Automatic and efficient tomographic reconstruction algorithms

In this thesis we present several methods to automate tomographic reconstruction algorithms and several novel tomographic reconstruction algorithms with the focus on being easily applicable and efficient to use.</table

A framework for directional and higher-order reconstruction in photoacoustic tomography

Photoacoustic tomography is a hybrid imaging technique that combines high optical tissue contrast with high ultrasound resolution. Direct reconstruction methods such as filtered back-projection, time reversal and least squares suffer from curved line artefacts and blurring, especially in the case of limited angles or strong noise. In recent years, there has been great interest in regularised iterative methods. These methods employ prior knowledge of the image to provide higher quality reconstructions. However, easy comparisons between regularisers and their properties are limited, since many tomography implementations heavily rely on the specific regulariser chosen. To overcome this bottleneck, we present a modular reconstruction framework for photoacoustic tomography, which enables easy comparisons between regularisers with different properties, e.g. nonlinear, higher-order or directional. We solve the underlying minimisation problem with an efficient first-order primal-dual algorithm. Convergence rates are optimised by choosing an operator-dependent preconditioning strategy. A variety of reconstruction methods are tested on challenging 2D synthetic and experimental data sets. They outperform direct reconstruction approaches for strong noise levels and limited angle measurements, offering immediate benefits in terms of acquisition time and quality. This work provides a basic platform for the investigation of future advanced regularisation methods in photoacoustic tomography

Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography

At x-ray beamlines of synchrotron light sources, the achievable time-resolution for 3D tomographic imaging of the interior of an object has been reduced to a fraction of a second, enabling rapidly changing structures to be examined. The associated data acquisition rates require sizable computational resources for reconstruction. Therefore, full 3D reconstruction of the object is usually performed after the scan has completed. Quasi-3D reconstruction—where several interactive 2D slices are computed instead of a 3D volume—has been shown to be significantly more efficient, and can enable the real-time reconstruction and visualization of the interior. However, quasi-3D reconstruction relies on filtered backprojection type algorithms, which are typically sensitive to measurement noise. To overcome this issue, we propose Noise2Filter, a learned filter method that can be trained using only the measured data, and does not require any additional training data. This method combines quasi-3D reconstruction, learned filters, and self-supervised learning to derive a tomographic reconstruction method that can be trained in under a minute and evaluated in real-time. We show limited loss of accuracy compared to training with additional training data, and improved accuracy compared to standard filter-based methods

Automated FDK-filter selection for cone-beam CT in research environments

Users of X-ray (micro-)CT in research environments often study many different types of objects, with many different research questions. For each new scan, the settings of the scan (number of angles, dose, cone angle) are chosen by the user, often based on how much time is available, the dose sensitivity of the sample, and geometrical characteristics of the particular CT-scanner that is used. The FDK algorithm is the most common reconstruction method used for circular cone-beam data. Its filter is typically chosen based on characteristics of the object, the scan parameters, and task-specific metrics. This imposes a problem for case-by-case research use, as selecting an optimal filter requires manual and subjective user choices as well as considerable expertise. In this article we present a computationally efficient and automated method to compute an FDK-filter for a given measured projection dataset that is optimal with respect to an objectively defined quality criterion that is based on the difference between the measured projection data and the computed projections of the reconstructed volume. We show that for a variety of objects, scan settings (number of angle

High-resolution cone-beam scan of an apple and pebbles with two dosage levels

We release two tomographic scans with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The objects are referred to as apple and pebbles (more specific, hydrograins), respectively. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels

High-resolution cone-beam scan of a pomegranate with two dosage levels

We release two tomographic scans of a pomegranate with two levels of radiation dosage for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels

High-resolution cone-beam scan of two pomegranates with two dosage levels

We release tomographic scans of two pomegranates with two levels of radiation dosage of two measured objects for noise-level comparative studies in data analysis, reconstruction or segmentation methods. The dataset collected with higher dosage is referred to as the "good" dataset; and the other as the "noisy" dataset, as a way to distinguish between the two dosage levels

Referentieraming van emissies naar de lucht uit landbouw en landgebruik tot 2030 : Achtergronddocument bij de Klimaat-en Energieverkenning 2019, met ramingen van emissies van methaan, lachgas, ammoniak, stikstofoxide, fijnstof en NMVOS uit de landbouw en kooldioxide en lachgas door landgebruik

In het kader van de Klimaat- en Energieverkenning (KEV) zijn ramingen gemaakt voor 2020, 2025 en 2030 van i) niet aan energie gerelateerde emissies uit de landbouw naar de lucht, in de vorm van methaan (CH4), lachgas (N2O), ammoniak (NH3), stikstofoxide (NOx), fijnstof (PM10 en PM2,5) en NMVOS (niet-methaan vluchtige organische stoffen) en ii) emissies van kooldioxide (CO2) en N2O door landgebruik, landgebruiksveranderingen en bosbouw (LULUCF). Het jaar 2017 is het basisjaar in de ramingen. De ramingen gaan uit van vastgesteld beleid en van naleving van onderliggende wet- en regelgeving. De peildatum voor het vastgestelde beleid in de raming is 1 mei 2019. De onzekerheden zijn in beeld gebracht voor de factoren met een groot effect op de emissies in 2030. In de raming is de CH4-emissie in 2030 met ruim 32 miljoen kg CH4 afgenomen ten opzichte van 2017 (6,4%). Deze afname wordt veroorzaakt door een afname van het aantal melkkoeien en jongvee. De geraamde N2O-emissie in 2030 is bijna 1 miljoen kg lager (4,5%) dan die in 2017. De grootste afname in N2O- emissie is zichtbaar bij bemesting met kunstmest en bij beweiding. De ammoniakemissie uit de landbouw neemt af van 114 miljoen kg in 2017 naar 109 miljoen kg in 2020 en 101 miljoen kg in 2030. Deze daling hangt samen met meer emissiearme stallen en minder melkkoeien, jongvee en varkens. De NOx-emissie (uitgedrukt in NO) is in 2030 0,7 miljoen kg lager dan in 2017. De emissie van fijnstof (PM10) neemt af van 6,2 miljoen kg in 2017 naar 5,1 miljoen kg in 2030 en die van de fijnere fractie van fijnstof (PM2,5) neemt af van 0,60 miljoen kg in 2017 naar 0,52 miljoen kg in 2030. De totale geraamde emissies uit de LULUCF-sector liggen in de periode 2020-2030 tussen de 5339 miljoen kg en 5707 miljoen kg CO2-equivalenten. Toepassing van de regels uit de LULUCF-verordening van de EU, om de prestaties van lidstaten te beoordelen op de emissies en verwijderingen van CO2 voor de tijdreeks 2020-2030, resulteert in een nettotekort van 316 miljoen kg CO2-equivalenten in 2025 en 258 miljoen kg CO2-equivalenten in 2030. Als prestaties voor de vijfjarige periodes 2021-2025 en 2026-2030 in het kader van LULUCF afgerekend worden, komt dat op een nettotekort van 1500 miljoen kg CO2-equivalent in de eerste periode en van 1200 miljoen kg CO2 equivalent in de tweede periode