel cone-beam CT (FP-CBCT) were acquired at 70 kV and 125Sn kV. A multienergy CT phantom with iodine inserts at various concentrations ranging from 0.2 to 15 mg/cc was used to characterize the behavior (i.e. dual-energy ratios DER) of the materials as a function of the kVp pair. DER values were needed to adjust an existing material decomposition software. The accuracy of iodine quantification was assessed with inserts including solid water, iodine, blood and mixed iodine and blood; all with concentrations ranges relevant for stroke imaging. Results: Imaging indicated the ability to differentiate between blood and iodine for the use with FP-CBCT. Pure blood and iodine inserts fell within the 95% confidence interval for precision of contrast concentration measured. For iodine inserts, the error from expected measurement was 7.5% or under for inserts with concentrations above 2 mg/cc. Inserts containing a combination of blood and iodine were consistently reading 1 mg/ml or less deviations relative to the value of the material specified by the manufacturer, which represented a 25-35% error difference from the expected value. Conclusion: These results establish the reproducibility of the phantom values for dual energy calculations and suggest that this technology may be clinically useful after EVT for stroke. Improvements in the accuracy of the iodine/blood combination under dual energy evaluation is needed for other applications outside of the use for differentiating between hemorrhage and contrast staining.
The purpose of this study was to develop an automated patient-specific and organ-based image quality (IQ) assessment tool for dual energy (DE) computed tomography (CT) images for large scale clinical analysis. To demonstrate its utility, this tool was used to compare the image quality of virtual monoenergetic images (VMI) with mixed images. The tool combines an automated organ segmentation model developed to segment key organs of interest and a patient-based IQ assessment model. The organ segmentation model was reported in our previous study and used to segment liver in this study; specifically, the model used 3D Unet architecture, developed by training on 200 manually labeled CT cases. We used task-based image quality assessment to define a spectral detectability index (ds'), which enables the task definition to be lesion with specific contrast properties depending on DE reconstruction chosen. For actual testing of the tool, this study included 322 abdominopelvic DECT examinations acquired with dual-source CT. Within regions of segmented organ volumes, the IQ assessment tool automatically measures noise and calculates the spectral dependent detectability index (ds') for a detection task (i.e., liver lesion). This organ-based IQ tool was used to compare the image quality of DE images including VMIs at 50 keV, 70 keV and mixed images. Compared to mixed images, the results showed that VMI at 70 keV had better or equivalent spectral detectability index (difference 12.62±2.95%), while 50 keV images showed improved detectability index (61.62±10.23%). The ability to automatically assess image quality on a patient-specific and organ-based level may facilitate large scale clinical analysis, standardization, and optimization.
Although photon counting systems have shown strong clinical potential, this technology has not yet been fully evaluated or optimized for specific clinical applications. The purpose of this study was to develop a framework for realistic virtual clinical trials (VCTs) in photon counting CT (PCCT) imaging. We developed a photon counting CT simulator based on the geometry and physics of an existing research prototype scanner. The developed simulator models primary, scatter, and noise signals, detector responses, vendor-specific bowtie filters and X-ray spectra, axial/helical trajectories, vendor-specific acquisition modes, and multiple energy thresholds per detector pixel. The simulation procedure is accelerated by parallel processing using multiple GPUs. The generated projection images can be reconstructed using generic reconstruction algorithms as well as a commercial reconstruction software (ReconCT Siemens). A computational model of a physical Mercury phantom was imaged at multiple energy thresholds (25 and 75 keV) and dose levels (36, 72, 144, and 216 mAs). Noise magnitude was measured in the simulated images and compared against noise measurements in a real scan acquired with a research prototype photon counting scanner (Siemens Healthcare). The results showed that our simulator was capable of synthesizing realistic photon counting CT data. The simulator can be combined with realistic 4D high-resolution XCAT phantoms with intra-organ heterogeneities to conduct VCTs for specific clinical applications. This framework can greatly facilitate the evaluation, optimization, and eventual clinical use of PCCT.
The purpose of this study was to develop a dynamic physical cardiac phantom with a realistic coronary plaque to investigate stenosis measurement accuracy under clinically relevant heart-rates. The coronary plaque model (5 mm diameter, 50% stenosis, and 32 mm long) was designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine-enhanced lumen). Realistic cardiac motion was modeled by converting computational cardiac motion vectors into compression and rotation profiles executed by a commercial base cardiac phantom. The phantom was imaged on a dual-source CT system applying a retrospective gated coronary CT angiography (CCTA) protocol using synthesized motion-synchronized electrocardiogram (ECG) waveforms. Multiplanar reformatted images were reconstructed along vessel centerlines. Enhanced lumens were segmented by five independent operators. On average, stenosis measurement accuracy was 0.9% positively biased for the motion-free condition. Average measurement accuracy monotonically decreased from 0.9% positive bias for the motion-free condition to 18.5% negative bias at 90 beats per minute. Contrast-to-noise ratio, lumen circularity, and segmentation conformity also decreased monotonically with increasing heart-rate. These results demonstrate successful implementation of a base cardiac phantom with a 3D-printed coronary plaque model, relevant motion profile, and coordinated ECG waveform. They further show the utility of the model to ascertain metrics of CCTA accuracy and image quality under realistic plaque, motion, and acquisition conditions.
The purpose of this study was to quantify the accuracy of coronary computed tomography angiography (CTA) stenosis measurements using newly developed physical coronary plaque models attached to a base dynamic cardiac phantom (Shelley Medical DHP-01). Coronary plaque models (5 mm diameter, 50% stenosis, and 32 mm long) were designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine enhanced lumen). Realistic cardiac motion was achieved by fitting known cardiac motion vectors to left ventricle volume-time curves to create synchronized heart motion profiles executed by the base cardiac phantom. Realistic coronary CTA acquisition was accomplished by synthesizing corresponding ECG waveforms for gating and reconstruction purposes. All scans were acquired using a retrospective gating technique on a dual-source CT system (Siemens SOMATOM FLASH) with 75ms temporal resolution. Multi-planar reformatted images were reconstructed along vessel centerlines and the enhanced lumens were manually segmented by 5 independent operators. On average, the stenosis measurement accuracy was 0.9% positive bias for the motion free condition (0 bpm). The measurement accuracy monotonically decreased to 18.5% negative bias at 90 bpm. Contrast-tonoise (CNR), vessel circularity, and segmentation conformity also decreased monotonically with increasing heart rate. These results demonstrate successful implementation of the base cardiac phantom with 3D-printed coronary plaque models, adjustable motion profiles, and coordinated ECG waveforms. They further show the utility of the model to ascertain metrics of coronary CT accuracy and image quality under a variety of plaque, motion, and acquisition conditions.
The purpose of this work was to compare CT low-contrast detectability between two reconstruction algorithms, filtered back-projection (FBP) and advanced modeled iterative reconstruction (ADMIRE). A phantom was designed with a range of low-contrast circular inserts representing 5 contrast levels and 3 sizes. The phantom was imaged on a third-generation dual-source CT scanner (SOMATOM Definition Force, Siemens Healthcare) under various dose levels (0.74 – 5.8 mGy CTDIVol). Images were reconstructed using different settings of slice thickness (0.6 – 5 mm) and reconstruction algorithms (FBP and ADMIRE with strength of 3-5) and were assessed by eleven blinded and independent readers using a two alternative forced choice (2AFC) detection experiment. A second observer experiment was further performed in which observers scored the images based on the total number of visible object groups. Detection performance increased with increasing contrast, size, dose, with accuracy ranging from 50% (i.e., guessing) to 87% with an average inter-observer variability of ±7%. The use of ADMIRE-3 increased performance by 5.2% resulting in an estimated dose reduction potential of 56-60%. The results from the second experiment also showed increased number of visible object groups for increasing dose, slice thickness, and ADMIRE strength. The score difference between FBP and ADMIRE was 0.9, 1.3, and 2.1 for ADMIRE strengths of 3, 4, and 5, respectively, resulting in estimated dose reduction potentials between 4-80%. Overall, the data indicated potential to image at reduced doses while maintaining comparable image quality when using ADMIRE compared to FBP.
The purpose of this study is to investigate how well model observer can correlate with human observer in the lesion
detection and localization task when the location of lesion is uncertain in CT imaging. A 35 × 26 cm oblong-shaped
water phantom was scanned with and without two cylindrical rods (3 mm and 5 mm diameters) to simulate lesions with -
15HU contrast. Scans were repeated 100 times with the rods and 100 times without for each of 4 dose levels. Signal
and background images were generated by selecting ROIs with 128x128 pixels, with the location of signal in each ROI
randomly distributed. Human observer studies were conducted as three medical physicists identified the presence or
absence of lesion, indicated the lesion location in each image and scored confidence level with a 6-point scale. ROC
curves were fitted and area under curve (AUC) was calculated. The same data set was also analyzed using a
Channelized Hottelling model observer with Gabor channels. Internal noise was added to the test variables for model
observer study. AUC of ROC and LROC curves were calculated using non-parametric approach. The performance of
human observer and model observer was compared. The Peason's product-moment correlation coefficients were 0.994
and 0.998 for 3mm and 5mm diameter lesions in ROC analysis and 0.987 and 0.999 in LROC analysis, indicating that
model observer performance was highly correlated with the human observer performance for different size of lesions
and different dose levels when signal location is uncertain. These results provide the potential of using model observer
that correlates with human observer to assess CT image quality, optimize scanning protocol and reduce radiation dose.
KEYWORDS: Compressed sensing, Computed tomography, Image quality, Reconstruction algorithms, In vivo imaging, Data acquisition, Image restoration, Computer simulations, Signal attenuation, Signal to noise ratio
The purpose of this paper is
to present a new image reconstruction algorithm for dynamic data, termed non-convex prior
image constrained compressed sensing (NC-PICCS). It generalizes the prior image constrained compressed sensing
(PICCS) algorithm with the use of non-convex priors. Here, we concentrate on perfusion studies using computed
tomography examples in simulated phantoms (with and without added noise) and in vivo data, to show how the NC-PICCS
method holds potential for dramatic reductions in radiation dose for time-resolved CT imaging. We show that NC-PICCS can provide additional undersampling compared to conventional convex compressed sensing and PICCS, as well as, faster convergence under a quasi-Newton numerical solver.
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