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Noise, sampling, and the number of projections in cone-beam CT with a flat-panel detector.

ABSTRACT: To investigate the effect of the number of projection views on image noise in cone-beam CT (CBCT) with a flat-panel detector.This fairly fundamental consideration in CBCT system design and operation was addressed experimentally (using a phantom presenting a uniform medium as well as statistically motivated "clutter") and theoretically (using a cascaded systems model describing CBCT noise) to elucidate the contributing factors of quantum noise (σ(Q)), electronic noise (σ(E)), and view aliasing (σ(view)). Analysis included investigation of the noise, noise-power spectrum, and modulation transfer function as a function of the number of projections (N(proj)), dose (D(tot)), and voxel size (b(vox)).The results reveal a nonmonotonic relationship between image noise and N(proj) at fixed total dose: for the CBCT system considered, noise decreased with increasing N(proj) due to reduction of view sampling effects in the regime N(proj) <~200, above which noise increased with N(proj) due to increased electronic noise. View sampling effects were shown to depend on the heterogeneity of the object in a direct analytical relationship to power-law anatomical clutter of the form κ/f(β)--and a general model of individual noise components (σ(Q), σ(E), and σ(view)) demonstrated agreement with measurements over a broad range in N(proj), D(tot), and b(vox).The work elucidates fairly basic elements of CBCT noise in a manner that demonstrates the role of distinct noise components (viz., quantum, electronic, and view sampling noise). For configurations fairly typical of CBCT with a flat-panel detector (FPD), the analysis reveals a "sweet spot" (i.e., minimum noise) in the range N(proj) ~ 250-350, nearly an order of magnitude lower in N(proj) than typical of multidetector CT, owing to the relatively high electronic noise in FPDs. The analysis explicitly relates view aliasing and quantum noise in a manner that includes aspects of the object ("clutter") and imaging chain (including nonidealities of detector blur and electronic noise) to provide a more rigorous basis for commonly held intuition and heurism in CBCT system design and operation.

SUBMITTER: Zhao Z

PROVIDER: S-EPMC4032401 | biostudies-other | 2014 Jun

REPOSITORIES: biostudies-other

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Project description:Cone-beam CT (CBCT) with a flat-panel detector (FPD) is finding application in areas such as breast and musculoskeletal imaging, where dual-energy (DE) capabilities offer potential benefit. The authors investigate the accuracy of material classification in DE CBCT using filtered backprojection (FBP) and penalized likelihood (PL) reconstruction and optimize contrast-enhanced DE CBCT of the joints as a function of dose, material concentration, and detail size.Phantoms consisting of a 15 cm diameter water cylinder with solid calcium inserts (50-200 mg/ml, 3-28.4 mm diameter) and solid iodine inserts (2-10 mg/ml, 3-28.4 mm diameter), as well as a cadaveric knee with intra-articular injection of iodine were imaged on a CBCT bench with a Varian 4343 FPD. The low energy (LE) beam was 70 kVp (+0.2 mm Cu), and the high energy (HE) beam was 120 kVp (+0.2 mm Cu, +0.5 mm Ag). Total dose (LE+HE) was varied from 3.1 to 15.6 mGy with equal dose allocation. Image-based DE classification involved a nearest distance classifier in the space of LE versus HE attenuation values. Recognizing the differences in noise between LE and HE beams, the LE and HE data were differentially filtered (in FBP) or regularized (in PL). Both a quadratic (PLQ) and a total-variation penalty (PLTV) were investigated for PL. The performance of DE CBCT material discrimination was quantified in terms of voxelwise specificity, sensitivity, and accuracy.Noise in the HE image was primarily responsible for classification errors within the contrast inserts, whereas noise in the LE image mainly influenced classification in the surrounding water. For inserts of diameter 28.4 mm, DE CBCT reconstructions were optimized to maximize the total combined accuracy across the range of calcium and iodine concentrations, yielding values of ∼ 88% for FBP and PLQ, and ∼ 95% for PLTV at 3.1 mGy total dose, increasing to ∼ 95% for FBP and PLQ, and ∼ 98% for PLTV at 15.6 mGy total dose. For a fixed iodine concentration of 5 mg/ml and reconstructions maximizing overall accuracy across the range of insert diameters, the minimum diameter classified with accuracy >80% was ∼ 15 mm for FBP and PLQ and ∼ 10 mm for PLTV, improving to ∼ 7 mm for FBP and PLQ and ∼ 3 mm for PLTV at 15.6 mGy. The results indicate similar performance for FBP and PLQ and showed improved classification accuracy with edge-preserving PLTV. A slight preference for increased smoothing of the HE data was found. DE CBCT discrimination of iodine and bone in the knee was demonstrated with FBP and PLTV at 6.2 mGy total dose.For iodine concentrations >5 mg/ml and detail size ∼ 20 mm, material classification accuracy of >90% was achieved in DE CBCT with both FBP and PL at total doses <10 mGy. Optimal performance was attained by selection of reconstruction parameters based on the differences in noise between HE and LE data, typically favoring stronger smoothing of the HE data, and by using penalties matched to the imaging task (e.g., edge-preserving PLTV in areas of uniform enhancement).

Project description:To measure and investigate the improvement of microcalcification (MC) visibility in cone beam breast CT with a high pitch (75 μm), thick (500 μm) scintillator CMOS/CsI flat panel detector (Dexela 2923, Perkin Elmer).Aluminum wires and calcium carbonate grains of various sizes were embedded in a paraffin cylinder to simulate imaging of calcifications in a breast. Phantoms were imaged with a benchtop experimental cone beam CT system at various exposure levels. In addition to the Dexela detector, a high pitch (50 μm), thin (150 μm) scintillator CMOS/CsI flat panel detector (C7921CA-09, Hamamatsu Corporation, Hamamatsu City, Japan) and a widely used low pitch (194 μm), thick (600 μm) scintillator aSi/CsI flat panel detector (PaxScan 4030CB, Varian Medical Systems) were also used in scanning for comparison. The images were independently reviewed by six readers (imaging physicists). The MC visibility was quantified as the fraction of visible MCs and measured as a function of the estimated mean glandular dose (MGD) level for various MC sizes and detectors. The modulation transfer functions (MTFs) and detective quantum efficiencies (DQEs) were also measured and compared for the three detectors used.The authors have demonstrated that the use of a high pitch (75 μm) CMOS detector coupled with a thick (500 μm) CsI scintillator helped make the smaller 150-160, 160-180, and 180-200 μm MC groups more visible at MGDs up to 10.8, 9, and 10.8 mGy, respectively. It also made the larger 200-212 and 212-224 μm MC groups more visible at MGDs up to 7.2 mGy. No performance improvement was observed for 224-250 μm or larger size groups. With the higher spatial resolution of the Dexela detector based system, the apparent dimensions and shapes of MCs were more accurately rendered. The results show that with the aforementioned detector, a 73% visibility could be achieved in imaging 160-180 μm MCs as compared to 28% visibility achieved by the low pitch (194 μm) aSi/CsI flat panel detector. The measurements confirm that the Hamamatsu detector has the highest MTF, followed by the Dexel detector, and then the Varian detector. However, the Dexela detector, with its thick (500 μm) CsI scintillator and low noise level, has the highest DQE at all frequencies, followed by the Varian detector, and then the Hamamatsu detector. The findings on the MC visibility correlated well with the differences in MTFs, noise power spectra, and DQEs measured for these three detectors.The authors have demonstrated that the use of the CMOS type Dexela detector with its high pitch (75 μm) and thick (500 μm) CsI scintillator could help improve the MC visibility. However, the improvement depended on the exposure level and the MC size. For imaging larger MCs or scanning at high exposure levels, there was little advantage in using the Dexela detector as compared to the aSi type Varian detector. These findings correlate well with the higher measured DQEs of the Dexela detector, especially at higher frequencies.

Project description:To determine the mean and range of location-averaged breast skin thickness using high-resolution dedicated breast CT for use in Monte Carlo-based estimation of normalized glandular dose coefficients.This study retrospectively analyzed image data from a clinical study investigating dedicated breast CT. An algorithm similar to that described by Huang et al. ["The effect of skin thickness determined using breast CT on mammographic dosimetry," Med. Phys. 35(4), 1199-1206 (2008)] was used to determine the skin thickness in 137 dedicated breast CT volumes from 136 women. The location-averaged mean breast skin thickness for each breast was estimated and the study population mean and range were determined. Pathology results were available for 132 women, and were used to investigate if the distribution of location-averaged mean breast skin thickness varied with pathology. The effect of surface fitting to account for breast curvature was also studied.The study mean (± interbreast SD) for breast skin thickness was 1.44 ± 0.25 mm (range: 0.87-2.34 mm), which was in excellent agreement with Huang et al. Based on pathology, pair-wise statistical analysis (Mann-Whitney test) indicated that at the 0.05 significance level, there were no significant difference in the location-averaged mean breast skin thickness distributions between the groups: benign vs malignant (p = 0.223), benign vs hyperplasia (p = 0.651), hyperplasia vs malignant (p = 0.229), and malignant vs nonmalignant (p = 0.172).Considering this study used a different clinical prototype system, and the study participants were from a different geographical location, the observed agreement between the two studies suggests that the choice of 1.45 mm thick skin layer comprising the epidermis and the dermis for breast dosimetry is appropriate. While some benign and malignant conditions could cause skin thickening, in this study cohort the location-averaged mean breast skin thickness distributions did not differ significantly with pathology. The study also underscored the importance of considering breast curvature in estimating breast skin thickness.

Project description:Objective.A dedicated cone-beam breast computed tomography (BCT) using a high-resolution, low-noise detector operating in offset-detector geometry has been developed. This study investigates the effects of varying detector offsets and image reconstruction algorithms to determine the appropriate combination of detector offset and reconstruction algorithm.Approach.Projection datasets (300 projections in 360°) of 30 breasts containing calcified lesions that were acquired using a prototype cone-beam BCT system comprising a 40 × 30 cm flat-panel detector with 1024 × 768 detector pixels were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. The projection datasets were retrospectively truncated to emulate cone-beam datasets with sinograms of 768×768 and 640×768 detector pixels, corresponding to 5 cm and 7.5 cm lateral offsets, respectively. These datasets were reconstructed using the FDK algorithm with appropriate weights and an ASD-POCS-based Fast, total variation-Regularized, Iterative, Statistical reconstruction Technique (FRIST), resulting in a total of 4 offset-detector reconstructions (2 detector offsets × 2 reconstruction methods). Signal difference-to-noise ratio (SDNR), variance, and full-width at half-maximum (FWHM) of calcifications in two orthogonal directions were determined from all reconstructions. All quantitative measurements were performed on images in units of linear attenuation coefficient (1/cm).Results.The FWHM of calcifications did not differ (P > 0.262) among reconstruction algorithms and detector formats, implying comparable spatial resolution. For a chosen detector offset, the FRIST algorithm outperformed FDK in terms of variance and SDNR (P < 0.0001). For a given reconstruction method, the 5 cm offset provided better results.Significance.This study indicates the feasibility of using the compressed sensing-based, FRIST algorithm to reconstruct sinograms from offset-detectors. Among the reconstruction methods and detector offsets studied, FRIST reconstructions corresponding to a 30 cm × 30 cm with 5 cm lateral offset, achieved the best performance. A clinical prototype using such an offset geometry has been developed and installed for clinical trials.

Project description:PurposeA clinical-prototype, dedicated, cone-beam breast computed tomography (CBBCT) system with offset detector is undergoing clinical evaluation at our institution. This study is to estimate the normalized glandular dose coefficients ( DgNCT ) that provide air kerma-to-mean glandular dose conversion factors using Monte Carlo simulations.Materials and methodsThe clinical prototype CBBCT system uses 49 kV x-ray spectrum with 1.39 mm 1st half-value layer thickness. Monte Carlo simulations (GATE, version 8) were performed with semi-ellipsoidal, homogeneous breasts of various fibroglandular weight fractions ( fg=0.01,0.15,0.5,1) , chest wall diameters ( d=8,10,14,18,20 cm), and chest wall to nipple length ( l=0.75d ), aligned with the axis of rotation (AOR) located at 65 cm from the focal spot to determine the DgNCT . Three geometries were considered - 40×30 -cm detector with no offset that served as reference and corresponds to a clinical CBBCT system, 30×30 -cm detector with 5 cm offset, and a 30×30 -cm detector with 10 cm offset.ResultsFor 5 cm lateral offset, the DgNCT ranged 0.177-0.574 mGy/mGy and reduction in DgNCT with respect to reference geometry was observed only for 18 cm ( 6.4%±0.23% ) and 20 cm ( 9.6%±0.22% ) diameter breasts. For the 10 cm lateral offset, the DgNCT ranged 0.221-0.581 mGy/mGy and reduction in DgNCT was observed for all breast diameters. The reduction in DgNCT was 1.4%±0.48% , 7.1%±0.13% , 17.5%±0.19% , 25.1%±0.15% , and 27.7%±0.08% for 8, 10, 14, 18, and 20 cm diameter breasts, respectively. For a given breast diameter, the reduction in DgNCT with offset-detector geometries was not dependent on fg . Numerical fits of DgNCTd,l,fg were generated for each geometry.ConclusionThe DgNCT and the numerical fit, DgNCTd,l,fg would be of benefit for current CBBCT systems using the reference geometry and for future generations using offset-detector geometry. There exists a potential for radiation dose reduction with offset-detector geometry, provided the same technique factors as the reference geometry are used, and the image quality is clinically acceptable.

Project description:Simulation of x-ray projection images plays an important role in cone beam CT (CBCT) related research projects, such as the design of reconstruction algorithms or scanners. A projection image contains primary signal, scatter signal, and noise. It is computationally demanding to perform accurate and realistic computations for all of these components. In this work, the authors develop a package on graphics processing unit (GPU), called gDRR, for the accurate and efficient computations of x-ray projection images in CBCT under clinically realistic conditions.The primary signal is computed by a trilinear ray-tracing algorithm. A Monte Carlo (MC) simulation is then performed, yielding the primary signal and the scatter signal, both with noise. A denoising process specifically designed for Poisson noise removal is applied to obtain a smooth scatter signal. The noise component is then obtained by combining the difference between the MC primary and the ray-tracing primary signals, and the difference between the MC simulated scatter and the denoised scatter signals. Finally, a calibration step converts the calculated noise signal into a realistic one by scaling its amplitude according to a specified mAs level. The computations of gDRR include a number of realistic features, e.g., a bowtie filter, a polyenergetic spectrum, and detector response. The implementation is fine-tuned for a GPU platform to yield high computational efficiency.For a typical CBCT projection with a polyenergetic spectrum, the calculation time for the primary signal using the ray-tracing algorithms is 1.2-2.3 s, while the MC simulations take 28.1-95.3 s, depending on the voxel size. Computation time for all other steps is negligible. The ray-tracing primary signal matches well with the primary part of the MC simulation result. The MC simulated scatter signal using gDRR is in agreement with EGSnrc results with a relative difference of 3.8%. A noise calibration process is conducted to calibrate gDRR against a real CBCT scanner. The calculated projections are accurate and realistic, such that beam-hardening artifacts and scatter artifacts can be reproduced using the simulated projections. The noise amplitudes in the CBCT images reconstructed from the simulated projections also agree with those in the measured images at corresponding mAs levels.A GPU computational tool, gDRR, has been developed for the accurate and efficient simulations of x-ray projections of CBCT with realistic configurations.

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Noise, sampling, and the number of projections in cone-beam CT with a flat-panel detector.

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