arXiv subjects

Mats Persson

Publications and source records attributed to Mats Persson.

At least 19 recordsLinked to original sources

arXiv2026

Spatial resolution for gamma ray interactions in stacks of monolithic silicon sensors

We are developing a new implementation of Compton imaging for nuclear medicine using a large volume of stacked monolithic silicon sensors. In this work we investigate how the spatial resolution is impacted by the pixel size, an important input to the design of the sensors. In general CMOS design is less challenging with larger pixels leaving more space for analog and digital electronics. On the other hand, spatial resolution is one of the key parameters that will impact the performance of the Compton imaging system. It is also important to consider the range of the recoil electron produced in Compton or photoelectric interactions, which impacts how accurately the interaction point can be estimated. The achievable resolution was evaluated with two interaction position reconstruction algorithms: one based on a Gaussian fit of the detected charge and a second based on the characteristic energy deposition along the electron track, with the former performing better at lower energies. Monte Carlo simulations were performed for 140 keV and 511 keV sources embedded in a water phantom, with pixel pitches ranging from 25 $\mu m$ to 250 $\mu m$. Energy-averaged in-plane resolution degraded from 8 $\mu m$ to 94 $\mu m$ as pixel size increased. The out-of-plane resolution was 216 $\mu m$ at low energies, limited by sensor thickness, and improved to 155 $\mu m$ at higher energies. The results suggest that pixel sizes in the order of 100 $\mu m$ can achieve spatial resolutions on the order of tens of micrometers.

physics.ins-det↗

arXiv2026

Unpaired Deep Learning Synthesis of Photon-Counting CT Material Basis Maps from Non-contrast Energy-Integrating Abdominal CT Images

Photon-counting Computed Tomography (PCCT) is the most advanced Computed Tomography (CT) technology, offering significant improvements in image quality and diagnostic capabilities. However, since PCCT has only recently been adopted in the clinc, there are no publicly available PCCT image datasets for study. We therefore aim to synthesize PCCT spectral material-basis images from publicly available EID CT images. We propose a two-step deep learning model designed to synthesize photon-counting spectral material basis images from public Energy-Integrating Detector (EID) CT images. In the first step, we use a Denoising Diffusion Implicit Model (DDIM) to generate EID CT images from PCCT images. In the second step we use a U-Net with a Domain-Adversarial Neural Network to predict water and iodine maps from generated EID CT images. We also reconstruct basis images and virtual monoenergetic images (VMIs) from the predicted material-basis maps for evaluation. We evaluated the generated water and iodine maps as well as the 40 and 70 keV PCCT images in terms of Hounsfield Unit accuracy, modulation transfer function and noise power spectrum as well as qualitative image appearance. The reconstructed 40 and 70 keV PCCT images exhibit higher spatial resolution while preserving the anatomical structures and textures of the original EID CT images, thereby demonstrating the feasibility of the proposed approach. The proposed framework provides a feasible approach for synthesizing PCCT spectral material-basis images from conventional EID CT without requiring paired images. This method has the potential to provide large sets of synthetic training and evaluation data for PCCT algorithm development in data-limited environments.

physics.med-ph↗

arXiv2024

First Experimental Evaluation of a High-Resolution Deep Silicon Photon-Counting Sensor

Purpose: Current photon-counting computed tomography detectors are limited to a pixel size of around 0.3 mm-0.5 mm due to excessive charge sharing degrading the dose efficiency and energy resolution as the pixels become smaller. In this work, we present measurements of a prototype photon-counting detector that leverages the charge sharing to reach a theoretical sub-pixel resolution in the order of $1 {\mu}$m. The goal of the study is to validate our Monte-Carlo simulation using measurements, enabling further development. Approach: We measure the channel response at the MAX IV Lab, in the DanMAX beamline, with a 35 keV photon beam, and compare the measurements with a 2D Monte Carlo simulation combined with a charge transport model. Only a few channels on the prototype are connected to keep the number of wire bonds low. Results: The measurements agree generally well with the simulations with the beam close to the electrodes but diverge as the beam is moved further away. The induced charge cloud signals also seem to increase linearly as the beam is moved away from the electrodes. Conclusions: The agreement between measurements and simulations indicates that the Monte-Carlo simulation can accurately model the channel response of the detector with the photon interactions close to the electrodes, which indicates that the unconnected electrodes introduce unwanted effects that need to be further explored. With the same Monte-Carlo simulation previously indicating a resolution of around $1 {\mu}$m with similar geometry, the results are promising that an ultra-high resolution detector is not far in the future.

physics.med-ph↗

arXiv2023

PPFM: Image denoising in photon-counting CT using single-step posterior sampling Poisson flow generative models

Diffusion and Poisson flow models have shown impressive performance in a wide range of generative tasks, including low-dose CT image denoising. However, one limitation in general, and for clinical applications in particular, is slow sampling. Due to their iterative nature, the number of function evaluations (NFE) required is usually on the order of $10-10^3$, both for conditional and unconditional generation. In this paper, we present posterior sampling Poisson flow generative models (PPFM), a novel image denoising technique for low-dose and photon-counting CT that produces excellent image quality whilst keeping NFE=1. Updating the training and sampling processes of Poisson flow generative models (PFGM)++, we learn a conditional generator which defines a trajectory between the prior noise distribution and the posterior distribution of interest. We additionally hijack and regularize the sampling process to achieve NFE=1. Our results shed light on the benefits of the PFGM++ framework compared to diffusion models. In addition, PPFM is shown to perform favorably compared to current state-of-the-art diffusion-style models with NFE=1, consistency models, as well as popular deep learning and non-deep learning-based image denoising techniques, on clinical low-dose CT images and clinical images from a prototype photon-counting CT system.

eess.IV↗

arXiv2023

Noise suppression in photon-counting CT using unsupervised Poisson flow generative models

Deep learning has proven to be important for CT image denoising. However, such models are usually trained under supervision, requiring paired data that may be difficult to obtain in practice. Diffusion models offer unsupervised means of solving a wide range of inverse problems via posterior sampling. In particular, using the estimated unconditional score function of the prior distribution, obtained via unsupervised learning, one can sample from the desired posterior via hijacking and regularization. However, due to the iterative solvers used, the number of function evaluations (NFE) required may be orders of magnitudes larger than for single-step samplers. In this paper, we present a novel image denoising technique for photon-counting CT by extending the unsupervised approach to inverse problem solving to the case of Poisson flow generative models (PFGM)++. By hijacking and regularizing the sampling process we obtain a single-step sampler, that is NFE=1. Our proposed method incorporates posterior sampling using diffusion models as a special case. We demonstrate that the added robustness afforded by the PFGM++ framework yields significant performance gains. Our results indicate competitive performance compared to popular supervised, including state-of-the-art diffusion-style models with NFE=1 (consistency models), unsupervised, and non-deep learning-based image denoising techniques, on clinical low-dose CT data and clinical images from a prototype photon-counting CT system developed by GE HealthCare.

physics.med-ph↗

arXiv2023

Ring Artifact Correction in Photon-Counting Spectral CT Using a Convolutional Neural Network With Spectral Loss

Photon-counting spectral computed tomography is now clinically available. These new detectors come with the promise of higher contrast-to-noise ratio and spatial resolution and improved low-dose imaging. However, one important design consideration is to build detector elements that are sufficiently homogeneous. In practice, there will always be a degree of inhomogeneity in the detector elements, which will materialize as variations in the energy bin thresholds. Without proper detector calibration, this will lead to streak artifacts in the sinograms and corresponding ring artifacts in the reconstructed images, which limit their clinical usefulness. Since detector calibration is a time-consuming process, having access to a fast ring correction technique may greatly improve workflow. In this paper, we propose a deep learning-based post-processing technique for ring artifact correction in photon-counting spectral CT. We train a UNet with a custom loss to correct for ring artifacts in the material basis images. Our proposed loss is made ``task-aware'' by explicitly incorporating the fact that we are working with spectral CT by combining a L1-loss operating on the material basis images with a perceptual loss, using VGG16 as feature extractor, operating on 70 keV virtual monoenergetic images. Our results indicate that using this novel loss greatly improves performance. We demonstrate that our proposed method can successfully produce ring corrected 40, 70, and 100 keV virtual monoenergetic images.

physics.med-ph↗

arXiv2022

Dimensionality and Background Cancellation in Energy Selective X-Ray Imaging

Purpose: The set of linear attenuation coefficients that belong to materials in the human body is commonly assumed to be spanned by two basis functions in the range of clinical x-ray energies, even though there is evidence that the dimensionality of this set is greater than two. It has not yet been clear that the use of a third basis function could be beneficial in absence of contrast agents. Approach: In this work, the choice of the number of basis functions used in the basis decomposition method is studied for the task of producing an image where a third material is separated from a background of two other materials, in a case where none of the materials have a K-edge in the range of considered x-ray energies (20-140 keV). The case of separating iron from mixtures of liver and adipose tissue is studied with a simulated phantom which incorporates random and realistic tissue variability. Results: Inclusion of a third basis function improves the quantitative estimate of iron concentration by several orders of magnitude in terms of mean squared error in the resulting image. Conclusions: The inclusion of a third basis function in the basis decomposition is essential for the studied imaging task and could have potential application for quantitative estimation of iron concentration from material decomposed images.

physics.med-ph↗

arXiv2022

The effects of intra-detector Compton scatter on zero-frequency DQE for photon-counting CT using edge-on-irradiated silicon detectors

Background: Edge-on-irradiated silicon detectors are currently being investigated for use in photon-counting CT applications. The low atomic number of silicon leads to a significant number of incident photons being Compton scattered in the detector, depositing a part of their energy and potentially being counted multiple times. Although the physics of Compton scatter is well established, the effects of Compton interactions in the detector on image quality for an edge-on-irradiated silicon detector have still not been thoroughly investigated. Purpose: To investigate and explain effects of Compton scatter on zero-frequency DQE for photon-counting CT using edge-on-irradiated silicon detectors. Methods: We extend an existing Monte Carlo model of an edge-on-irradiated silicon detector to develop projection and image domain performance metrics for pure density and pure spectral imaging tasks. We show that the lowest energy threshold of the detector can be used as an effective discriminator of primary counts and cross-talk caused by Compton scatter. We study the developed metrics as functions of the lowest threshold energy. Results: Density imaging performance decreases monotonically as a function of the lowest threshold in both projection and image domains. Spectral imaging performance has a plateau between 0 and 10 keV and decreases monotonically thereafter, in both projection and image domain. Conclusions: Compton interactions contribute significantly to the density imaging performance of edge-on-irradiated silicon detectors. With the studied detector topology, the benefit of counting primary Compton interactions outweighs the penalty of multiple counting at all lower threshold energies. Compton interactions also contribute significantly to the spectral imaging performance for measured energies above 10 keV.

physics.med-ph↗

arXiv2022

Normalization of the task-dependent detective quantum efficiency of spectroscopic x-ray imaging detectors

Spectroscopic x-ray detectors (SXDs) are poised to play a substantial role in the next generation of medical x-ray imaging. Evaluating their performance in terms of the detective quantum efficiency (DQE) requires normalization of the frequency-dependent signal-to-noise ratio (SNR) by that of an ideal SXD. We provide mathematical expressions of the SNR of ideal SXDs for quantification and detection tasks and tabulate their numeric values for standardized tasks. We propose using standardized RQA-series x-ray spectra. We define ideal SXDs as those that (1) have an infinite number of infinitesimal energy bins, (2) do not distort the incident distribution of x-ray photons in the spatial or energy domains, and (3) do not decrease the frequency-dependent SNR of the incident distribution of x-ray quanta. We derive analytic expressions for the noise power spectrum (NPS) of such ideal detectors for detection and quantification tasks. We tabulate the NPS of ideal SXDs for RQA x-ray spectra for detection and quantification of aluminum, PMMA, iodine, and gadolinium basis materials. Our analysis shows that a single matrix determines the noise power of ideal SXDs in detection and quantification tasks, including basis material decomposition and line-integral estimation for pseudo-mono-energetic imaging. This NPS matrix is determined by the x-ray spectrum incident on the detector and the mass-attenuation coefficients of the set of basis materials. Combining existing tabulated values of the mass-attenuation coefficients of basis materials with standardized RQA x-ray spectra enabled tabulating numeric values of the NPS matrix for selected spectra and tasks. The numeric values and mathematical expressions of the NPS of ideal SXDs reported here can be used to normalize measurements of the frequency-dependent SNR of SXDs for experimental study of the task-dependent DQE.

physics.med-ph↗

arXiv2021

Development and evaluation of a 3D annotation software for interactive COVID-19 lesion segmentation in chest CT

Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent. However, manual segmentation can be very time consuming and subjective, given the lesions' large variation in shape, size and position. On the other hand, we still lack large manually segmented datasets that could be used for training machine learning-based models for fully automatic segmentation. In this work, we propose a new interactive and user-friendly tool for COVID-19 lesion segmentation, which works by alternating automatic steps (based on level-set segmentation and statistical shape modeling) with manual correction steps. The present software was tested by two different expertise groups: one group of three radiologists and one of three users with an engineering background. Promising segmentation results were obtained by both groups, which achieved satisfactory agreement both between- and within-group. Moreover, our interactive tool was shown to significantly speed up the lesion segmentation process, when compared to fully manual segmentation. Finally, we investigated inter-observer variability and how it is strongly influenced by several subjective factors, showing the importance for AI researchers and clinical doctors to be aware of the uncertainty in lesion segmentation results.

eess.IV↗

arXiv2020

Probing molecular excited states by atomic force microscopy

By employing single charge injections with an atomic force microscope, we investigated redox reactions of a molecule on a multilayer insulating film. First, we charged the molecule positively by attaching a single hole. Then we neutralized it by attaching an electron and observed three channels for the neutralization. We rationalize that the three channels correspond to transitions to the neutral ground state, to the lowest energy triplet excited states and to the lowest energy singlet excited states. By single-electron tunneling spectroscopy we measured the energy differences between the transitions obtaining triplet and singlet excited state energies. The experimental values are compared with density functional theory calculations of the excited state energies. Our results show that molecules in excited states can be prepared and that energies of optical gaps can be quantified by controlled single-charge injections. Our work demonstrates the access to, and provides insight into, ubiquitous electron-attachment processes related to excited-state transitions important in electron transfer and molecular optoelectronics phenomena on surfaces.

cond-mat.mes-hall↗

arXiv2020

Detective quantum efficiency of photon-counting CdTe and Si detectors for computed tomography: a simulation study

Purpose: Developing photon-counting CT detectors requires understanding the impact of parameters such as converter material, absorption length and pixel size. We apply a novel linear-systems framework, incorporating spatial and energy resolution, to study realistic silicon (Si) and cadmium telluride (CdTe) detectors at low count rate. Approach: We compared CdTe detector designs with $0.5\times0.5\; \mathrm{mm}^2$ and $0.225\times0.225\; \mathrm{mm}^2$ pixels and Si detector designs with $0.5\times0.5\; \mathrm{mm}^2$ pixels of 30 and 60 mm active absorption length, with and without tungsten scatter blockers. Monte-Carlo simulations of photon transport were used together with Gaussian charge sharing models fitted to published data. Results: For detection in a 300 mm thick object at 120 kVp, the 0.5 mm and 0.225 mm pixel CdTe systems have 28-41 $\%$ and 5-29 $\%$ higher DQE, respectively, than the 60 mm Si system with tungsten, whereas the corresponding numbers for two-material decomposition are 2 $\%$ lower to 11 $\%$ higher DQE and 31-54 $\%$ lower DQE compared to Si. We also show that combining these detectors with dual-spectrum acquisition is beneficial. Conclusions: In the low-count-rate regime, CdTe detector systems outperform the Si systems for detection tasks, while silicon outperforms one or both of the CdTe systems for material decomposition.

physics.med-ph↗

arXiv2019

A framework for performance characterization of energy-resolving photon-counting detectors

Photon-counting energy resolving detectors are subject to intense research interest, and there is a need for a general framework for performance assessment of these detectors. The commonly used linear-systems theory framework, which measures detector performance in terms of noise-equivalent quanta (NEQ) and detective quantum efficiency (DQE) is widely used for characterizing conventional X-ray detectors but does not take energy-resolving capabilities into account. We extend this framework to encompass energy-resolving photon-counting detectors and elucidate how the imperfect energy response of real-world detectors affects imaging performance. We generalize NEQ and DQE to matrix-valued quantities as functions of spatial frequency, and show how these can be calculated from simple Monte Carlo simulations. To demonstrate how the new metrics can be interpreted, we compute them for simplified models of fluorescence and Compton scatter in a photon-counting detector and for a Monte Carlo model of a CdTe detector with 0.5 x 0.5 mm^2 pixels. Our results show that the ideal-linear-observer performance for any detection or material quantification task can be calculated from the proposed metrics. Off-diagonal elements in these matrices are shown to be related to imperfect energy resolution. The Monte Carlo model of the CdTe detector predicts a zero-frequency dose efficiency relative to an ideal detector of 0.86 and 0.65 for detecting water and bone, respectively. When the task instead is to quantify these materials, the corresponding values are 0.34 for water and 0.26 for bone. We have shown that the matrix-valued NEQ and DQE metrics contain sufficient information for calculating the dose efficiency for both detection or quantification tasks, the task having any spatial and energy dependence. This framework will be beneficial for the development of photon-counting X-ray detectors.

physics.med-ph↗

arXiv2018

Robustness of Optimal Energy Thresholds in Photon-counting Spectral CT

An important question when developing photon-counting detectors for computed tomography is how to select energy thresholds. In this work thresholds are optimized by maximizing signal-difference-to-noise ratio squared (SDNR2) in an optimally weighted image and signal-to-noise ratio squared (SNR2) in a gadolinium basis image in a silicon-strip detector and a cadmium zinc telluride (CZT) detector, factoring in pileup and imperfect energy response in both detectors. To investigate to what extent one single set of thresholds could be applied in various imaging tasks, the robustness of optimal thresholds with 2 to 8 bins is examined with the variation of phantom thicknesses and target materials. In contrast to previous studies, the optimal threshold locations don't always increase with increasing attenuation if pileup is included. Optimizing the thresholds for a 30 cm phantom yields near-optimal SDNR2 or SNR2 regardless of target tissue types and surrounding attenuation for both detectors. Having more than 3 bins reduces the need for changing the thresholds depending on anatomies and tissues. Using around 6 bins or 8 bins may give near-optimal SDNR2 or SNR2 without generating an unnecessarily large amount of data.

physics.med-ph↗

arXiv2017

Frontier molecular orbitals of single molecules adsorbed on thin insulating films supported by a metal substrate: A simplified density functional theory approach

We present a simplified density functional theory (DFT) method to com- pute vertical electron and hole attachment energies to frontier orbitals of molecules absorbed on insulating films supported by a metal substrate. The adsorbate and the film is treated fully within DFT, whereas the metal is treated implicitly by a perfect conductor model. As illustrated for a pentacene molecule adsorbed on NaCl films sup- ported by a Cu substrate, we find that the computed energy gap between the highest and lowest occupied molecular orbitals - HOMO and LUMO -from the vertical attach- ment energies increases with the thickness of the insulating film, in agreement with experiments. This increase of the gap can be rationalized in a simple dielectric model with parameters determined from DFT calculations and is found to be dominated by the image interaction with the metal. However, this model overestimates the down- ward shift of the energy gap in the limit of an infinitely thick film. This work provides a new and efficient strategy to extend the use of density functional theory to the study of charging and discharging of large molecular absorbates on insulating films supported by a metal substrate.

cond-mat.mes-hall↗

arXiv2015

Tunable magnetoresistance in an asymmetrically coupled single molecule junction

Phenomena that are highly sensitive to magnetic fields can be exploited in sensors and non-volatile memories. The scaling of such phenomena down to the single molecule level may enable novel spintronic devices. Here we report magnetoresistance in a single molecule junction arising from negative differential resistance that shifts in a magnetic field at a rate two orders of magnitude larger than Zeeman shifts. This sensitivity to the magnetic field produces two voltage-tunable forms of magnetoresistance, which can be selected via the applied bias. The negative differential resistance is caused by transient charging of an iron phthalocyanine (FePc) molecule on a single layer of copper nitride (Cu2N) on a Cu(001) surface, and occurs at voltages corresponding to the alignment of sharp resonances in the filled and empty molecular states with the Cu(001) Fermi energy. An asymmetric voltage-divider effect enhances the apparent voltage shift of the negative differential resistance with magnetic field, which inherently is on the scale of the Zeeman energy. These results illustrate the impact that asymmetric coupling to metallic electrodes can have on transport through molecules, and highlight how this coupling can be used to develop molecular spintronic applications.

cond-mat.mes-hall↗

arXiv2015

Effect of electron-phonon interaction on the formation of one-dimensional electronic states in coupled Cl vacancies

The formation of extended electron states in one-dimensional nanostructures is of key importance for the function of molecular electronics devices. Here we study the effects of strong electron-phonon interaction on the formation of extended electronic states in intentionally created Cl vacancy pairs and chains in a NaCl bilayer on Cu(111). The interaction between the vacancies was tailored by fabricating vacancy pairs and chains of different orientation and separation with atomic precision using vertical manipulation. Small separation of divacancies led to the formation of symmetric and antisymmetric vacancy states and localized interface-states. By scanning tunneling spectroscopy (STS) we measured their energy splitting and broadening as a function of the inter-vacancy separation. Unexpectedly, the energy splitting between the vacancy states is enlarged by level repulsion resulting from phonon dressing of the electronic states, as evidenced by theory. Already for a few coupled vacancies we observe an emerging band structure of the defect band.

cond-mat.mes-hall↗

arXiv2014

Investigating Atomic Contrast in Atomic Force Microscopy and Kelvin Probe Force Microscopy on Ionic Systems using Functionalized Tips

Noncontact atomic force microscopy (NC-AFM) and Kelvin probe force microscopy (KPFM) have become important tools for nanotechnology; however, their contrast mechanisms on the atomic scale are not entirely understood. Here we used chlorine vacancies in NaCl bilayers on Cu(111) as a model system to investigate atomic contrast as a function of applied voltage, tip height, and tip functionalization. We demonstrate that the AFM contrast on the atomic scale decisively depends on both the tip termination and the sample voltage. On the contrary, the local contact potential difference (LCPD) acquired with KPFM showed the same qualitative contrast for all tip terminations investigated, which resembled the contrast of the electric field of the sample. We find that the AFM contrast stems mainly from electrostatic interactions but its tip dependence cannot be explained by the tip dipole alone. With the aid of a simple electrostatic model and by density functional theory we investigate the underlying contrast mechanisms.

cond-mat.mes-hall↗