Quantitative material characterization from multi‐energy photon counting CT

Alessio, Adam M.; MacDonald, L.R.

doi:10.1118/1.4790692

Cited by 64 publications

(51 citation statements)

References 23 publications

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“…Other groups [25,63,64] have reported noisy regions with incorrect material identification of high-Z materials but they claimed their ability to detect low concentrations of high-Z materials with high accuracy by measuring the concentrations in decomposed images and comparing it to the true concentration value. Our results show that good correlation over the full range of the known and measured concentrations fails to provide information about the misidentification between different materials and the extent of such misidentification in decomposed material images.…”

Section: Discussionmentioning

confidence: 99%

Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

et al. 2018

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Material decomposition methods are used to identify and quantify multiple tissue components in spectral CT but there is no published method to quantify the misidentification of materials. This paper describes a new method for assessing misidentification and mis-quantification in spectral CT. We scanned a phantom containing gadolinium (1, 2, 4, 8 mg/mL), hydroxyapatite (54.3, 211.7, 808.5 mg/mL), water and vegetable oil using a MARS spectral scanner equipped with a poly-energetic X-ray source operated at 118 kVp and a CdTe Medipix3RX camera. Two imaging protocols were used; both with and without 0.375 mm external brass filter. A proprietary material decomposition method identified voxels as gadolinium, hydroxyapatite, lipid or water. Sensitivity and specificity information was used to evaluate material misidentification. Biological samples were also scanned. There were marked differences in identification and quantification between the two protocols even though spectral and linear correlation of gadolinium and hydroxyapatite in the reconstructed images was high and no qualitative segmentation differences in the material decomposed images were observed. At 8 mg/mL, gadolinium was correctly identified for both protocols, but concentration was underestimated by over half for the unfiltered protocol. At 1 mg/mL, gadolinium was misidentified in 38% of voxels for the filtered protocol and 58% of voxels for the unfiltered protocol. Hydroxyapatite was correctly identified at the two higher concentrations for both protocols, but mis-quantified for the unfiltered protocol. Gadolinium concentration as measured in the biological specimen showed a two-fold difference between protocols. In future, this methodology could be used to compare and optimize scanning protocols, image reconstruction methods, and methods for material differentiation in spectral CT.

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Section: Discussionmentioning

confidence: 99%

Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

et al. 2018

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“…They offer potential benefits in image quality and dose, arising from reduced electronics noise, capability for energy discrimination, and better use of low-energy photons (optimal energy weighting) compared to energy-integrating detectors (Tapiovaara and Wagner 1985, Tanguay et al 2013, Xu et al 2014). A variety of PCD-based x-ray systems have been proposed for applications such as mammography (Thunberg et al 2004, Fredenberg et al 2010a, Cole et al 2012), radiography (Francke et al 2001, Weigel et al 2014), tomography (Maidment et al 2005, Schmitzberger et al 2011, Alivov et al 2014), and energy-resolved imaging (Wang et al 2011, Alessio and MacDonald 2013, Silkwood et al 2013). A comprehensive review of current implementations, potential benefits, and clinical and preclinical applications of PCDs can be found in (Taguchi and Iwanczyk 2013).…”

Section: Introductionmentioning

confidence: 99%

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters)

et al. 2015

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Novel x-ray medical imaging sensors, such as photon counting detectors (PCDs) and large area CCD and CMOS cameras can involve irregular and/or sparse sampling of the detector plane. Application of such detectors to CT involves undersampling that is markedly different from the commonly considered case of sparse angular sampling. This work investigates volumetric sampling in CT systems incorporating sparsely sampled detectors with axial and helical scan orbits and evaluates performance of model-based image reconstruction (MBIR) with spatially varying regularization in mitigating artifacts due to sparse detector sampling. Volumetric metrics of sampling density and uniformity were introduced. Penalized-likelihood MBIR with a spatially varying penalty that homogenized resolution by accounting for variations in local sampling density (i.e. detector gaps) was evaluated. The proposed methodology was tested in simulations and on an imaging bench based on a Si-strip PCD (total area 5 cm × 25 cm) consisting of an arrangement of line sensors separated by gaps of up to 2.5 mm. The bench was equipped with translation/rotation stages allowing a variety of scanning trajectories, ranging from a simple axial acquisition to helical scans with variable pitch. Statistical (spherical clutter) and anthropomorphic (hand) phantoms were considered. Image quality was compared to that obtained with a conventional uniform penalty in terms of structural similarity index (SSIM), image uniformity, spatial resolution, contrast, and noise. Scan trajectories with intermediate helical width (~10 mm longitudinal distance per 360° rotation) demonstrated optimal tradeoff between the average sampling density and the homogeneity of sampling throughout the volume. For a scan trajectory with 10.8 mm helical width, the spatially varying penalty resulted in significant visual reduction of sampling artifacts, confirmed by a 10% reduction in minimum SSIM (from 0.88 to 0.8) and a 40% reduction in the dispersion of SSIM in the volume compared to the constant penalty (both penalties applied at optimal regularization strength). Images of the spherical clutter and wrist phantoms confirmed the advantages of the spatially varying penalty, showing a 25% improvement in image uniformity and 1.8 × higher CNR (at matched spatial resolution) compared to the constant penalty. The studies elucidate the relationship between sampling in the detector plane, acquisition orbit, sampling of the reconstructed volume, and the resulting image quality. They also demonstrate the benefit of spatially varying regularization in MBIR for scenarios with irregular sampling patterns. Such findings are important and integral to the incorporation of a sparsely sampled Si-strip PCD in CT imaging.

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“…9 In addition, DECT, such as dual-source CT, sometimes requires relatively high dose levels as compared to conventional CT, 11 and it is not safe for sequential studies. 12 As a result, these limitations currently exclude DECT from widespread use in several clinical applications and from serial studies in plaque imaging.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have shown that material decomposition with spectral CT imaging provides better differentiation of materials for atherosclerotic plaque features. 4,12,[28][29][30][31] Nonetheless, there are limited studies of quantitative metrics for evaluating performance in terms of objective image assessment exploiting a numerical observer in spectral CT imaging. 32 The major problems may be because it is difficult to correlate the data from different energy bins, especially given the complexity of the spectral CT data, since spectral CT imaging can be considered a four-dimensional (4-D) technique to form measurements in three spatial dimensions and a unique energy dimension.…”

Section: Introductionmentioning

confidence: 99%

“…MECT has potential to better characterize and quantify the concentration of the plaque components (e.g., water, lipid, and calcium) as well as various contrast agents (e.g., iodine, gadolinium, and gold) relative to DECT. 4,12 The photon-counting detector approach is one technical strategy to implementing MECT, since it can allow more than the two-energy windows available in DECT. CT based on photon-counting detectors measures each incident x-ray photon as it interacts with the detector and generates an electronic signal, with the measured event then accumulated into different bins based on the measured photon energy.…”

Section: Introductionmentioning

confidence: 99%

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Numerical observer for atherosclerotic plaque classification in spectral computed tomography

Lorsakul

Fakhri

Worstell³

et al. 2016

J. Med. Imag

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Abstract. Spectral computed tomography (SCT) generates better image quality than conventional computed tomography (CT). It has overcome several limitations for imaging atherosclerotic plaque. However, the literature evaluating the performance of SCT based on objective image assessment is very limited for the task of discriminating plaques. We developed a numerical-observer method and used it to assess performance on discrimination vulnerable-plaque features and compared the performance among multienergy CT (MECT), dualenergy CT (DECT), and conventional CT methods. Our numerical observer was designed to incorporate all spectral information and comprised two-processing stages. First, each energy-window domain was preprocessed by a set of localized channelized Hotelling observers (CHO). In this step, the spectral image in each energy bin was decorrelated using localized prewhitening and matched filtering with a set of Laguerre-Gaussian channel functions. Second, the series of the intermediate scores computed from all the CHOs were integrated by a Hotelling observer with an additional prewhitening and matched filter. The overall signal-to-noise ratio (SNR) and the area under the receiver operating characteristic curve (AUC) were obtained, yielding an overall discrimination performance metric. The performance of our new observer was evaluated for the particular binary classification task of differentiating between alternative plaque characterizations in carotid arteries. A clinically realistic model of signal variability was also included in our simulation of the discrimination tasks. The inclusion of signal variation is a key to applying the proposed observer method to spectral CT data. Hence, the task-based approaches based on the signal-known-exactly/background-known-exactly (SKE/BKE) framework and the clinical-relevant signal-known-statistically/background-known-exactly (SKS/BKE) framework were applied for analytical computation of figures of merit (FOM). Simulated data of a carotid-atherosclerosis patient were used to validate our methods. We used an extended cardiac-torso anthropomorphic digital phantom and three simulated plaque types (i.e., calcified plaque, fatty-mixed plaque, and iodine-mixed blood). The images were reconstructed using a standard filtered backprojection (FBP) algorithm for all the acquisition methods and were applied to perform two different discrimination tasks of: (1) calcified plaque versus fatty-mixed plaque and (2) calcified plaque versus iodine-mixed blood. MECT outperformed DECT and conventional CT systems for all cases of the SKE/BKE and SKS/BKE tasks (all p < 0.01). On average of signal variability, MECT yielded the SNR improvements over other acquisition methods in the range of 46.8% to 65.3% (all p < 0.01) for FBP-Ramp images and 53.2% to 67.7% (all p < 0.01) for FBP-Hanning images for both identification tasks. This proposed numerical observer combined with our signal variability framework is promising for assessing material characterization obtained through the additional energ...

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Quantitative material characterization from multi‐energy photon counting CT

Cited by 64 publications

References 23 publications

Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

Measuring Identification and Quantification Errors in Spectral CT Material Decomposition

Volumetric CT with sparse detector arrays (and application to Si-strip photon counters)

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

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