KEYWORDS: Digital breast tomosynthesis, Mammography, Breast, X-rays, Modulation transfer functions, Imaging systems, Tomosynthesis, Spatial resolution, Breast cancer
Digital breast tomosynthesis (DBT) enables significantly higher cancer detection rates compared to full-field digital mammography (FFDM) without compromising the recall rate. However, regarding microcalcification assessment established tomosynthesis system concepts still tend to be inferior to FFDM. To further boost the clinical role of DBT in breast cancer screening and diagnosis, a system concept was developed that enables fast wide-angle DBT with the unique in-plane resolution capabilities known from FFDM. The concept comprises a novel x-ray tube concept that incorporates an adaptive focal spot position, fast flat-panel detector technology, and innovative algorithmic concepts for image reconstruction. We have built a DBT system that provides tomosynthesis image stacks and synthetic mammograms from 50° tomosynthesis scans realized in less than five seconds. In this contribution, we motivate the design of the system concept, present a physics characterization of its imaging performance, and outline the algorithmic concepts used for image processing. We conclude with illustrating the potential clinical impact by means of clinical case examples from first evaluations in Europe.
Wide–angle digital breast tomosynthesis (DBT) is well known to offer benefits in mass perceptibility compared to narrow–angle DBT due to reduced anatomical overlap. Regarding the perceptibility of micro–calcifications the situation is somehow inverted. On the one hand this can be related to effects during data acquisition and their impact on the system MTF. On the other hand there is a wider spread of calcifications in depth direction in narrow–angle DBT, which distributes calcifications over more slices. This is equivalent to an inherent thicker slice for high spatial frequencies. In this work we want to assume an equivalent quality of raw data and only focus on the effects of different acquisition angles in the reconstruction. We propose an algorithm which creates so–called hybrid thick DBT slices and optimizes the visualization of calcifications while preserving the high mass perceptibility of thin wide–angle DBT slices. The algorithm is purely based on filtered backprojection (FBP) and can be implemented in an efficient manner. For validation simulation studies using the VICTRE (FDA) pipeline are performed. Our results indicate that hybrid thick–slices in wide-angle DBT enable to successfully solve the contrarian imaging tasks of high mass and high calcification perception within one imaging setup.
Denoising algorithms are sensitive to the noise level and noise power spectrum of the input image and their ability to adapt to this. In the worst-case, image structures can be accidentally removed or even added. This holds up for analytical image filters but even more for deep learning-based denoising algorithms due to their high parameter space and their data-driven nature. We propose to use the knowledge about the noise distribution of the image at hand to limit the influence and ability of denoising algorithms to a known and plausible range. Specifically, we can use the physical knowledge of X-ray radiography by considering the Poisson noise distribution and the noise power spectrum of the detector. Through this approach, we can limit the change of the acquired signal by the denoising algorithm to the expected noise range, and therefore prevent the removal or hallucination of small relevant structures. The presented method allows to use denoising algorithms and especially deep learning-based methods in a controlled and safe fashion in medical x-ray imaging.
Recently introduced multi-layer flat panel detectors (FPDs) enable single acquisition spectral radiography. We perform an in-depth simulation study to investigate different decomposition algorithms under the influence of adipose tissue and scattered radiation using physics-based material decomposition algorithms for the task of bone removal. We examine a matrix-based material decomposition (MBMD) under assumption of monoenergetic X-ray spectra (equivalent to weighted logarithmic subtraction (WLS)), a matrix-based material decomposition with polynomial beam hardening pre-correction (MBMD-PBC) and a projection domain decomposition (PDD). The simulated setup corresponds to an intensive care unit (ICU) anterior posterior (AP) bedside chest examination (contact scan). The limitations of the three algorithms are evaluated using a high-fidelity X-ray simulator with five phantom realizations that differ in terms of added adipose tissue. For each simulated phantom realization, different amounts of scatter correction are considered, ranging from no correction at all to an ideal scatter correction. Unless quantitative imaging is required, the three algorithms are capable of removing bone structures when adipose tissue is present. Bone removal using a multi-layer FPDs in an ICU setup is feasible. However, uncorrected scatter can lead to bone structures becoming visible in the soft tissue image. This indicates the need for accurate scatter estimation and correction algorithms, especially when using quantitative algorithms such as PDD.
KEYWORDS: Denoising, Breast, Education and training, Digital breast tomosynthesis, Tomosynthesis, Computer simulations, Deep learning, X-rays, Breast density, Photons
PurposeHigh noise levels due to low X-ray dose are a challenge in digital breast tomosynthesis (DBT) reconstruction. Deep learning algorithms show promise in reducing this noise. However, these algorithms can be complex and biased toward certain patient groups if the training data are not representative. It is important to thoroughly evaluate deep learning-based denoising algorithms before they are applied in the medical field to ensure their effectiveness and fairness. In this work, we present a deep learning-based denoising algorithm and examine potential biases with respect to breast density, thickness, and noise level.ApproachWe use physics-driven data augmentation to generate low-dose images from full field digital mammography and train an encoder-decoder network. The rectified linear unit (ReLU)-loss, specifically designed for mammographic denoising, is utilized as the objective function. To evaluate our algorithm for potential biases, we tested it on both clinical and simulated data generated with the virtual imaging clinical trial for regulatory evaluation pipeline. Simulated data allowed us to generate X-ray dose distributions not present in clinical data, enabling us to separate the influence of breast types and X-ray dose on the denoising performance.ResultsOur results show that the denoising performance is proportional to the noise level. We found a bias toward certain breast groups on simulated data; however, on clinical data, our algorithm denoises different breast types equally well with respect to structural similarity index.ConclusionsWe propose a robust deep learning-based denoising algorithm that reduces DBT projection noise levels and subject it to an extensive test that provides information about its strengths and weaknesses.
KEYWORDS: Medical image reconstruction, Bone, X-ray computed tomography, Sensors, X-rays, Medical imaging, Aluminum, Physics, Photons, Signal attenuation, Monte Carlo methods
We investigate the feasibility of bone marrow edema (BME) detection using a kV-switching Dual-Energy (DE) Cone-Beam CT (CBCT) protocol. This task is challenging due to unmatched X-ray paths in the low-energy (LE) and high-energy (HE) spectral channels, CBCT non-idealities such as X-ray scatter, and narrow spectral separation between fat (bone marrow) and water (BME). We propose a comprehensive DE decomposition framework consisting of projection interpolation onto matching LE and HE view angles, fast Monte Carlo scatter correction with low number of tracked photons and Gaussian denoising, and two-stage three-material decompositions involving two-material (fat-Aluminum) Projection-Domain Decomposition (PDD) followed by image-domain three-material (fat-water-bone) base-change. Performance in BME detection was evaluated in simulations and experiments emulating a kV-switching CBCT wrist imaging protocol on a robotic x-ray system with 60 kV LE beam, 120 kV HE beam, and 0.5° angular shift between the LE and HE views. Cubic B-spline interpolation was found to be adequate to resample HE and LE projections of a wrist onto common view angles required by PDD. The DE decomposition maintained acceptable BME detection specificity (⪅0.2 mL erroneously detected BME volume compared to 0.85 mL true BME volume) over +/-10% range of scatter magnitude errors, as long as the scatter shape was estimated without major distortions. Physical test bench experiments demonstrated successful discrimination of ~20% change in fat concentrations in trabecular bone-mimicking solutions of varying water and fat content.
Purpose: We investigated the feasibility of detection and quantification of bone marrow edema (BME) using dual-energy (DE) Cone-Beam CT (CBCT) with a dual-layer flat panel detector (FPD) and three-material decomposition. Methods: A realistic CBCT system simulator was applied to study the impact of detector quantization, scatter, and spectral calibration errors on the accuracy of fat-water-bone decompositions of dual-layer projections. The CBCT system featured 975 mm source-axis distance, 1,362 mm source-detector distance and a 430 × 430 mm2 dual-layer FPD (top layer: 0.20 mm CsI:Tl, bottom layer: 0.55 mm CsI:Tl; a 1 mm Cu filter between the layers to improve spectral separation). Tube settings were 120 kV (+2 mm Al, +0.2 mm Cu) and 10 mAs per exposure. The digital phantom consisted of a 160 mm water cylinder with inserts containing mixtures of water (volume fraction ranging 0.18 to 0.46) - fat (0.5 to 0.7) - Ca (0.04 to 0.12); decreasing fractions of fat indicated increasing degrees of BME. A two-stage three-material DE decomposition was applied to DE CBCT projections: first, projection-domain decomposition (PDD) into fat-aluminum basis, followed by CBCT reconstruction of intermediate base images, followed by image-domain change of basis into fat, water and bone. Sensitivity to scatter was evaluated by i) adjusting source collimation (12 to 400 mm width) and ii) subtracting various fractions of the true scatter from the projections at 400 mm collimation. The impact of spectral calibration was studied by shifting the effective beam energy (± 2 keV) when creating the PDD lookup table. We further simulated a realistic BME imaging framework, where the scatter was estimated using a fast Monte Carlo (MC) simulation from a preliminary decomposition of the object; the object was a realistic wrist phantom with an 0.85 mL BME stimulus in the radius. Results: The decomposition is sensitive to scatter: approx. <20 mm collimation width or <10% error of scatter correction in a full field-of-view setting is needed to resolve BME. A mismatch in PDD decomposition calibration of ± 1 keV results in ~25% error in fat fraction estimates. In the wrist phantom study with MC scatter corrections, we were able to achieve ~0.79 mL true positive and ~0.06 mL false positive BME detection (compared to 0.85 mL true BME volume). Conclusions: Detection of BME using DE CBCT with dual-layer FPD is feasible, but requires scatter mitigation, accurate scatter estimation, and robust spectral calibration.
KEYWORDS: Denoising, X-rays, Digital breast tomosynthesis, X-ray imaging, Photons, Mammography, Sensors, Physics, Signal to noise ratio, Interference (communication)
Digital Breast Tomosynthesis (DBT) is becoming increasingly popular for breast cancer screening because of its high depth resolution. It uses a set of low-dose x-ray images called raw projections to reconstruct an arbitrary number of planes. These are typically used in further processing steps like backprojection to generate DBT slices or synthetic mammography images. Because of their low x-ray dose, a high amount of noise is present in the projections. In this study, the possibility of using deep learning for the removal of noise in raw projections is investigated. The impact of loss functions on the detail preservation is analized in particular. For that purpose, training data is augmented following the physics driven approach of Eckert et al.1 In this method, an x-ray dose reduction is simulated. First pixel intensities are converted to the number of photons at the detector. Secondly, Poisson noise is enhanced in the x-ray image by simulating a decrease in the mean photon arrival rate. The Anscombe Transformation2 is then applied to construct signal independent white Gaussian noise. The augmented data is then used to train a neural network to estimate the noise. For training several loss functions are considered including the mean square error (MSE), the structural similarity index (SSIM)3 and the perceptual loss.4 Furthermore the ReLU-Loss1 is investigated, which is especially designed for mammogram denoising and prevents the network from noise overestimation. The denoising performance is then compared with respect to the preservation of small microcalcifications. Based on our current measurements, we demonstrate that the ReLU-Loss in combination with SSIM improves the denoising results.
We investigate an image-based strategy to compensate for cardiac motion-induced artifacts in Digital Chest Tomosynthesis (DCT). We apply the compensation to conventional unidirectional vertical “↕” scan DCT and to a multidirectional circular trajectory "O" providing improved depth resolution. Propagation of heart motion into the lungs was simulated as a dynamic deformation. The studies investigated a range of motion propagation distances and scan times. Projection-domain retrospective gating was used to detect heart phases. Sparsely sampled reconstructions of each phase were deformably aligned to yield a motion compensated image with reduced sampling artifacts. The proposed motion compensation mitigates artifacts and blurring in DCT images both for “↕” and "O" scan trajectories. Overall, the “O” orbit achieved the same or better nodule structural similarity index in than the conventional “↕” orbit. Increasing the scan time improved the sampling of individual phase reconstructions.
Purpose: We compare the effects of scatter on the accuracy of areal bone mineral density (BMD) measurements obtained using two flat-panel detector (FPD) dual-energy (DE) imaging configurations: a dual-kV acquisition and a dual-layer detector. Methods: Simulations of DE projection imaging were performed with realistic models of x-ray spectra, scatter, and detector response for dual-kV and dual-layer configurations. A digital body phantom with 4 cm Ca inserts in place of vertebrae (concentrations 50 - 400 mg/mL) was used. The dual-kV configuration involved an 80 kV low-energy (LE) and a 120 kV high-energy (HE) beam and a single-layer, 43x43 cm FPD with a 650 μm cesium iodide (CsI) scintillator. The dual-layer configuration involved a 120 kV beam and an FPD consisting of a 200 μm CsI layer (LE data), followed by a 1 mm Cu filter, and a 550 μm CsI layer (HE data). We investigated the effects of an anti-scatter grid (13:1 ratio) and scatter correction. For the correction, the sensitivity to scatter estimation error (varied ±10% of true scatter distribution) was evaluated. Areal BMD was estimated from projection-domain DE decomposition. Results: In the gridless dual-kV setup, the scatter-to-primary ratio (SPR) was similar for the LE and HE projections, whereas in the gridless dual layer setup, the SPR was ~26% higher in the LE channel (top CsI layer) than in the HE channel (bottom layer). Because of the resulting bias in LE measurements, the conventional projection-domain DE decomposition could not be directly applied to dual-layer data; this challenge persisted even in the presence of a grid. In contrast, DE decomposition of dual-kV data was possible both without and with the grid; the BMD error of the 400 mg/mL insert was -0.4 g/cm2 without the grid and +0.3 g/cm2 with the grid. The dual-layer FPD configuration required accurate scatter correction for DE decomposition: a -5% scatter estimation error resulted in -0.1 g/cm2 BMD error for the 50 mg/mL insert and a -0.5 g/cm2 BMD error for the 400 mg/mL with a grid, compared to <0.1 g/cm2 for all inserts in a dual-kV setup with the same scatter estimation error. Conclusion: This comparative study of quantitative performance of dual-layer and dual-kV FPD-based DE imaging indicates the need for accurate scatter correction in the dual-layer setup due to increased susceptibility to scatter errors in the LE channel.
Purpose: We investigate the feasibility of slot-scan dual-energy x-ray absorptiometry (DXA) on robotic x-ray platforms capable of synchronized source and detector translation. This novel approach will enhance the capabilities of such platforms to include quantitative assessment of bone quality using areal bone mineral density (aBMD), normally obtained only with a dedicated DXA scanner. Methods: We performed simulation studies of a robotized x-ray platform that enables fast linear translation of the x-ray source and flat-panel detector (FPD) to execute slot-scan dual-energy (DE) imaging of the entire spine. Two consecutive translations are performed to acquire the low-energy (LE, 80 kVp) and high-energy (HE, 120 kVp) data in <15 sec total time. The slot views are corrected with convolution-based scatter estimation and backprojected to yield tiled long-length LE and HE radiographs. Projection-based DE decomposition is applied to the tiled radiographs to yield (i) aBMD measurements in bone, and (ii) adipose content measurement in bone-free regions. The feasibility of achieving accurate aBMD estimates was assessed using a high-fidelity simulation framework with a digital body phantom and a realistic bone model covering a clinically relevant range of mineral densities. Experiments examined the effects of slot size (1 – 20 cm), scatter correction, and patient size/adipose content (waist circumference: 77 – 95 cm) on the accuracy and reproducibility of aBMD. Results: The proposed combination of backprojection-based tiling of the slot views and DE decomposition yielded bone density maps of the spine that were free of any apparent distortions. The x-ray scatter increased with slot width, leading to aBMD errors ranging from 0.2 g/cm2 for a 5 cm slot to 0.7 g/cm2 for a 20 cm slot when no scatter correction was applied. The convolution-based correction reduced the aBMD error to within 0.02 g/cm2 for slot widths <10 cm. Reproducible aBMD measurements across a range of body sizes (aBMD variability <0.1 g/cm2) were achieved by applying a calibration based on DE adipose thickness estimates from peripheral body sites. Conclusion: The feasibility of accurate and reproducible aBMD measurements on an FPD-based x-ray platform was demonstrated using DE slot scan trajectories, backprojection-domain decomposition, scatter correction, and adipose precorrection.
Purpose: We investigate cone-beam CT (CBCT) imaging protocols and scan orbits for 3D cervical spine imaging on a twin-robotic x-ray imaging system (Multitom Rax). Tilted circular scan orbits are studied to assess potential benefits in visualization of lower cervical vertebrae, in particular in low-dose imaging scenarios. Methods: The Multitom Rax system enables flexible scan orbit design by using two robotic arms to independently move the x-ray source and detector. We investigated horizontal and tilted circular scan orbits (up to 45° tilt) for 3D imaging of the cervical spine. The studies were performed using an advanced CBCT simulation framework involving GPU accelerated x-ray scatter estimation and accurate modeling of x-ray source, detector and noise. For each orbit, the x-ray scatter and scatter-to-primary ratio (SPR) were evaluated; cervical spine image quality was characterized by analyzing the contrast-to-noise ratio (CNR) for each vertebrae. Performance evaluation was performed for a range of scan exposures (263 mAs/scan – 2.63 mAs/scan) and standard and dedicated low dose reconstruction protocols. Results: The tilted orbit reduces scatter and increases primary detector signal for lower cervical vertebrae because it avoids ray paths crossing through both shoulders. Orbit tilt angle of 35° was found to achieve a balanced performance in visualization of upper and lower cervical spine. Compared with a flat orbit, using the optimized 35° tilted orbit reduces lateral projection SPR at the C7 vertebra by <40%, and increases CNR by 220% for C6 and 76% for C7. Adequate visualization of the vertebrae with CNR <1 was achieved for scan exposures as low as 13.2 mAs / scan, corresponding to ~3 mGy absorbed spine dose. Conclusion: Optimized tilted scan orbits are advantageous for CBCT imaging of the cervical spine. The simulation studies presented here indicate that CBCT image quality sufficient for evaluation of spine alignment and intervertebral joint spaces might be achievable at spine doses below 5 mGy.
Mammographic breast density is an important risk marker in breast cancer screening. The ACR BI-RADS guidelines (5th ed.) define four breast density categories that can be dichotomized by the two super-classes dense" and not dense". Due to the qualitative description of the categories, density assessment by radiologists is characterized by a high inter-observer variability. To quantify this variability, we compute the overall percentage agreement (OPA) and Cohen's kappa of 32 radiologists to the panel majority vote based on the two super-classes. Further, we analyze the OPA between individual radiologists and compare the performances to an automated assessment via a convolutional neural network (CNN). The data used for evaluation contains 600 breast cancer screening examinations with four views each. The CNN was designed to take all views of an examination as input and trained on a dataset with 7186 cases to output one of the two super-classes. The highest agreement to the panel majority vote (PMV) achieved by a single radiologist is 99%, the lowest score is 71% with a mean of 89%. The OPA of two individual radiologists ranges from a maximum of 97.5% to a minimum of 50.5% with a mean of 83%. Cohen's kappa values of radiologists to the PMV range from 0.97 to 0.47 with a mean of 0.77. The presented algorithm reaches an OPA to all 32 radiologists of 88% and a kappa of 0.75. Our results show that inter-observer variability for breast density assessment is high even if the problem is reduced to two categories and that our convolutional neural network can provide labelling comparable to an average radiologist. We also discuss how to deal with automated classification methods for subjective tasks.
Measurements of skeletal geometries are a crucial tool for the assessment of pathologies in orthopedics. Usually, those measurements are performed in conventional 2-D X-ray images. Due to the cone-beam geometry of most commercially available X-ray systems, effects like magnification and distortion are inevitable and may impede the precision of the orthopedic measurements. In particular measurements of angles, axes, and lengths in spine or limb acquisitions would benefit from a true 1-to-1 mapping without any distortion or magnification.
In this work, we developed a model to quantify these effects for realistic patient sizes and clinically relevant acquisition procedures. Moreover, we compared the current state-of-the-art technique for the imaging of length- extended radiographs, e. g. for spine or leg acquisitions (i. e. the source-tilt technique) with a slot-scanning method. To validate our model we conducted several experiments with physical as well as anthropomorphic phantoms, which turned out to be in good agreement with our model. To this end, we found, that the images acquired with the reconstruction-based slot-scanning technique comprise no magnification or distortion. This would allow precise measurements directly on images without considering calibration objects, which might be beneficial for the quality and workflow efficiency of orthopedic applications.
The acquisition time of cone-beam CT (CBCT) systems is limited by different technical constraints. One important factor is the mechanical stability of the system components, especially when using C-arm or robotic systems. This leads to the fact that today’s acquisition protocols are performed at a system speed, where geometrical reproducibility can be guaranteed. However, from an application point of view faster acquisition times are useful since the time for breath-holding or being restraint in a static position has direct impact on patient comfort and image quality. Moreover, for certain applications, like imaging of extremities, a higher resolution might offer additional diagnostic value. In this work, we show that it is possible to intentionally exceed the conventional acquisition limits by accepting geometrical inaccuracies. To compensate deviations from the assumed scanning trajectory, a marker-free auto-focus method based on the gray-level histogram entropy was developed and evaluated. First experiments on a modified twin-robotic X-ray system (Multitom Rax, Siemens Healthcare GmbH, Erlangen, Germany) show that the acquisition time could be reduced from 14 s down to 9 s, while maintaining the same high-level image quality. In addition to that, by using optimized acquisition protocols, ultra-high-resolution imaging techniques become accessible.
Purpose: We optimize scan orbits and acquisition protocols for 3D imaging of the weight-bearing spine on a twin-robotic x-ray system (Multitom Rax). An advanced Cone-Beam CT (CBCT) simulation framework is used for systematic optimization and evaluation of protocols in terms of scatter, noise, imaging dose, and task-based performance in 3D image reconstructions. Methods: The x-ray system uses two robotic arms to move an x-ray source and a 43×43 cm2 flat-panel detector around an upright patient. We investigate two classes of candidate scan orbits, both with the same source-axis distance of 690 mm: circular scans with variable axis-detector distance (ADD, air gap) ranging from 400 to 800 mm, and elliptical scans, where the ADD smoothly changes between the anterior-posterior view (ADDAP) and the lateral view (ADDLAT). The study involved elliptical orbits with fixed ADDAP of 400 mm and variable ADDLAT, ranging 400 to 800 mm. Scans of human lumbar spine were simulated using a framework that included accelerated Monte Carlo scatter estimation and realistic models of the x-ray source and detector. In the current work, x-ray fluence was held constant across all imaging configurations, corresponding to 0.5 mAs/frame. Performance of circular and elliptical orbits was compared in terms of scatter and scatter-to-primary ratio (SPR) in projections, and contrast, noise, contrast-to-noise ratio (CNR), and truncation (field of view, FOV) in 3D image reconstructions. Results: The highest mean SPR was found in lateral views, ranging from ~5 at ADD of 300 mm to ~1.2 at ADD of 800 mm. Elliptical scans enabled image acquisition with reduced lateral SPR and almost constant SPR across projection angles. The improvement in contrast across the investigated range of air gaps (due to reduction in scatter) was ~2.3x for circular orbits and ~1.9x for elliptical orbits. The increase in noise associated with increased ADD was more pronounced for circular scans (~2x) compared to elliptical scans (~1.5x). The circular orbit with the best CNR performance (ADD=600 mm) yielded ~10% better CNR than the best elliptical orbit (ADDLAT=600 mm); however, the elliptical orbit increased FOV by ~16%. Conclusion: The flexible imaging geometry of the robotic x-ray system enables development of highly optimized scan orbits. Imaging of the weight-bearing spine could benefit from elliptical detector trajectories to achieve improved tradeoffs in scatter reduction, noise, and truncation.
KEYWORDS: Breast, Digital breast tomosynthesis, Tissues, Visualization, Mammography, Breast cancer, Medicine, Magnetic resonance imaging, X-ray imaging, X-rays
Assessment of breast density at the point of mammographic examination could lead to optimized breast cancer screening pathways. The onsite breast density information may offer guidance of when to recommend supplemental imaging for women in a screening program. A software application (Insight BD, Siemens Healthcare GmbH) for fast onsite quantification of volumetric breast density is evaluated. The accuracy of the method is assessed using breast tissue equivalent phantom experiments resulting in a mean absolute error of 3.84%. Reproducibility of measurement results is analyzed using 8427 exams in total, comparing for each exam (if available) the densities determined from left and right views, from cranio-caudal and medio-lateral oblique views, from full-field digital mammograms (FFDM) and digital breast tomosynthesis (DBT) data and from two subsequent exams of the same breast. Pearson correlation coefficients of 0.937, 0.926, 0.950, and 0.995 are obtained. Consistency of the results is demonstrated by evaluating the dependency of the breast density on women’s age. Furthermore, the agreement between breast density categories computed by the software with those determined visually by 32 radiologists is shown by an overall percentage agreement of 69.5% for FFDM and by 64.6% for DBT data. These results demonstrate that the software delivers accurate, reproducible, and consistent measurements that agree well with the visual assessment of breast density by radiologists.
Assessment of breast density at the point of mammographic examination could lead to optimized breast cancer screening pathways. The onsite breast density information may offer guidance when to recommend supplemental imaging for women in a screening program. In this work, performance evaluation of a new software (Insight BD, Siemens Healthcare GmbH) for fast onsite quantification of volumetric breast density is presented. Accuracy of volumetric measurement is evaluated using breast tissue equivalent phantom experiments. Reproducibility of measurement results is analyzed using 8150 4-view mammography exams. Furthermore, agreement between breast density categories computed by the software with those determined visually by radiologists is examined. The results of the performance evaluation demonstrate that the software delivers accurate and reproducible measurements that agree well with the visual assessment of breast density by radiologists.
Digital breast tomosynthesis (DBT) is a three-dimensional (3-D) X-ray imaging modality that allows the breast to be viewed in a 3-D format, minimizing the effect of overlapping breast tissue. DBT is commonly known for its high in-plane spatial resolution allowing to detect very small structures inside the breast which makes it a powerful tool in the clinical environment. However, since DBT is a limited angle tomography, artifacts are inevitable. In this paper, we investigate the influence of the angular scanning range as well as the slice thickness, i. e. the distance between two adjacent slices, on the in-plane spatial resolution of calcifications and present an analytic model to describe the imaging process. For the validation of the analytic model, 54 datasets with varying calcification diameter, slice thickness, and angular scanning range, were used and compared to a ray-casting simulation. It could be shown that the relative mean error between the analytic model and the generated ground truth over all datatsets is ε- = 0.0137. The results indicate that both investigated parameters affect the in-plane spatial resolutio
KEYWORDS: Digital breast tomosynthesis, Signal attenuation, Signal detection, Breast, Tissues, Image quality, Clinical trials, Digital mammography, 3D image reconstruction, Psychophysics
Detection of lesions is an essential part of making a diagnosis in mammography and therefore is a main focus in the development of algorithms built for image quality assessment. We propose a hybrid approach with an accurate lesion projection model and embedding of lesions into clinical images that already contain relevant structures of anatomical noise. Using an algebraic lesion model, lesions with different sizes and contrasts are generated. The projection algorithm incorporates the modeling of blur effects due to system movement and the physical extent of the anode. Signal and background patches are extracted and used to evaluate channelized Hotelling observers with Laguerre-Gauss channels and with Gabor channels. A four-alternative forced-choice study with five medical imaging experts is performed and the inter-reader agreement with and without the model observers is determined by using Fleiss' kappa. Analyzing three different sizes for tiny, dense lesions and four density levels for larger mass-like lesions we find a good detection rate of the tiny lesions for both human as well as model observers. The inter-reader agreement using the common interpretation of Fleiss' kappa is substantial or better. Comparing full-field digital mammography and digital breast tomosynthesis w.r.t. the different mass densities we find that human readers and model observers perform well on the DBT data and the detection rate drops with lesion contrast as expected. The inter-reader agreement here is fair for the lowest contrast and substantial for the denser cases. Both human readers and model observers show difficulty in detecting the low contrast lesions in FFDM images. The inter-reader agreement is rather poor among all readers. Overall, the results indicate a good agreement between human observers and model observers and a distinctive benefit of 3-D reconstruction over FFDMs for low contrast lesions.
X-ray cone-beam (CB) imaging is moving towards playing a large role in diagnostic radiology. Recently, an innovative, versatile X-ray system (Multitom Rax, Siemens Healthcare, GmbH, Forchheim, Germany) was introduced for diagnostic radiology. This system enables taking X-ray radiographs with high flexibility in patient positioning, as well as acquiring semi-circular short CB scans in a variety of orientations. We show here that this system can be further programmed to accurately scan the entire spine in the weight-bearing position. Such a diagnostic imaging capability has never been demonstrated so far. However, we may expect it to play an important clinical role as clinicians agree that spine diseases would be more accurately interpretable in the weight-bearing position. We implemented a geometry that provides complete data so that CB artifacts may be avoided. This geometry consists of two circular arcs connected by a line segment. We assessed immediate and short-term motion reproducibility, as well as ability to image the entire spine within a Rando phantom. Strongly encouraging results were obtained. Reproducibility with sub-mm accuracy was observed and the entire spine was accurately reconstructed.
Talbot-Lau X-ray imaging (TLXI) provides information about scattering and refractive features of objects – in addition to the well-known conventional X-ray attenuation image. We investigated the potential of TLXI for the detection of hairline fractures in bones, which are often initially occult in conventional 2D X-ray images. For this purpose, hairline fractures were extrinsically provoked in a porcine trotter (post-mortem) and scanned with a TLXI system. In the examined case, hairline fractures caused dark-field and differential-phase signals, whereas they were not evident in the conventional X-ray image. These findings motivate a comprehensive and systematic investigation of the applicability of TLXI for diagnosing hairline fractures.
The upsurge in interest of digital tomosynthesis is mainly caused by breast imaging; however, it finds more and more attention in orthopedic imaging as well. Offering a superior in-plane resolution compared to CT imaging and the additional depth information compared to conventional 2-D X-ray images, tomosynthesis may be an interesting complement to the other two imaging modalities. Additionally, a tomosynthesis scan is likely to be faster and the radiation dose is considerably below that of a CT. Usually, a tomosynthetic acquisition focuses only on one body part as the common acquisition techniques restrict the field-of-view. We propose a method which is able to perform full-body acquisitions with a standard X-ray system by shifting source and detector simultaneously in parallel planes without the need to calibrate the system beforehand. Furthermore, a novel aliasing filter is introduced which addresses the impact of the non-isotropic resolution during the reconstruction. We provide images obtained by filtered as well as unfiltered backprojection and discuss the influence of the scanning angle as well as the reconstruction filter on the reconstructed images. We found from the experiments that our method shows promising results especially for the imaging of anatomical structures which are usually obscured by each other since the depth resolution allows to distinguish between these structures. Additionally, as of the high isotropic in-plane spatial resolution of the tomographic volume, it is easily possible to perform precise measurements which are a crucial task, e. g. during the planning of orthopedic surgeries or the assessment of pathologies like scoliosis or subtle fractures.
Tomosynthesis images of the breast suffer from artifacts caused by the presence of highly absorbing materials. These can be either induced by metal objects like needles or clips inserted during biopsy devices, or larger calcifications inside the examined breast. Mainly two different kinds of artifacts appear after the filtered backprojection procedure. The first type is undershooting artifacts near edges of high-contrast objects caused by the filtering step. The second type is out-of-plane (ripple) artifacts that appear even in slices where the metal object or macrocalcifications does not exist. Due to the limited angular range of tomosynthesis systems, overlapping structures have high influence on neighboring regions. To overcome these problems, a segmentation of artifact introducing objects is performed on the projection images. Both projection versions, with and without high-contrast objects are filtered independently to avoid undershootings. During backprojection a decision is made for each reconstructed voxel, if it is artifact or high-contrast object. This is based on a mask image, gained from the segmentation of high-contrast objects. This procedure avoids undershooting artifacts and additionally reduces out-of-plane ripple. Results are demonstrated for different kinds of artifact inducing objects and calcifications.
In this work, we provide an initial characterization of a novel twin robotic X-ray system. This system is equipped
with two motor-driven telescopic arms carrying X-ray tube and flat-panel detector, respectively. 2D radiographs
and fluoroscopic image sequences can be obtained from different viewing angles. Projection data for 3D cone-beam
CT reconstruction can be acquired during simultaneous movement of the arms along dedicated scanning
trajectories. We provide an initial evaluation of the 3D image quality based on phantom scans and clinical
images. Furthermore, initial evaluation of patient dose is conducted. The results show that the system delivers
high image quality for a range of medical applications. In particular, high spatial resolution enables adequate
visualization of bone structures. This system allows 3D X-ray scanning of patients in standing and weight-bearing
position. It could enable new 2D/3D imaging workflows in musculoskeletal imaging and improve diagnosis of
musculoskeletal disorders.
KEYWORDS: Sensors, Fluctuations and noise, Stereoscopy, Image quality, Data acquisition, Computed tomography, Prototyping, 3D image processing, Physics, Cancer
In the last decade C–arm–based cone–beam CT became a widely used modality for intraoperative imaging. Typically a C–arm scan is performed using a circle–like trajectory around a region of interest. Therefor an angular range of at least 180° plus fan–angle must be covered to ensure a completely sampled data set. This fact defines some constraints on the geometry and technical specifications of a C–arm system, for example a larger C radius or a smaller C opening respectively. These technical modifications are usually not beneficial in terms of handling and usability of the C–arm during classical 2D applications like fluoroscopy. The method proposed in this paper relaxes the constraint of 180◦ plus fan–angle rotation to acquire a complete data set. The proposed C–arm trajectory requires a motorization of the orbital axis of the C and of ideally two orthogonal axis in the C plane. The trajectory consists of three parts: A rotation of the C around a defined iso–center and two translational movements parallel to the detector plane at the begin and at the end of the rotation. Combining these three parts to one trajectory enables for the acquisition of a completely sampled dataset using only 180° minus fan–angle of rotation. To evaluate the method we show animal and cadaver scans acquired with a mobile C-arm prototype. We expect that the transition of this method into clinical routine will lead to a much broader use of intraoperative 3D imaging in a wide field of clinical applications.
Compresssed sensing seems to be very promising for image reconstruction in computed tomography. In the last
years it has been shown, that these algorithms are able to handle incomplete data sets quite well. As cost function
these algorithms use the l1-norm of the image after it has been transformed by a sparsifying transformation.
This yields to an inequality-constrained convex optimization problem.
Due to the large size of the optimization problem some heuristic optimization algorithms have been proposed
in the last years. The most popular way is optimizing the rawdata and sparsity cost functions separately in an
alternating manner.
In this paper we will follow this strategy. Thereby we present a new method to adapt these optimization steps.
Compared to existing methods which perform similar, the proposed method needs no a priori knowledge about
the rawdata consistency. It is ensured that the algorithm converges to the best possible value of the rawdata cost
function, while holding the sparsity constraint at a low value. This is achieved by transferring both optimization
procedures into the rawdata domain, where they are adapted to each other.
To evaluate the algorithm, we process measured clinical datasets. To cover a wide field of possible applications, we
focus on the problems of angular undersampling, data lost due to metal implants, limited view angle tomography
and interior tomography. In all cases the presented method reaches convergence within less than 25 iteration
steps, while using a constant set of algorithm control parameters. The image artifacts caused by incomplete
rawdata are mostly removed without introducing new effects like staircasing. All scenarios are compared to an
existing implementation of the ASD-POCS algorithm, which realizes the stepsize adaption in a different way.
Additional prior information as proposed by the PICCS algorithm can be incorporated easily into the optimization
process.
The limited angle problem is a well-known problem in computed tomography. It is caused by missing data over
a certain angle interval, which make an inverse Radon transform impossible. In daily routine this problem can
arise for example in tomosynthesis, C-arm CT or dental CT.
In the last years there has been a big development in the field of compressed sensing algorithms in computed
tomography, which deal very good with incomplete data. The most popular way is to integrate a minimal total
variation norm in form of a cost function into the iteration process. To find an exact solution of such a constrained
minimization problem, computationally very demanding higher order algorithms should be used. Due to the non
perfect sparsity of the total variation representation, reconstructions often show the so called staircase effect.
The method proposed here uses the solutions of the iteration process as an estimation for the missing angle
data. Compared to a pure compressed sensing-based algorithm we reached much better results within the same
number of iterations and could eliminate the staircase effect.
The algorithm is evaluated using measured clinical datasets.
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