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Optimization of a dual-energy contrast-enhanced technique for a photon-counting digital breast tomosynthesis system: II. An experimental validation.

ABSTRACT: Previously, the authors developed a dual-energy (DE) acquisition technique for a photon-counting digital breast tomosynthesis (DBT) imaging system. Low-energy (LE) and high-energy (HE) images are acquired in a single scan by covering alternate slits of a multislit prepatient collimator with Sn and Cu, respectively. A theoretical model was used to optimize the technique. In this article, an experimental validation of this technique is presented.Experiments were performed on a prototype DBT system. LE and HE projection images were acquired sequentially; either a Sn or a Cu filter was positioned in the filter holder at the exit window of the x-ray tube. Sn filters from 0.113 to 0.242 mm thick and Cu filters from 0.103 to 0.267 mm were used. The images were acquired with a W target at 49 kV. Tomographic images, hereafter referred to as DBT images, were reconstructed using a shift-and-add algorithm. DE-DBT images were obtained by weighted logarithmic subtraction of the LE and HE images. Weighting factors w(t) that optimally cancel breast tissues with two different glandularities were assessed for 20-80 mm thick phantoms with 0%, 50%, and 100% glandularity. The mean and standard deviation in the per-pixel signal intensity (SI) were calculated in the DBT images. These data were used to calculate signal-difference-to-noise ratios (SDNRs) between iodine enhanced and nonenhanced polymethyl methacrylate backgrounds. To illustrate the feasibility of the technique, DE-DBT images of a structured phantom containing iodine disks were assessed. The experimental results were compared against the values obtained from a theoretical model of the imaging system.The average difference between theoretical and experimental w(t) was found to range from 8% to 21%. Experimental w(t) values increase with phantom thickness and Cu thickness, depend somewhat on Sn thickness, and vary more as a function of breast composition in thick breasts than in thin breasts. Theoretical and experimental mean and standard deviation in the per-pixel SI differ by -7% to 10% and by -3% to 4%. Theoretical and experimental SDNR values differ, on average, by 1.5%. Iodine concentrations can be predicted from SDNR; the relationship can be accurately fit to a quadratic. In the images of the structured phantom, iodine concentrations of 1 mg/cm2 and larger are discernable.The strong agreement between experimental and theoretical results in this article indicates that the authors' computer model is accurate.

SUBMITTER: Carton AK

PROVIDER: S-EPMC3188981 | biostudies-other | 2010 Nov

REPOSITORIES: biostudies-other

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Project description:Dual-energy (DE) iodine contrast-enhanced x-ray imaging of the breast has been shown to identify cancers that would otherwise be mammographically occult. In this article, theoretical modeling was performed to obtain optimally enhanced iodine images for a photon-counting digital breast tomosynthesis (DBT) system using a DE acquisition technique.In the system examined, the breast is scanned with a multislit prepatient collimator aligned with a multidetector camera. Each detector collects a projection image at a unique angle during the scan. Low-energy (LE) and high-energy (HE) projection images are acquired simultaneously in a single scan by covering alternate collimator slits with Sn and Cu filters, respectively. Sn filters ranging from 0.08 to 0.22 mm thickness and Cu filters from 0.11 to 0.27 mm thickness were investigated. A tube voltage of 49 kV was selected. Tomographic images, hereafter referred to as DBT images, were reconstructed using a shift-and-add algorithm. Iodine-enhanced DBT images were acquired by performing a weighted logarithmic subtraction of the HE and LE DBT images, The DE technique was evaluated for 20-80 mm thick breasts. Weighting factors, w(t) that optimally cancel breast tissue were computed. Signal-difference-to-noise ratios (SDNRs) between iodine-enhanced and nonenhanced breast tissue normalized to the square root of the mean glandular dose (MGD) were computed as a function of the fraction of the MGD allocated to the HE images. Peak SDNR/ mean square root of MGD and optimal dose allocations were identified. SDNR/ mean square root of MGD and dose allocations were computed for several practical feasible system configurations (i.e., determined by the number of collimator slits covered by Sn and Cu). A practicalsystem configuration an d Sn-Cu filterpair that accounts for the trade-off between SDNR, tube-output, and MGD were selected.w(t) depends on the Sn-Cu filter combination used, as well as on the breast thickness; to optimally cancel 0% with 50% glandular breast tissue, w(t) values were found to range from 0.46 to 0.72 for all breast thicknesses and Sn-Cu filter pairs studied. The optimal w(t) values needed to cancel all possible breast tissue glandularites vary by less than 1% for 20 mm thick breasts and 18% for 80 mm breasts. The system configuration where one collimator slit covered by Sn is alternated with two collimator slits covered by Cu delivers SDNR/ mean square root of MGD nearest to the peak value. A reasonable compromise is a 0.16 mm Sn-0.23 mm Cu filter pair, resulting in SDNR values between 1.64 and 0.61 and MGD between 0.70 and 0.53 mGy for 20-80 mm thick breasts at the maximum tube current.A DE acquisition technique for a photon-counting DBT imaging system has been developed and optimized.

Project description:Purpose To conduct a multi-institutional, multireader study to compare the performance of digital tomosynthesis, dual-energy (DE) imaging, and conventional chest radiography for pulmonary nodule detection and management. Materials and Methods In this binational, institutional review board-approved, HIPAA-compliant prospective study, 158 subjects (43 subjects with normal findings) were enrolled at four institutions. Informed consent was obtained prior to enrollment. Subjects underwent chest computed tomography (CT) and imaging with conventional chest radiography (posteroanterior and lateral), DE imaging, and tomosynthesis with a flat-panel imaging device. Three experienced thoracic radiologists identified true locations of nodules (n = 516, 3-20-mm diameters) with CT and recommended case management by using Fleischner Society guidelines. Five other radiologists marked nodules and indicated case management by using images from conventional chest radiography, conventional chest radiography plus DE imaging, tomosynthesis, and tomosynthesis plus DE imaging. Sensitivity, specificity, and overall accuracy were measured by using the free-response receiver operating characteristic method and the receiver operating characteristic method for nodule detection and case management, respectively. Results were further analyzed according to nodule diameter categories (3-4 mm, >4 mm to 6 mm, >6 mm to 8 mm, and >8 mm to 20 mm). Results Maximum lesion localization fraction was higher for tomosynthesis than for conventional chest radiography in all nodule size categories (3.55-fold for all nodules, P < .001; 95% confidence interval [CI]: 2.96, 4.15). Case-level sensitivity was higher with tomosynthesis than with conventional chest radiography for all nodules (1.49-fold, P < .001; 95% CI: 1.25, 1.73). Case management decisions showed better overall accuracy with tomosynthesis than with conventional chest radiography, as given by the area under the receiver operating characteristic curve (1.23-fold, P < .001; 95% CI: 1.15, 1.32). There were no differences in any specificity measures. DE imaging did not significantly affect nodule detection when paired with either conventional chest radiography or tomosynthesis. Conclusion Tomosynthesis outperformed conventional chest radiography for lung nodule detection and determination of case management; DE imaging did not show significant differences over conventional chest radiography or tomosynthesis alone. These findings indicate performance likely achievable with a range of reader expertise. © RSNA, 2016 Online supplemental material is available for this article.

Project description:We developed a novel dual-energy (DE) virtual monochromatic (VM) very-deep super-resolution (VDSR) method with an unsharp masking reconstruction algorithm (DE-VM-VDSR) that uses projection data to improve the nodule contrast and reduce ripple artifacts during chest digital tomosynthesis (DT). For estimating the residual errors from high-resolution and multiscale VM images from the projection space, the DE-VM-VDSR algorithm employs a training network (mini-batch stochastic gradient-descent algorithm with momentum) and a hybrid super-resolution (SR) image [simultaneous algebraic reconstruction technique (SART) total-variation (TV) first-iterative shrinkage-thresholding algorithm (FISTA); SART-TV-FISTA] that involves subjective reconstruction with bilateral filtering (BF) [DE-VM-VDSR with BF]. DE-DT imaging was accomplished by pulsed X-ray exposures rapidly switched between low (60 kV, 37 projection) and high (120 kV, 37 projection) tube-potential kVp by employing a 40° swing angle. This was followed by comparison of images obtained employing the conventional polychromatic filtered backprojection (FBP), SART, SART-TV-FISTA, and DE-VM-SART-TV-FISTA algorithms. The improvements in contrast, ripple artifacts, and resolution were compared using the signal-difference-to-noise ratio (SDNR), Gumbel distribution of the largest variations, radial modulation transfer function (radial MTF) for a chest phantom with simulated ground-glass opacity (GGO) nodules, and noise power spectrum (NPS) for uniform water phantom. The novel DE-VM-VDSR with BF improved the overall performance in terms of SDNR (DE-VM-VDSR with BF: 0.1603, without BF: 0.1517; FBP: 0.0521; SART: 0.0645; SART-TV-FISTA: 0.0984; and DE-VM-SART-TV-FISTA: 0.1004), obtained a Gumbel distribution that yielded good images showing the type of simulated GGO nodules used in the chest phantom, and reduced the ripple artifacts. The NPS of DE-VM-VDSR with BF showed the lowest noise characteristics in the high-frequency region (~0.8 cycles/mm). The DE-VM-VDSR without BF yielded an improved resolution relative to that of the conventional reconstruction algorithms for radial MTF analysis (0.2-0.3 cycles/mm). Finally, based on the overall image quality, DE-VM-VDSR with BF improved the contrast and reduced the high-frequency ripple artifacts and noise.

Project description:Breast CT is an emerging imaging technique that can portray the breast in 3D and improve visualization of important diagnostic features. Early clinical studies have suggested that breast CT has sufficient spatial and contrast resolution for accurate detection of masses and microcalcifications in the breast, reducing structural overlap that is often a limiting factor in reading mammographic images. For a number of reasons, image quality in breast CT may be improved by use of an energy resolving photon counting detector. In this study, the authors investigate the improvements in image quality obtained when using energy weighting with an energy resolving photon counting detector as compared to that with a conventional energy integrating detector.Using computer simulation, realistic CT images of multiple breast phantoms were generated. The simulation modeled a prototype breast CT system using an amorphous silicon (a-Si), CsI based energy integrating detector with different x-ray spectra, and a hypothetical, ideal CZT based photon counting detector with capability of energy discrimination. Three biological signals of interest were modeled as spherical lesions and inserted into breast phantoms; hydroxyapatite (HA) to represent microcalcification, infiltrating ductal carcinoma (IDC), and iodine enhanced infiltrating ductal carcinoma (IIDC). Signal-to-noise ratio (SNR) of these three lesions was measured from the CT reconstructions. In addition, a psychophysical study was conducted to evaluate observer performance in detecting microcalcifications embedded into a realistic anthropomorphic breast phantom.In the energy range tested, improvements in SNR with a photon counting detector using energy weighting was higher (than the energy integrating detector method) by 30%-63% and 4%-34%, for HA and IDC lesions and 12%-30% (with Al filtration) and 32%-38% (with Ce filtration) for the IIDC lesion, respectively. The average area under the receiver operating characteristic curve (AUC) for detection of microcalcifications was higher by greater than 19% (for the different energy weighting methods tested) as compared to the AUC obtained with an energy integrating detector.This study showed that breast CT with a CZT photon counting detector using energy weighting can provide improvements in pixel SNR, and detectability of microcalcifications as compared to that with a conventional energy integrating detector. Since a number of degrading physical factors were not modeled into the photon counting detector, this improvement should be considered as an upper bound on achievable performance.

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Optimization of a dual-energy contrast-enhanced technique for a photon-counting digital breast tomosynthesis system: II. An experimental validation.

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