Single X-ray Image Research Articles

2D/3D registration based on biplanar X-ray and CT images for surgical navigation

Background and Objectives:Image-based 2D/3D registration is a crucial technology for fluoroscopy-guided surgical interventions. However, traditional registration methods relying on a single X-ray image into surgical navigation systems. This study proposes a novel 2D/3D registration approach utilizing biplanar X-ray images combined with computed tomography (CT) to significantly reduce registration and navigation errors. The method is successfully implemented in a surgical navigation system, enhancing its precision and reliability. Methods:First, we simultaneously register the frontal and lateral X-ray images with the CT image, enabling mutual complementation and more precise localization. Additionally, we introduce a novel similarity measure for image comparison, providing a more robust cost function for the optimization algorithm. Furthermore, a multi-resolution strategy is employed to enhance registration efficiency. Lastly, we propose a more accurate coordinate transformation method, based on projection and 3D reconstruction, to improve the precision of surgical navigation systems.Results: We conducted registration and navigation experiments using pelvic, spinal, and femur phantoms. The navigation results demonstrated that the feature registration errors (FREs) in the three experiments were 0.505±0.063 mm, 0.515±0.055 mm, and 0.577±0.056 mm, respectively. Compared to the point-to-point (PTP) registration method based on anatomical landmarks, our method reduced registration errors by 31.3%, 23.9%, and 26.3%, respectively. Conclusion:The results demonstrate that our method significantly reduces registration and navigation errors, highlighting its potential for application across various anatomical sites. Our code is available at: https://github.com/SJTUdemon/2D-3D-Registration

Measurement of the Acetabular Cup Orientation After Total Hip Arthroplasty Based on 3-Dimensional Reconstruction From a Single X-Ray Image Using Generative Adversarial Networks

BackgroundThe purpose of this study was to reconstruct 3-dimensional (3D) computed tomography (CT) images from single anteroposterior (AP) postoperative total hip arthroplasty (THA) X-ray images using a deep learning algorithm known as generative adversarial networks (GANs) and to validate the accuracy of cup angle measurement on GAN-generated CT. MethodsWe used 2 GAN-based models, CycleGAN and X2CT-GAN, to generate 3D CT images from X-ray images of 386 patients who underwent primary THAs using a cementless cup. The training dataset consisted of 522 CT images and 2,282 X-ray images. The image quality was validated using the peak signal-to-noise ratio and the structural similarity index measure. The cup anteversion and inclination measurements on the GAN-generated CT images were compared with the actual CT measurements. Statistical analyses of absolute measurement errors were performed using Mann–Whitney U tests and nonlinear regression analyses. ResultsThe study successfully achieved 3D reconstruction from single AP postoperative THA X-ray images using GANs, exhibiting excellent peak signal-to-noise ratio (37.40) and structural similarity index measure (0.74). The median absolute difference in radiographic anteversion was 3.45° and the median absolute difference in radiographic inclination was 3.25°, respectively. Absolute measurement errors tended to be larger in cases with cup malposition than in those with optimal cup orientation. ConclusionsThis study demonstrates the potential of GANs for 3D reconstruction from single AP postoperative THA X-ray images to evaluate cup orientation. Further investigation and refinement of this model are required to improve its performance.

High-rate, high-resolution single photon X-ray imaging: Medipix4, a large 4-side buttable pixel readout chip with high granularity and spectroscopic capabilities

The Medipix4 chip is the latest member in the Medipix/Timepix family of hybrid pixel detector chips aimed at high-rate spectroscopic X-ray imaging using high-Z materials. It can be tiled on all 4 sides making it ideal for constructing large-area detectors with minimal dead area. The chip is designed to read out a sensor of 320 × 320 pixels with dimensions of 75 μm× 75 μm or 160 × 160 pixels with dimensions of 150 μm× 150 μm. The readout architecture features energy binning of the single photons, which includes charge sharing correction for hits with energy spread over adjacent pixels. This paper presents the specifications, architecture, and circuit implementation of the chip, along with the first electrical measurements.

2D-3D Reconstruction of a Femur by Single X-Ray Image Based on Deep Transfer Learning Network

ObjectiveConstructing a 3D model from its 2D images, known as 2D-3D reconstruction, is a challenging task. Conventionally, a parametric 3D model such as a statistical shape model (SSM) is deformed by matching the shapes in its 2D images through a series of processes, including calibration, 2D-3D registration, and optimization for nonrigid deformation. To overcome this complicated procedure, a streamlined 2D-3D reconstruction using a single X-ray image is developed in this study. MethodsWe propose 2D-3D reconstruction of a femur by adopting a deep neural network, where the deformation parameters in the SSM determining the 3D shape of the femur are predicted from a single X-ray image using a deep transfer-learning network. For learning the network from distinct features representing the 3D shape information in the X-ray image, a specific proximal part of the femur from a unique X-ray pose that allows accurate prediction of the 3D femur shape is designated and used to train the network. Then, the corresponding proximal/distal 3D femur model is reconstructed from only the single X-ray image acquired at the designated position. ResultsExperiments were conducted using actual X-ray images of a femur phantom and X-ray images of a patient's femur derived from computed tomography to verify the proposed method. The average errors of the reconstructed 3D shape of the proximal and distal femurs from the proposed method were 1.20 mm and 1.08 mm in terms of root mean squared point-to-surface distance, respectively. ConclusionThe proposed method presents an innovative approach to simplifying the 2D-3D reconstruction using deep neural networks that exhibits performance compatible with the existing methodologies.

CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.

The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagnose multiple diseases that occurred in a single X-Ray image since multiple levels of features are generated in the images, and create different features as in single label detection. Various works were developed to address this challenge with proposed deep learning architectures to improve classification performance and enrich diagnosis results with multi-probability disease detection. The objective is to create an accurate result and a faster inference system to support a quick diagnosis in the medical system. To contribute to this state-of-the-art, we designed a fusion architecture, CheXNet and Feature Pyramid Network (FPN), to classify and discriminate multiple thoracic diseases from chest X-Rays. This concept enables the model to extract while creating a pyramid of feature maps with different spatial resolutions that capture low-level and high-level semantic information to encounter multiple features. The model's effectiveness is evaluated using the NIH ChestXray14 dataset, with the Area Under Curve (AUC) and accuracy metrics used to compare the results against other cutting-edge approaches. The overall results demonstrate that our method outperforms other approaches and has become promising for multilabel disease classification in chest X-Rays, with potential applications in clinical practice. The result demonstrated that we achieved an average AUC of 0.846 and an accuracy of 0.914. Further, our proposed architecture diagnoses images in 0.013s, faster than the latest approaches.

A Convolution Neural Network Design for Knee Osteoarthritis Diagnosis Using X-ray Images

Knee osteoarthritis (OA) is a chronic degenerative joint disease affecting millions worldwide, particularly those over 60. It is a significant cause of disability and can impact an individual's quality of life. The condition occurs when the cartilage in the knee joint wears away over time, leading to bone-on-bone contact, which can result in pain, stiffness, swelling, and decreased range of motion. Deep neural networks, especially convolutional neural networks (CNN), are powerful tools in medical applications such as diagnosis and detection. This research proposes a CNN model to classify knee osteoarthritis into five categories using x-ray images. These classes are labeled: Minimal, Healthy, Moderate, Doubtful, and Severe. Furthermore, the proposed CNN model has been compared with two pre-trained transfer learning models: Xception and InceptionResNet V2. These models were evaluated based on precision, recall, F1 score, and accuracy. The results showed that although all three models performed very well, the proposed model outperformed both transfer learning models with 98% accuracy. It also achieved the highest values for other parameters such as precision, recall, and F1 score. The proposed model has several potential applications in clinical practice, such as assisting doctors in accurately classifying knee osteoarthritis severity levels by analyzing single X-ray images.

AUTOMATED SEVERITY SCORING OF COVID-19 CT SEQUENCES USING SPACE-TIME TRANSFORMERS

Automatic diagnosis of Covid-19 lung complications from Computerized Tomography (CT) scans is an increasingly important research topic. In this rapidly developing area of Covid detection from medical image sequences, it is noted that most prior literature has focused on binary classification to detect diseased versus healthy cases from single X-ray or CT image. In this paper, we advance a step further by presenting a comprehensive framework for automated classification of the severity of lung infection (mild, moderate and severe) from CT sequences of confirmed Covid cases. We consider the sequence information for automation because in practice, the medical experts look at the CT sequence to score the severity of infection. We have collected a new lung CT sequence dataset at various stages of Covid infection from Indian patients. This dataset has been scored in terms of the severity of each lung lobe by experts in the field. We present a novel application of space-time transformers for CT sequences and achieve 93.3% accuracy for sequence level and 99% accuracy for patient-level, for multi- class classification of severity classes.

Semi-XctNet: Volumetric images reconstruction network from a single projection image via semi-supervised learning

Deep learning networks have achieved remarkable progress in various tasks of medical imaging. Most of the recent success in computer vision highly depend on large amounts of carefully annotated data, whereas labelling is arduous, time-consuming and in need of expertise. In this paper, a semi-supervised learning method, Semi-XctNet, is proposed for volumetric images reconstruction from a single X-ray image. In our framework, the effect of regularization on pixel-level prediction is enhanced by introducing a transformation consistent strategy into the model. Furthermore, a multi-stage training strategy is designed to ameliorate the generalization performance of the teacher network. An assistant module is also introduced to improve the pixel quality of pseudo-labels, thereby further improving the reconstruction accuracy of the semi-supervised model. The semi-supervised method proposed in this paper has been extensively validated on the LIDC-IDRI lung cancer detection public data set. Quantitative results show that SSIM (structural similarity measurement) and PSNR (peak signal noise ratio) are 0.8384 and 28.7344 respectively. Compared with the state-of-the-arts, Semi-XctNet exhibits excellent reconstruction performance, thus demonstrating the effectiveness of our method on the task of volumetric images reconstruction network from a single X-ray image.

Auto-outlier Fusion Technique for Chest X-ray Classification with Multi-head Attention Mechanism

A chest X-ray is one of the most widely available radiological examinations for diagnosing and detecting various lung illnesses. The National Institutes of Health (NIH) provides an extensive database, ChestX-ray8 and ChestXray14, to help establish a deep learning community for analysing and predicting lung diseases. ChestX-ray14 consists of 112,120 frontal-view X-ray images of 30,805 distinct patients with text-mined fourteen disease image labels, where each image has multiple labels and has been utilised in numerous research in the past. To our current knowledge, no previous study has investigated outliers and multi-label impact for a single X-ray image during the preprocessing stage. The effect of outliers is mitigated in this paper by our proposed auto-outlier fusion technique. The image label is regenerated by concentrating on a particular factor in one image. The final cleaned dataset will be used to compare the mechanisms of multi-head self-attention and multi-head attention with generalised max-pooling.

Auto-outlier Fusion Technique for Chest X-ray Classification with Multi-head Attention Mechanism

A chest X-ray is one of the most widely available radiological examinations for diagnosing and detecting various lung illnesses. The National Institutes of Health (NIH) provides an extensive database, ChestX-ray8 and ChestXray14, to help establish a deep learning community for analysing and predicting lung diseases. ChestX-ray14 consists of 112,120 frontal-view X-ray images of 30,805 distinct patients with text-mined fourteen disease image labels, where each image has multiple labels and has been utilised in numerous research in the past. To our current knowledge, no previous study has investigated outliers and multi-label impact for a single X-ray image during the preprocessing stage. The effect of outliers is mitigated in this paper by our proposed auto-outlier fusion technique. The image label is regenerated by concentrating on a particular factor in one image. The final cleaned dataset will be used to compare the mechanisms of multi-head self-attention and multi-head attention with generalised max-pooling.

Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering

BackgroundTo obtain phase-contrast X-ray images, single-grid imaging systems are effective, but Moire artifacts remain a significant issue. The solution for removing Moire artifacts from an image is grid rotation, which can distinguish between these artifacts and sample information within the Fourier space. However, the mechanical movement of grid rotation is slower than the real-time change in Moire artifacts. Thus, Moire artifacts generated during real-time imaging cannot be removed using grid rotation. To overcome this problem, we propose an effective method to obtain phase-contrast X-ray images using instantaneous frequency and noise filtering.ResultThe proposed phase-contrast X-ray image using instantaneous frequency and noise filtering effectively suppressed noise with Moire patterns. The proposed method also preserved the clear edge of the inner and outer boundaries and internal anatomical information from the biological sample, outperforming conventional Fourier analysis-based methods, including absorption, scattering, and phase-contrast X-ray images. In particular, when comparing the phase information for the proposed method with the x-axis gradient image from the absorption image, the proposed method correctly distinguished two different types of soft tissue and the detailed information, while the latter method did not.ConclusionThis study successfully achieved a significant improvement in image quality for phase-contrast X-ray images using instantaneous frequency and noise filtering. This study can provide a foundation for real-time bio-imaging research using three-dimensional computed tomography.

Federated learning based Covid-19 detection.

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Progress in in situ x-ray imaging of welding process.

Welding has been widely used in industry for hundreds of years, and pursuing higher weld quality requires a better understanding of the welding process. The x-ray imaging technique is a powerful tool to in situ observe the inner characteristics of the melt pool in the welding process. Here, current progress in in situ x-ray imaging of the welding process is concluded, including the experiments based on the laboratory-based single x-ray imaging system, the laboratory-based double x-ray imaging system, and the synchrotron radiation tomography system. The corresponding experimental results with the in situ x-ray imaging technique about the formation and evolution of the keyhole, melt pool, pore, solidification crack, etc., have been introduced. A new understanding of welding based on the current progress in in situ x-ray imaging of additive manufacturing is concluded. In addition, the future development trend of applying x-ray imaging technology in the field of monitoring the welding process is proposed.

Method of quantitative assessment of the shape of the lumbar-thoracic spine

Introduction. The presence of deformation of the lumbar-thoracic spine in the sagittal plane is the main factor determining the health status of adult patients. The studies of the features of human posture with or without spinal deformities in statics and movement have been in the focus of clinician attention for a long time. Recent studies offer a unifi ed approach to assessing the position of the vertebrae. However, no such studies have been conducted to analyze the lumbar-thoracic junction. The purpose of the work — to develop a method of qualitative and quantitative assessment of the vertebra positions in lumbar-thoracic junction. Issues: to develop a schematic model of the lumbar-thoracic spine; to develop a typology of the lumbar-thoracic transition; to develop an objective indicator refl ecting the features of the lumbar-thoracic transition in the patient; to characterize the age characteristics of this area of the spine.Materials and methods. A study of digital radiographs for all spine parts in sagittal projection for 141 patients with dorsopathies, 57 men and 84 women aged from 21 to 88 years, was conducted. The study was performed on a personal computer screen, without patient participation. A single digital X-ray image of the spine in the sagittal projection was obtained for each patient. The occipital vertical and anteroposterior axes of TIX–LV (LVI) vertebrae (r axes) were applied to the combined radiograph. At the intersection points of the axes with the occipital vertical, the perpendiculars to the r axes were restored, and the angles between the perpendiculars and the occipital vertical (angles r) were measured. Statistical analysis was carried out using the Microsoft Offi ce Excel 2007 software package.Results. Schematic models of the lumbar-thoracic junction for all cases were constructed on the basis of the data obtained. The models were used to compare the vertebra positions and describe three form types of lumbar-thoracic junction: normal, straightened and reinforced. An aggregated ArTL indicator is proposed and the boundaries of this indicator were determined for the quantitative assessment of each case. It is demonstrated that the age features for this part of the spine are expressed not in a monotonous change in the average value of ArTL with age, but in an increase in the proportion of patients with straightened and enhanced kyphosis, and it is especially noticeable in the group of people over 75 years old.Conclusion. The proposed technology for assessing the position of the vertebrae of the lumbar-thoracic spine was developed to satisfy the needs of osteopaths and specialists in restorative medicine, and this technology is presented for the fi rst time. In the course of the study, schematic models of the spine of each patient were developed; an ArTL indicator was proposed to quantify the type of the lumbar-thoracic region shape. The boundaries for the diagnosis of each type were determined and a study was conducted to identify the age trend. The study revealed the absence of a linear age trend of changes in this part of the spine. Among people over 75 years of age, patients with straightened or enhanced kyphosis of this zone were more common.

XctNet: Reconstruction network of volumetric images from a single X-ray image.

Conventional Computed Tomography (CT) produces volumetric images by computing inverse Radon transformation using X-ray projections from different angles, which results in high dose radiation, long reconstruction time and artifacts. Biologically, prior knowledge or experience can be utilized to identify volumetric information from 2D images to certain extents. a deep learning network, XctNet, is proposed to gain this prior knowledge from 2D pixels and produce volumetric data. In the proposed framework, self-attention mechanism is used for feature adaptive optimization; multiscale feature fusion is used to further improve the reconstruction accuracy; a 3D branch generation module is proposed to generate the details of different generation fields. Comparisons are made with the state-of-arts methods using public dataset and XctNet shows significantly higher image quality as well as better accuracy (SSIM and PSNR values of XctNet are 0.8681 and 29.2823 respectively).

Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists

The front-line imaging modalities computed tomography (CT) and X-ray play important roles for triaging COVID patients. Thoracic CT has been accepted to have higher sensitivity than a chest X-ray for COVID diagnosis. Considering the limited access to resources (both hardware and trained personnel) and issues related to decontamination, CT may not be ideal for triaging suspected subjects. Artificial intelligence (AI) assisted X-ray based application for triaging and monitoring require experienced radiologists to identify COVID patients in a timely manner with the additional ability to delineate and quantify the disease region is seen as a promising solution for widespread clinical use. Our proposed solution differs from existing solutions presented by industry and academic communities. We demonstrate a functional AI model to triage by classifying and segmenting a single chest X-ray image, while the AI model is trained using both X-ray and CT data. We report on how such a multi-modal training process improves the solution compared to single modality (X-ray only) training. The multi-modal solution increases the AUC (area under the receiver operating characteristic curve) from 0.89 to 0.93 for a binary classification between COVID-19 and non-COVID-19 cases. It also positively impacts the Dice coefficient (0.59 to 0.62) for localizing the COVID-19 pathology. To compare the performance of experienced readers to the AI model, a reader study is also conducted. The AI model showed good consistency with respect to radiologists. The DICE score between two radiologists on the COVID group was 0.53 while the AI had a DICE value of 0.52 and 0.55 when compared to the segmentation done by the two radiologists separately. From a classification perspective, the AUCs of two readers was 0.87 and 0.81 while the AUC of the AI is 0.93 based on the reader study dataset. We also conducted a generalization study by comparing our method to the-state-art methods on independent datasets. The results show better performance from the proposed method. Leveraging multi-modal information for the development benefits the single-modal inferencing.

Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network

The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This paper proposes a novel approach for the automated detection of COVID19 using chest X-ray images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is decomposed into seven modes. The evaluated modes are used as input to the multiscale deep Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia, and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images from two different publicly available databases, where database A consists of 1225 images and database B consists of 9000 images. The results show that the proposed approach has obtained a maximum accuracy of 96% and 100% for the multiclass and binary classification schemes using X-ray images from dataset A with 5-fold cross-validation (CV) strategy. For dataset B, the accuracy values of 97.17% and 96.06% are achieved using multiscale deep CNN for multiclass and binary classification schemes with 5-fold CV. The proposed multiscale deep learning model has demonstrated a higher classification performance than the existing approaches for detecting COVID19 using X-ray images.

Sizes of pure and doped helium droplets from single shot x-ray imaging.

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5μm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9K. This work extends the measurement of large helium nanodroplets containing 109-1011 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.

A Comprehensive Exploration of Neural Networks for Forensic Analysis of Adult Single Tooth X-Ray Images

Determining the demographic characteristics of a person post-mortem is a fundamental task for forensic experts, and the dental system is a crucial source of those information. Those characteristics, namely age and sex, can reliably be determined. The mandible and individual teeth survive even the harshest conditions, making them a prime target for forensic analysis. Current methods in forensic odontology rely on time-consuming manual measurements and reference tables, many of which rely on the correct determination of the tooth type. This study thoroughly explores the applicability of deep learning for sex assessment, age estimation, and tooth type determination from x-ray images of individual teeth. A series of models that use state-of-the-art feature extraction architectures and attention have been trained and evaluated. Their hyperparameters have been explored and optimized using a combination of grid and random search, totaling over a thousand experiments and 14076 hours of GPU compute time. Our dataset contains 86495 individual tooth x-ray image samples, with a subset of 7630 images having additional information about tooth alterations. The best-performing models are fine-tuned, the impact of tooth alterations is analyzed, and model performance is compared to current methods in forensic odontology literature. We achieve an accuracy of 76.41% for sex assessment, a median absolute error of 4.94 years for age estimation, and an accuracy of 87.24% to 99.15% for tooth type determination. The constructed models are fully automated and fast, their results are reproducible, and the performance is equal to or better than current state-of-the-art methods in forensic odontology.

Spatial frequency-dependent pulse-heightspectrum and method for analyzing detector DQE(f) from ensembles of single X-rayimages.

Scintillators and photoconductors used in energy integrating detectors (EIDs) have inherent variations in their imaging response to single-detected X-rays due to variations in X-ray energy deposition and secondary quanta generation and transport, which degrades DQE(f). The imaging response of X-ray scintillators to single X-rays may be recorded and studied using single X-ray imaging (SXI) experiments; however, no method currently exists for relating SXI experimental results to EID DQE(f). This work proposes a general analytical framework for computing and analyzing the DQE(f) performance of EIDs from single X-ray image ensembles using a spatial frequency-dependent pulse-height spectrum. A spatial frequency (f)-dependent gain, , is defined as the Fourier transform of the imaging response of an EID to a single-detected X-ray. A f-dependent pulse-height spectrum, , is defined as the 2D probability density function of over the complex plane. is used to define a f-dependent Swank factor, AS (f), which fully characterizes the DQE(f) degradation due to single X-ray noise. AS (f) is analyzed in terms of its degradation due to Swank noise, variations in the frequency-dependent attenuation of , and noise in which occurs due to variations in the asymmetry in each single X-ray's imaging response. Three example imaging systems are simulated to demonstrate the impact of depth-dependent variation in , remote energy deposition, and a finite number of secondary quanta, on , AS (f), MTF(f), and NPS(f)/NPS(0), which are computed from ensembles of single X-ray images. The same is also demonstrated by simulating a realistic imaging system; that is, a Gd2 O2 S-based EID. Using the latter imaging system, the convergence of AS (f) estimates is investigated as a function of the number of detected X-rays per ensemble. Depth-dependent variation resulted in AS (f) degradation exclusively due to depth-dependent optical Swank noise and the Lubberts effect. Conversely, the majority of AS (f) degradation caused by remote energy deposition and finite secondary quanta occurred due to variations in . When using input X-ray energies below the K-edge of Gd, variations in the frequency-dependent attenuation of accounted for the majority of AS (f) degradation in the GOS-based EID, and very little Swank noise and variations in were observed. Above the K-edge, however, AS (f) degradation due to Swank noise and variations in greatly increased. The convergence of AS (f) was limited by variation in ; imaging systems with more variation in required more detected X-rays per ensemble. An analytical framework is proposed that generalizes the pulse-height spectrum and Swank factor to arbitrary f. The impact of single X-ray noise sources, such as the Lubberts effect, remote energy deposition, and finite secondary quanta on detector performance, may be represented using , and quantified using AS (f). The approach may be used to compute MTF(f), NPS(f), and DQE(f) from ensembles of single X-ray images and provides an additional tool to analyze proposed EID designs.