PurposeProton radiation therapy may achieve precise dose delivery to the tumor while sparing non-cancerous surrounding tissue, owing to the distinct Bragg peaks of protons. Aligning the high-dose region with the tumor requires accurate estimates of the proton stopping power ratio (SPR) of patient tissues, commonly derived from computed tomography (CT) image data. Photon-counting detectors for CT have demonstrated advantages over their energy-integrating counterparts, such as improved quantitative imaging, higher spatial resolution, and filtering of electronic noise. We assessed the potential of photon-counting computed tomography (PCCT) for improving SPR estimation by training a deep neural network on a domain transform from PCCT images to SPR maps.ApproachThe XCAT phantom was used to simulate PCCT images of the head with CatSim, as well as to compute corresponding ground truth SPR maps. The tube current was set to 260 mA, tube voltage to 120 kV, and number of view angles to 4000. The CT images and SPR maps were used as input and labels for training a U-Net.ResultsPrediction of SPR with the network yielded average root mean square errors (RMSE) of 0.26% to 0.41%, which was an improvement on the RMSE for methods based on physical modeling developed for single-energy CT at 0.40% to 1.30% and dual-energy CT at 0.41% to 3.00%, performed on the simulated PCCT data.ConclusionsThese early results show promise for using a combination of PCCT and deep learning for estimating SPR, which in extension demonstrates potential for reducing the beam range uncertainty in proton therapy.
It has previously been shown that 2D spectral mammography can be used to discriminate between (likely benign) cystic and (potentially malignant) solid lesions in order to reduce unnecessary recalls in mammography. One limitation of the technique is, however, that the composition of overlapping tissue needs to be interpolated from a region surrounding the lesion. The purpose of this investigation was to demonstrate that lesion characterization can be done with spectral tomosynthesis, and to investigate whether the 3D information available in tomosynthesis can reduce the uncertainty from the interpolation of surrounding tissue. A phantom experiment was designed to simulate a cyst and a tumor, where the tumor was overlaid with a structure that made it mimic a cyst. In 2D, the two targets appeared similar in composition, whereas spectral tomosynthesis revealed the exact compositional difference. However, the loss of discrimination signal due to spread from the plane of interest was of the same strength as the reduction of anatomical noise. Results from a preliminary investigation on clinical tomosynthesis images of solid lesions yielded results that were consistent with the phantom experiments, but were still to some extent inconclusive. We conclude that lesion characterization is feasible in spectral tomosynthesis, but more data, as well as refinement of the calibration and discrimination algorithms, are needed to draw final conclusions about the benefit compared to 2D.
Spectral imaging is the acquisition of multiple images of an object at different energy spectra. In mammography,
dual-energy imaging (spectral imaging with two energy levels) has been investigated for several applications, in
particular material decomposition, which allows for quantitative analysis of breast composition and quantitative
contrast-enhanced imaging. Material decomposition with dual-energy imaging is based on the assumption that
there are two dominant photon interaction effects that determine linear attenuation: the photoelectric effect and
Compton scattering. This assumption limits the number of basis materials, i.e. the number of materials that
are possible to differentiate between, to two. However, Rayleigh scattering may account for more than 10% of
the linear attenuation in the mammography energy range. In this work, we show that a modified version of a
scanning multi-slit spectral photon-counting mammography system is able to acquire three images at different
spectra and can be used for triple-energy imaging. We further show that triple-energy imaging in combination
with the efficient scatter rejection of the system enables measurement of Rayleigh scattering, which adds an
additional energy dependency to the linear attenuation and enables material decomposition with three basis
materials. Three available basis materials have the potential to improve virtually all applications of spectral
imaging.
KEYWORDS: Modulation transfer functions, Sensors, Point spread functions, Photodetectors, X-rays, Image quality, 3D modeling, 3D image processing, Mammography, X-ray imaging
Tomosynthesis is emerging as a next generation technology in mammography. Combined with photon-counting detectors with the ability for energy discrimination, a novel modality is enabled — spectral tomosynthesis. Further advantages of photon-counting detectors in the context of tomosynthesis include elimination of electronic noise, efficient scatter rejection (in some geometries) and no lag. Fourier-based linear-systems analysis is a well-established method for optimizing image quality in two-dimensional x-ray systems. The method has been successfully adapted to threedimensional imaging, including tomosynthesis, but several areas need further investigation. This study focuses on two such areas: 1) Adaption of the methodology to photon-counting detectors, and 2) violation of the shift-invariance and stationarity assumptions in non-cylindrical geometries. We have developed a Fourier-based framework to study the image quality in a photon-counting tomosynthesis system, assuming locally linear, stationary, and shift-invariant system response. The framework includes a cascaded-systems model to propagate the modulation-transfer function (MTF) and noise-power spectrum (NPS) through the system. The model was validated by measurements of the MTF and NPS. High degrees of non-shift invariance and non-stationarity were observed, in particular for the depth resolution as the angle of incidence relative the reconstruction plane varied throughout the imaging volume. The largest effects on image quality in a given point in space were caused by interpolation from the inherent coordinate system of the x-rays to the coordinate system that was used for reconstruction. This study is part of our efforts to fully characterize the spectral tomosynthesis system, we intend to extend the model further to include the detective-quantum efficiency, observer modelling, and spectral effects.
The development of new x-ray imaging techniques often requires prior knowledge of tissue attenuation, but the sources of such information are sparse. We have measured the attenuation of adipose breast tissue using spectral imaging, in vitro and in vivo. For the in-vitro measurement, fixed samples of adipose breast tissue were imaged on a spectral mammography system, and the energy-dependent x-ray attenuation was measured in terms of equivalent thicknesses of aluminum and poly-methyl methacrylate (PMMA). For the in-vivo measurement, a similar procedure was applied on a number of spectral screening mammograms. The results of the two measurements agreed well and were consistent with published attenuation data and with measurements on tissue-equivalent material.
In x-ray imaging, contrast information content varies with photon energy. It is, therefore, possible to improve image quality by weighting photons according to energy. We have implemented and evaluated so-called energy weighting on a commercially available spectral photon-counting mammography system. The technique was evaluated using computer simulations, phantom experiments, and analysis of screening mammograms. The CNR benefit of energy weighting for a number of relevant target-background combinations measured by the three methods fell in the range of 2.2 to 5.2% when using optimal weight factors. This translates to a potential dose reduction at constant CNR in the range of 4.5 to 11%. We expect the choice of weight factor in practical implementations to be straightforward because (1) the CNR improvement was not very sensitive to weight, (2) the optimal weight was similar for all investigated target-background combinations, (3) aluminum/PMMA phantoms were found to represent clinically relevant tasks well, and (4) the optimal weight could be calculated directly from pixel values in phantom images. Reasonable agreement was found between the simulations and phantom measurements. Manual measurements on microcalcifications and automatic image analysis confirmed that the CNR improvement was detectable in energy-weighted screening mammograms.
Spectral X-ray imaging allows to differentiate between two given tissue types, provided their spectral absorption characteristics differ measurably. In mammography, this method is used clinically to determine a decomposition of the breast into adipose and glandular tissue compartments, from which the glandular tissue fraction and, hence, the volumetric breast density (VBD) can be computed. Another potential application of this technique is the characterization of lesions by spectral mammography. In particular, round lesions are relatively easily detected by experienced radiologists, but are often difficult to characterize. Here, a method is described that aims at discriminating cystic from solid lesions directly on a spectral mammogram, obtained with a calibrated spectral mammography system and using a hypothesis-testing algorithm based on a maximum likelihood approach. The method includes a parametric model describing the lesion shape, compression height variations and breast composition. With the maximum likelihood algorithm, the model parameters are estimated separately under the cyst and solid hypothesis. The resulting ratio of the maximum likelihood values is used for the final tissue characterization. Initial results using simulations and phantom measurements are presented.
Photon counting detector (PCD) x-ray imaging systems have seen increasing use in the past decade in applications such as low-dose radiography and tomography. A cascaded systems analysis model has been developed to describe the signal and noise transfer characteristics for such systems in a manner that accounts for unique PCD functionality (such as an application of a threshold) and explicitly considers the distribution of quanta through each stage. This model was used to predict the mean signal, modulation transfer function (MTF), noise-power spectrum (NPS), and detective quantum efficiency (DQE) of a silicon-strip PCD system, and these predictions were compared to measurements across a range of exposure conditions and thresholds. Further, the model was used to investigate the impact of design parameters such as detector thickness and pulse height amplification as well as unique PCD performance effects such as charge sharing and additive noise with respect to threshold. The development of an analytical model for prediction of such metrics provides a framework for understanding the complex imaging performance characteristics of PCD systems – especially important in the early development of new radiographic and tomographic applications – and a guide to task-based performance optimization.
In x-ray imaging, contrast information content varies with photon energy. It is therefore possible to improve image quality by weighting photons according to energy. We have implemented and evaluated so-called energy weighting on a commercially available spectral photon-counting mammography system. A practical formula for calculating the optimal weight from pixel values was derived. Computer simulations and phantom measurements revealed that the contrast-tonoise ratio was improved by 3%–5%, and automatic image analysis showed that the improvement was detectable in a set of screening mammograms.
Knowledge of x-ray attenuation is essential for developing and evaluating x-ray imaging technologies. For instance,
techniques to better characterize cysts at mammography screening would be highly desirable to reduce recalls, but
the development is hampered by the lack of attenuation data for cysts. We have developed a method to measure xray
attenuation of tissue samples using a prototype photon-counting spectral mammography unit. Spectral (energyresolved)
images were acquired and the image signal was mapped to two known reference materials, which were
used to derive the x-ray attenuation as a function of energy. We have measured the attenuation of 25 samples of
breast cyst fluid. Spectral measurements of water samples showed consistent results compared to published
attenuation values.
E. Fredenberg, E. Roessl, T. Koehler, U. van Stevendaal, I. Schulze-Wenck, N. Wieberneit, M. Stampanoni, Z. Wang, R. Kubik-Huch, N. Hauser, M. Lundqvist, M. Danielsson, M. Åslund
Phase-contrast imaging is an emerging technology that may increase the signal-difference-to-noise ratio in medical
imaging. One of the most promising phase-contrast techniques is Talbot interferometry, which, combined with
energy-sensitive photon-counting detectors, enables spectral differential phase-contrast mammography. We have
evaluated a realistic system based on this technique by cascaded-systems analysis and with a task-dependent
ideal-observer detectability index as a figure-of-merit. Beam-propagation simulations were used for validation
and illustration of the analytical framework. Differential phase contrast improved detectability compared to
absorption contrast, in particular for fine tumor structures. This result was supported by images of human
mastectomy samples that were acquired with a conventional detector. The optimal incident energy was higher
in differential phase contrast than in absorption contrast when disregarding the setup design energy. Further,
optimal weighting of the transmitted spectrum was found to have a weaker energy dependence than for absorption
contrast. Taking the design energy into account yielded a superimposed maximum on both detectability as a
function of incident energy, and on optimal weighting. Spectral material decomposition was not facilitated by
phase contrast, but phase information may be used instead of spectral information.
We present a novel method for characterizing mammographic findings using spectral imaging without the use
of contrast agent. Within a statistical framework, suspicious findings are analyzed to determine if they are
likely to be benign cystic lesions or malignant tissue. To evaluate the method, we have designed a phantom
where combinations of different tissue types are realized by decomposition into the material bases aluminum and
polyethylene. The results indicate that the lesion size limit for reliable characterization is below 10 mm diameter,
when quantum noise is the only considered source of uncertainty. Furthermore, preliminary results using clinical
images are encouraging, but allow no conclusions with significance.
We have designed a mammography system that for the first time combines photon-counting spectral imaging with
tomosynthesis. The present study is a comprehensive physical evaluation of the system; tomosynthesis, spectral
imaging, and the combination of both are compared using an ideal-observer model that takes anatomical noise
into account. Predictions of signal and noise transfer through the system are verified by contrast measurements
on a tissue phantom and 3D measurements of MTF and NPS. Clinical images acquired with the system are
discussed in view of the model predictions.
Beam quality optimization in mammography traditionally considers detection of a target obscured by quantum
noise on a homogenous background. It can be argued that this scheme does not correspond well to the
clinical imaging task because real mammographic images contain a complex superposition of anatomical structures,
resulting in anatomical noise that may dominate over quantum noise. Using a newly developed spectral
mammography system, we measured the correlation and magnitude of the anatomical noise in a set of mammograms.
The results from these measurements were used as input to an observer-model optimization that included
quantum noise as well as anatomical noise. We found that, within this framework, the detectability of tumors
and microcalcifications behaved very differently with respect to beam quality and dose. The results for small
microcalcifications were similar to what traditional optimization methods would yield, which is to be expected
since quantum noise dominates over anatomical noise at high spatial frequencies. For larger tumors, however,
low-frequency anatomical noise was the limiting factor. Because anatomical structure has similar energy dependence
as tumor contrast, optimal x-ray energy was significantly higher and the useful energy region wider than
traditional methods suggest. Measurements on a tissue phantom confirmed these theoretical results. Furthermore,
since quantum noise constitutes only a small fraction of the noise, the dose could be reduced substantially
without sacrificing tumor detectability. Exposure settings used clinically are therefore not necessarily optimal
for this imaging task. The impact of these findings on the mammographic imaging task as a whole is, however,
at this stage unclear.
Spectral imaging is a method in medical x-ray imaging to extract information about the object constituents by the material-specific energy dependence of x-ray attenuation. Contrast-enhanced spectral imaging has been
thoroughly investigated, but unenhanced imaging may be more useful because it comes as a bonus to the
conventional non-energy-resolved absorption image at screening; there is no additional radiation dose and no need
for contrast medium. We have used a previously developed theoretical framework and system model that include
quantum and anatomical noise to characterize the performance of a photon-counting spectral mammography
system with two energy bins for unenhanced imaging. The theoretical framework was validated with synthesized
images. Optimal combination of the energy-resolved images for detecting large unenhanced tumors corresponded
closely, but not exactly, to minimization of the anatomical noise, which is commonly referred to as energy
subtraction. In that case, an ideal-observer detectability index could be improved close to 50% compared to absorption imaging. Optimization with respect to the signal-to-quantum-noise ratio, commonly referred to as energy weighting, deteriorated detectability. For small microcalcifications or tumors on uniform backgrounds, however, energy subtraction was suboptimal whereas energy weighting provided a minute improvement. The performance was largely independent of beam quality, detector energy resolution, and bin count fraction. It is clear that inclusion of anatomical noise and imaging task in spectral optimization may yield completely different results than an analysis based solely on quantum noise.
We present the first evaluation of a recently developed silicon-strip detector for photon-counting dual-energy
breast tomosynthesis. The detector is well suited for tomosynthesis with high dose efficiency and intrinsic scatter
rejection. A method was developed for measuring the spatial resolution of a system based on the detector in terms
of the three-dimensional modulation transfer function (MTF). The measurements agreed well with theoretical
expectations, and it was seen that depth resolution was won at the cost of a slightly decreased lateral resolution.
This may be a justifiable trade-off as clinical images acquired with the system indicate improved conspicuity of
breast lesions. The photon-counting detector enables dual-energy subtraction imaging with electronic spectrumsplitting.
This improved the detectability of iodine in phantom measurements, and the detector was found to be
stable over typical clinical acquisition times. A model of the energy resolution showed that further improvements
are within reach by optimization of the detector.
X-ray detectors made of crystalline silicon have several advantages including low dark currents, fast charge
collection and high energy resolution. For high-energy x-rays, however, silicon suffers from its low atomic
number, which might result in low detection efficiency, as well as low energy and spatial resolution due to
Compton scattering. We have used a monte-carlo model to investigate the feasibility of a detector for pediatric
CT with 30 to 40 mm of silicon using x-ray spectra ranging from 80 to 140 kVp. A detection efficiency of 0.74
was found at 80 kVp, provided the noise threshold could be set low. Scattered photons were efficiently blocked
by a thin metal shielding between the detector units, and Compton scattering in the detector could be well
separated from photo absorption at 80 kVp. Hence, the detector is feasible at low acceleration voltages, which
is also suitable for pediatric imaging. We conclude that silicon detectors may be an alternative to other designs
for this special case.
We present data on a first prototype for photon counting tomosynthesis imaging of small children, which we call photoncounting
tomosynthesis (PCT). A photon counting detector can completely eliminate electronic noise, which makes it
ideal for tomosynthesis because of the low dose in each projection. Another advantage is that the detector allows for
energy sensitivity in later versions, which will further lower the radiation dose. In-plane resolution is high and has been
measured to be 5 lp/mm, at least 4 times better than in CT, while the depth resolution was significantly lower than
typical CT resolution. The image SNR decreased from 30 to 10 for a detail of 10 mm depth in increasing thickness of
PMMA from 10 to 80 mm. The air kerma measured for PCT was 5.2 mGy, which leads to an organ dose to the brain of
approximately 0.7 mGy. This dose is 96 % lower than a typical CT dose. PCT can be appealing for pediatric imaging
since young children have an increased sensitivity to radiation induced cancers. We have acquired post mortem images
of a newborn with the new device and with a state-of-the-art CT and compared the diagnostic information and dose
levels of the two modalities. The results are promising but more work is needed to provide input to a next generation
prototype that would be suitable for clinical trials.
The multi-prism lens (MPL) is a refractive x-ray lens consisting of two rows of prisms facing each other at an
angle. Rays entering the lens at the periphery will encounter a larger number of prisms than will central ones,
hence experiencing a greater refraction. The focusing effect of the MPL can be used to gather radiation from a
large aperture onto a smaller detector, and accordingly to make better use of the available x-ray flux in medical
x-ray imaging. Potential advantages of a better photon economy include shorter acquisition times, a reduced
tube loading, or an improved resolution. Since the focusing effect is one-dimensional it matches the design of
scanning systems.
In this study we present the first images acquired with an MPL instead of the pre-breast slit collimator in a
scanning mammography system. According to the measurements, the MPL is able to increase the flux 32% at
equal resolution compared to the slit collimator, or to improve the resolution 2.4 mm-1 at equal flux. If used
with a custom-made absorption filter in a clinical set-up, the gain of flux of the MPL is expected to be at least
45%, and the corresponding improvement in resolution to be 3 mm-1.
Dual-energy subtraction imaging (DES) is a method to improve the detectability of contrast agents over a lumpy
background. Two images, acquired at x-ray energies above and below an absorption edge of the agent material,
are logarithmically subtracted, resulting in suppression of the signal from the tissue background and a relative
enhancement of the signal from the agent. Although promising, DES is still not widely used in clinical practice.
One reason may be the need for two distinctly separated x-ray spectra that are still close to the absorption edge,
realized through dual exposures which may introduce motion unsharpness.
In this study, electronic spectrum-splitting with a silicon-strip detector is theoretically and experimentally
investigated for a mammography model with iodinated contrast agent. Comparisons are made to absorption
imaging and a near-ideal detector using a signal-to-noise ratio that includes both statistical and structural noise.
Similar to previous studies, heavy absorption filtration was needed to narrow the spectra at the expense of a
large reduction in x-ray flux. Therefore, potential improvements using a chromatic multi-prism x-ray lens (MPL)
for filtering were evaluated theoretically. The MPL offers a narrow tunable spectrum, and we show that the
image quality can be improved compared to conventional filtering methods.
Conventional energy filters for x-ray imaging are based on absorbing
materials which attenuate low energy photons, sometimes combined
with an absorption edge, thus also discriminating towards photons of
higher energies. These filters are fairly inefficient, in particular
for photons of higher energies, and other methods for achieving a
narrower bandwidth have been proposed. Such methods include various
types of monochromators, based on for instance mosaic crystals or
refractive multi-prism x-ray lenses (MPL's). Prism-array lenses
(PAL's) are similar to MPL's, but are shorter, have larger
apertures, and higher transmission. A PAL consists of a number of
small prisms arranged in columns perpendicular to the optical axis.
The column height decreases along the optical axis so that the
projection of lens material is approximately linear with a Fresnel
phase-plate pattern superimposed on it. The focusing effect is one
dimensional, and the lens is chromatic. Hence, unwanted energies can
be blocked by placing a slit in the image plane of a desired energy.
We present the first experimental and theoretical results on an
energy filter based on a silicon PAL. The study includes an
evaluation of the spectral shaping properties of the filter as well
as a quantification of the achievable increase in dose efficiency
compared to standard methods. Previously, PAL's have been
investigated with synchrotron radiation, but in this study a medical
imaging setup, based on a regular x-ray tube, is considered.
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