S Richard

Optimization of dual-energy imaging systems using generalized NEQ and imaging task

Dual-energy (DE) imaging is a promising advanced application of flat-panel detectors (FPDs) with a potential host of applications ranging from thoracic and cardiac imaging to interventional procedures. The performance of FPD-based DE imaging systems is investigated in this work by incorporating the noise-power spectrum associated with overlying anatomical structures (anatomical noise modeled according to a 1/f characteristic) into descriptions of noise-equivalent quanta (NEQ) to yield the generalized NEQ (GNEQ). Signal and noise propagation in the DE imaging chain is modeled by cascaded systems analysis. A Fourier-based description of the imaging task is integrated with the GNEQ to yield a detectability index used as an objective function for optimizing DE image reconstruction, allocation of dose between low- and high-energy images, and selection of low- and high-kVp. Optimal reconstruction and acquisition parameters were found to depend on dose; for example, optimal kVp varied from [60/150] kVp at typical radiographic dose levels (approximately 0.5 mGy entrance surface dose, ESD) but increased to [90/150] kVp at high dose (ESD approximately 5.0 mGy). At very low dose (ESD approximately 0.05 mGy), detectability index indicates an optimal low-energy technique of 60 kVp but was largely insensitive to the choice of high-kVp in the range 120-150 kVp. Similarly, optimal dose allocation, defined as the ratio of low-energy ESD and the total ESD, varied from 0.2 to 0.4 over the range ESD=(0.05-5.0) mGy. Furthermore, two applications of the theoretical framework were explored: (i) the increase in detectability for DE imaging compared to conventional radiography; and (ii) the performance of single-shot vs double-shot DE imaging, wherein the latter is found to have a DQE approximately twice that of the former. Experimental and theoretical analysis of GNEQ and task-based detectability index provides a fundamental understanding of the factors governing DE imaging performance and offers a framework for system design and optimization.

Comparison of model and human observer performance for detection and discrimination tasks using dual-energy x-ray images

Model observer performance, computed theoretically using cascaded systems analysis (CSA), was compared to the performance of human observers in detection and discrimination tasks. Dual-energy (DE) imaging provided a wide range of acquisition and decomposition parameters for which observer performance could be predicted and measured. This work combined previously derived observer models (e.g., Fisher-Hotelling and non-prewhitening) with CSA modeling of the DE image noise-equivalent quanta (NEQ) and imaging task (e.g., sphere detection, shape discrimination, and texture discrimination) to yield theoretical predictions of detectability index (d) and area under the receiver operating characteristic (Az). Theoretical predictions were compared to human observer performance assessed using 9-alternative forced-choice tests to yield measurement of Az as a function of DE image acquisition parameters (viz., allocation of dose between the low- and high-energy images) and decomposition technique [viz., three DE image decomposition algorithms: standard log subtraction (SLS), simple-smoothing of the high-energy image (SSH), and anti-correlated noise reduction (ACNR)]. Results showed good agreement between theory and measurements over a broad range of imaging conditions. The incorporation of an eye filter and internal noise in the observer models demonstrated improved correspondence with human observer performance. Optimal acquisition and decomposition parameters were shown to depend on the imaging task; for example, ACNR and SSH yielded the greatest performance in the detection of soft-tissue and bony lesions, respectively. This study provides encouraging evidence that Fourier-based modeling of NEQ computed via CSA and imaging task provides a good approximation to human observer performance for simple imaging tasks, helping to bridge the gap between Fourier metrics of detector performance (e.g., NEQ) and human observer performance.

Cascaded systems analysis of noise reduction algorithms in dual-energy imaging.

An important aspect of dual-energy (DE) x-ray image decomposition is the incorporation of noise reduction techniques to mitigate the amplification of quantum noise. This article extends cascaded systems analysis of imaging performance to DE imaging systems incorporating linear noise reduction algorithms. A general analytical formulation of linear DE decomposition is derived, with weighted log subtraction and several previously reported noise reduction algorithms emerging as special cases. The DE image noise-power spectrum (NPS) and modulation transfer function (MTF) demonstrate that noise reduction algorithms impart significant, nontrivial effects on the spatial-frequency-dependent transfer characteristics which do not cancel out of the noise-equivalent quanta (NEQ). Theoretical predictions were validated in comparison to the measured NPS and MTF. The resulting NEQ was integrated with spatial-frequency-dependent task functions to yield the detectability index, d, for evaluation of DE imaging performance using different decomposition algorithms. For a 3 mm lung nodule detection task, the detectability index varied from d < 1 (i.e., nodule barely visible) in the absence of noise reduction to d > 2.5 (i.e., nodule clearly visible) for anti-correlated noise reduction (ACNR) or simple-smoothing of the high-energy image (SSH) algorithms applied to soft-tissue or bone-only decompositions, respectively. Optimal dose allocation (A*, the fraction of total dose delivered in the low-energy projection) was also found to depend on the choice of noise reduction technique. At fixed total dose, multi-function optimization suggested a significant increase in optimal dose allocation from A* = 0.32 for conventional log subtraction to A* = 0.79 for ACNR and SSH in soft-tissue and bone-only decompositions, respectively. Cascaded systems analysis extended to the general formulation of DE image decomposition provided an objective means of investigating DE imaging performance across a broad range of acquisition and decomposition algorithms in a manner that accounts for the spatial-frequency-dependent imaging task.

Soft‐tissue detectability in cone‐beam CT: Evaluation by 2AFC tests in relation to physical performance metrics

Soft-tissue detectability in cone-beam computed tomography (CBCT) was evaluated via two-alternative forced-choice (2AFC) tests. Investigations included the dependence of detectability on radiation dose, the influence of the asymmetric three-dimensional (3D) noise-power spectrum (NPS) in axial and sagittal or coronal planes, and the effect of prior knowledge on detectability. Custom-built phantoms (approximately 15 cm diameter cylinders) containing soft-tissue-simulating spheres of variable contrast and diameter were imaged on an experimental CBCT bench. The proportion of correct responses (Pcorr) in 2AFC tests was analyzed as a figure of merit, ideally equal to the area under the receiver operating characteristic curve. Pcorr was evaluated as a function of the sphere diameter (1.6-12.7 mm), contrast (20-165 HU), dose (1-7 mGy), plane of visualization (axial/sagittal), apodization filter (Hanning and Ram-Lak), and prior knowledge provided to the observer [ranging from stimulus known exactly (SKE) to stimulus unknown (SUK)]. Detectability limits were characterized in terms of the dose required to achieve a given level of Pcorr (e.g., 70%). For example, a 20 HU stimulus of diameter down to approximately 6 mm was detected with Pcorr 70% at dose > or =2 mGy. Detectability tended to be greater in axial than in sagittal planes, an effect amplified by sharper apodization filters in a manner consistent with 3D NPS asymmetry. Prior knowledge had a marked influence on detectability--e.g., Pcorr for a approximately 6 mm (20 HU) sphere was approximately 55%-65% under SUK conditions, compared to approximately 70%-85% for SKE conditions. Human observer tests suggest practical implications for implementation of CBCT: (i) Detectability limits help to define minimum-dose imaging techniques for specific imaging tasks; (ii) detectability of a given structure can vary between axial and sagittal/coronal planes, owing to the spatial-frequency content of the 3D NPS in relation to the imaging task; and (iii) performance under SKE conditions (e.g., image guidance tasks in which lesion characteristics are known) is maintained at a lower dose than in SUK conditions (e.g., diagnostic tasks in which lesion characteristics are unknown).

Dual-energy imaging of the chest: optimization of image acquisition techniques for the 'bone-only' image.

Experiments were conducted to determine optimal acquisition techniques for bone image decompositions for a prototype dual-energy (DE) imaging system. Technique parameters included kVp pair (denoted [kVp(L)/kVp(H)]) and dose allocation (the proportion of dose in low- and high-energy projections), each optimized to provide maximum signal difference-to-noise ratio in DE images. Experiments involved a chest phantom representing an average patient size and containing simulated ribs and lung nodules. Low- and high-energy kVp were varied from 60-90 and 120-150 kVp, respectively. The optimal kVp pair was determined to be [60/130] kVp, with image quality showing a strong dependence on low-kVp selection. Optimal dose allocation was approximately 0.5-i.e., an equal dose imparted by the low- and high-energy projections. The results complement earlier studies of optimal DE soft-tissue image acquisition, with differences attributed to the specific imaging task. Together, the results help to guide the development and implementation of high-performance DE imaging systems, with applications including lung nodule detection and diagnosis, pneumothorax identification, and musculoskeletal imaging (e.g., discrimination of rib fractures from metastasis).

High-performance dual-energy imaging with a flat-panel detector: imaging physics from blackboard to benchtop to bedside

The application of high-performance flat-panel detectors (FPDs) to dual-energy (DE) imaging offers the potential for dramatically improved detection and characterization of subtle lesions through reduction of anatomical noise, with applications ranging from thoracic imaging to image-guided interventions. In this work, we investigate DE imaging performance from first principles of image science to preclinical implementation, including: 1.) generalized task-based formulation of NEQ and detectability as a guide to system optimization; 2.) measurements of imaging performance on a DE imaging benchtop; and 3.) a preclinical system developed in our laboratory for cardiac-gated DE chest imaging in a research cohort of 160 patients. Theoretical and benchtop studies directly guide clinical implementation, including the advantages of double-shot versus single-shot DE imaging, the value of differential added filtration between low- and high-kVp projections, and optimal selection of kVp pairs, filtration, and dose allocation. Evaluation of task-based NEQ indicates that the detectability of subtle lung nodules in double-shot DE imaging can exceed that of single-shot DE imaging by a factor of 4 or greater. Filter materials are investigated that not only harden the high-kVp beam (e.g., Cu or Ag) but also soften the low-kVp beam (e.g., Ce or Gd), leading to significantly increased contrast in DE images. A preclinical imaging system suitable for human studies has been constructed based upon insights gained from these theoretical and experimental studies. An important component of the system is a simple and robust means of cardiac-gated DE image acquisition, implemented here using a fingertip pulse oximeter. Timing schemes that provide cardiac-gated image acquisition on the same or successive heartbeats is described. Preclinical DE images to be acquired under research protocol will afford valuable testing of optimal deployment, facilitate the development of DE CAD, and support comparison of DE diagnostic imaging performance to low-dose CT and radiography.

Development and implementation of a high-performance, cardiac-gated dual-energy imaging system

Mounting evidence suggests that the superposition of anatomical clutter in a projection radiograph poses a major impediment to the detectability of subtle lung nodules. Through decomposition of projections acquired at multiple kVp, dual-energy (DE) imaging offers to dramatically improve lung nodule detectability and, in part through quantitation of nodule calcification, increase specificity in nodule characterization. The development of a high-performance DE chest imaging system is reported, with design and implementation guided by fundamental imaging performance metrics. A diagnostic chest stand (Kodak RVG 5100 digital radiography system) provided the basic platform, modified to include: (i) a filter wheel, (ii) a flat-panel detector (Trixell Pixium 4600), (iii) a computer control and monitoring system for cardiac-gated acquisition, and (iv) DE image decomposition and display. Computational and experimental studies of imaging performance guided optimization of key acquisition technique parameters, including: x-ray filtration, allocation of dose between low- and high-energy projections, and kVp selection. A system for cardiac-gated acquisition was developed, directing x-ray exposures to within the quiescent period of the heart cycle, thereby minimizing anatomical misregistration. A research protocol including 200 patients imaged following lung nodule biopsy is underway, allowing preclinical evaluation of DE imaging performance relative to conventional radiography and low-dose CT.

NEQ and task in dual-energy imaging: From cascaded systems analysis to human observer performance

The relationship between theoretical descriptions of imaging performance (Fourier-based cascaded systems analysis) and the performance of real human observers was investigated for various detection and discrimination tasks. Dual-energy (DE) imaging provided a useful basis for investigating this relationship, because it presents a host of acquisition and processing parameters that can significantly affect signal and noise transfer characteristics and, correspondingly, human observer performance. The detectability index was computed theoretically using: 1) cascaded systems analysis of the modulation transfer function (MTF), and noise-power spectrum (NPS) for DE imaging; 2) a Fourier description of imaging task; and 3.) integration of MTF, NPS, and task function according to various observer models, including Fisher-Hotelling and non-prewhitening with and without an eye filter and internal noise. Three idealized tasks were considered: sphere detection, shape discrimination (sphere vs. disk), and texture discrimination (uniform vs. textured disk). Using images of phantoms acquired on a prototype DE imaging system, human observer performance was assessed in multiple-alternative forced choice (MAFC) tests, giving an estimate of area under the ROC curve (AΖ). The degree to which the theoretical detectability index correlated with human observer performance was investigated, and results agreed well over a broad range of imaging conditions, depending on the choice of observer model. Results demonstrated that optimal DE image acquisition and decomposition parameters depend significantly on the imaging task. These studies provide important initial validation that the detectability index derived theoretically by Fourier-based cascaded systems analysis correlates well with actual human observer performance and represents a meaningful metric for system optimization.

Generalized DQE analysis of dual-energy imaging using flat-panel detectors

Dual-energy (DE) imaging is a promising x-ray modality for the screening and early detection of lung cancer but has seen limited application primarily due to the lack of an adequate image detector. Recent development of flat-panel detectors (FPDs) for advanced imaging applications provide a promising technology for DE imaging, and a theoretical framework to quantify the imaging performance of FPD-based DE imaging systems is useful for system design and optimization. Traditional methods employed to describe imaging performance in radiographic systems [i.e., detective quantum efficiency (DQE) and noise-equivalent quanta (NEQ)] are extended in this paper to DE imaging systems using FPDs. To quantify the essential advantage imparted by DE imaging, we incorporate a spatial-frequency-dependent “anatomical noise” term associated with overlying structures to yield the generalized DQE and NEQ. We estimate anatomical noise in DE images through measurements using an anthropomorphic chest phantom and parameterize the measurements using a 1/f model. Cascaded systems analysis of the generalized NEQ is shown to reveal the tradeoffs between anatomical noise and quantum noise in DE image reconstructions. The generalized dual-energy NEQ is combined with idealized task functions to compute the detectability index, providing an estimate of ideal observer performance in a variety of detection and discrimination tasks. The generalized analysis is employed to investigate optimal tissue cancellation and kVp selection as a function of dose and imaging task.

TH‐C‐330A‐09: Cascaded Systems Analysis of Noise Reduction Algorithms for Dual‐Energy Imaging

Purpose: While dual‐energy (DE) imaging provides increased nodule conspicuity in soft‐tissue images and greater calcification visualization in bone‐only images, DE image decomposition amplifies noise present in the projection data. This paper extends task‐based cascaded systems analysis (CSA) to include a variety of DE noise reduction algorithms, offering a general analytical approach to optimizing DE imaging performance. Method and Materials: Two noise reduction algorithms [simple‐smoothing of the high‐energy image (SSH) and anti‐correlated noise reduction (ACNR)] were incorporated into CSA models for DE imaging to describe the DE modulation transfer function(MTFDE), noise‐power spectrum (NPSDE), and noise equivalent quanta (NEQDE). The MTFDE and NPSDE were measured using standard edge‐spread function and flood‐field techniques adapted to DE imaging (with noise‐reduction processing) and compared to theoretical results. The MTFDE and NPSDE were combined to yield the NEQDE and integrated with a spatial‐frequency‐dependent task function to provide a detectability index for evaluation of imaging performance using standard, SSH, and ACNR image decompositions. Results: The MTFDE and NPSDE calculated using CSA agreed well with measurements. Detectability index provided an objective performance metric for identifying superior noise reduction algorithms under conditions of varying kVp, dose, and imaging task. For example, the DE detectability index for a delta‐function detection task in the soft‐tissue image was by far greatest for the ACNR algorithm, whereas SSH performed best for the bone‐only image. A gaussian detection task, on the other hand, indicated superior performance for the ACNR algorithm for both soft‐tissue and bone‐only images. Conclusions: Extension of CSA to include the influence of DE noise‐reduction algorithms such as SSH and ACNR offers a powerful guide to system optimization. The general, analytical approach provides an objective means of selecting superior noise‐reduction algorithms and “tuning” the parameters therein in a manner that weighs spatial resolution and noise in relation to the imaging task.