Dark-field imaging with x-rays is a novel interferometric technique that enables visualization of the alveolar structure. In the clinical context, the technique has been limited to radiographic applications and has not yet been implemented for computed tomography (CT) imaging. Initial studies have already demonstrated that dark-field imaging complements and improves conventional radiography of the thorax. Dark-field computed tomography, which is capable of yielding unobstructed 3D views, has only recently been brought to the human scale: With a first prototype system, we demonstrated the feasibility to implement a Talbot-Lau interferometer on a clinical CT system to perform dark-field imaging with clinical acquisition time and field of view. Until now, this prototype was limited to axial scans. In this work, we present our advancements in extending the setups capabilities to also support other modes of acquisition, namely surview and helical scans. The new capabilities of the updated dark-field CT scanner are demonstrated using an anthropomorphic thorax phantom.
X-ray computed tomography (CT) has been established as a daily tool in clinical diagnostics and has been continuously refined by more recent innovations in the last years. These systems are, however, limited by fundamental constraints since they are only capable of mapping X-ray attenuation differences in the tissue. Phase-contrast and dark-field imaging provide complementary contrast, which originates from physically different interaction processes of X-rays with matter. Particularly the dark-field signal is considered to have significant diagnostic potential since it is capable to retrieve micro-structural information below the actual resolution limit of the imaging system. This was demonstrated in various laboratory setups and recently also in the fist study with human patients in a clinical radiography system based on a grating interferometer. In a recent work, we presented the first implementation of such an X-ray interferometer into a clinical CT gantry. Upscaling and adapting this technology for a rotating CT gantry involves several challenges and tradeoffs ranging from limitations in interferometer design over fast, continuous signal acquisition requirements to tolerances in applied patient dose. In this work we discuss the performance of the first clinical dark-field CT prototype. For this purpose, we present results of our phantom studies which were designed to evaluate whether and how the dark-field contrast generated by the system is capable to provide additional structural sample information. The key aspects include the possibility of quantitative imaging and a gradual approach to simulate results that come as close as possible to a real application in a human patient.
We present a geometric calibration method for integrating a seven degrees of freedom robotic arm as a sample holder within an existing laboratory X-ray computed tomography setup. We aim to provide a flexible sample holder that is able to execute non-standard and task-specific trajectories for complex samples. The calibration is necessary to identify the accurate pose of the sample which deviates from the expected pose due to inaccurate placement of the robotic arm. The robotic arm is integrated with a unified software package that allows for path planning, collision detection, geometric calibration and reconstruction of the sample. With our software the user is able to command the robotic arm to execute arbitrary trajectories for a given sample in a safe manner and output its reconstruction to the user. We present experimental results with a circular trajectory where the robotic sample holder achieves identical visual quality compared to a conventional sample holder.
Grating-based phase-contrast and dark-field X-ray imaging is a promising technology for improving the diagnosis and imaging capabilities of breast cancer and lung diseases. While traditional X-ray techniques only consider the attenuation coefficient, phase-contrast and dark-field imaging are also capable of measuring the refractive index decrement and the so-called linear diffusion coefficient, a measure of a sample’s small-angle scattering strength. Consequently, the technique provides additional information about the micro-structure of a sample. While it is already possible to perform human chest dark-field radiography, it is assumed that its diagnostic value increases when performed in a tomographic setup. The thereby acquired three-dimensional mappings of the three modalities yield detailed information about morphological changes without being obscured by overlaying structures. This work presents the sample data processing and reconstruction pipeline of the first human-sized clinical dark-field CT system. In this novel setting we require a processing concept which is (1) compatible with continuous rotation, (2) can compensate for perturbances induced by system vibrations, and (3) still enables short processing and reconstruction times. An advanced sliding window approach was chosen for the sample data extraction to meet requirements (1) and (3). Furthermore, we present the corrective measures that have to be applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. The developed techniques are shown to successfully reduce the emergence of artefacts in the reconstructed images.
X-ray computed tomography (CT) is an invaluable imaging technique for non-invasive medical diagnosis. However, for soft tissue in the human body the inherent small difference in attenuation limits its significance. Grating-based X-ray phase-contrast is a relatively novel imaging method which detects additional interaction mechanisms between photons and matter, namely refraction and small-angle scattering, to generate additional images with different contrast. The experimental setup involves a Talbot-Lau interferometer whose susceptibility to mechanical vibrations hindered acquisition schemes suitable for clinical routine in the past. We present a processing pipeline to identify spatially and temporally variable fluctuations occurring in the first interferometer installed on a continuously rotating clinical CT gantry. The correlations of the vibrations in the modular grating setup are exploited to identify a small number of relevant vibration modes, allowing for an artifact-free reconstruction of a sample.
In sparse X-ray Computed Tomography, the radiation dose to the patient is lowered by measuring fewer projection views compared to a standard protocol. In this work we investigate a hybrid approach combining shearlet representation with deep learning for reconstruction of sparse-view X-ray computed tomography. The proposed method is hybrid in that it reconstructs the parts that can provably be retrieved by utilizing a model-based approach, and it in-paints the parts that provably cannot through a learning-based approach. In doing so, we attempt to benefit from the best aspects of model- and learning-based methods. We demonstrate first promising results on publicly available data.
Fourier integral microscopy (FiMic), also referred to as Fourier light field microscopy (FLFM) in the literature, was recently proposed as an alternative to conventional light field microscopy (LFM). FiMic is designed to overcome the non-uniform lateral resolution limitation specific to LFM. By inserting a micro-lens array at the aperture stop of the microscope objective, the Fourier integral microscope directly captures in a single-shot a series of orthographic views of the scene from different viewpoints. We propose an algorithm for the deconvolution of FiMic data by combining the well known Maximum Likelihood Expectation (MLEM) method with total variation (TV) regularization to cope with noise amplification in conventional Richardson-Lucy deconvolution.
Software for tomographic reconstruction has been around for decades now. So why yet another software framework for tomographic reconstruction? Because we needed a flexible, operator- and optimization-based framework in C++ for our own target applications, we developed our own some years ago. As our framework has been applied to many tomographic problems by now, ranging from optical tomography, lightfield tomography, SPECT, to various X-ray based imaging modalities (absorption contrast, differential phase contrast and anisotropic dark-field contrast), we decided to open source a modernized version of it. The framework elsa is written in platform-independent modern C++17 using the CMake build system, with high unit-test coverage and continuous integration to ascertain reliability and correctness, as well as a Python interface for easy and rapid prototyping. Our intent in open sourcing the framework and presenting it here is three-fold, first for easier reproducibility of our own research, second for use in teaching, and last but not least, in the hopes that some of you also find some usefulness in it for your own tasks.
Anisotropic X-ray Dark-field Tomography (AXDT) is a novel imaging modality aimed at the reconstruction of spherical scattering functions in every three-dimensional volume element, based on the directional X-ray dark-field contrast as measured by an X-ray grating interferometer. In this work, we re-derive a detectability index for the AXDT forward model directly using the spherical function formulation, and use it to compute optimized acquisition trajectories using a greedy algorithm. The results demonstrate that the optimized trajectories can represent task-specific features in AXDT accurately using only a fraction of the data.
Multispectral image acquisitions are increasingly popular in dermatology, due to their improved spectral resolution which enables better tissue discrimination. Most applications however focus on restricted regions of interest, imaging only small lesions. In this work we present and discuss an imaging framework for high-resolution multispectral imaging on large regions of interest.
Iterative tomographic reconstruction gets more and more into the focus of interest for x-ray computed tomography as parallel high-performance computing finds its way into compact and affordable computing systems in form of GPU devices. However, when it comes to the point of high-resolution x-ray computed tomography, e. g. measured at synchrotron facilities, the limited memory and bandwidth of such devices are soon stretched to their limits. Especially keeping the core part of tomographic reconstruction, the projectors, both versatile and fast for large datasets is challenging. Therefore, we demonstrate a multi-GPU accelerated forward- and backprojector based on projection matrices and taking advantage of two concepts to distribute large datasets into smaller units. The first concept involves splitting up the volume into chunks of slices perpendicular to the axis of rotation. The result is many perfectly independent tasks which then can be solved by distinct GPU devices. A novel ultrafast precalculation kernel prevents unnecessary data transfers for cone-beam geometries. Datasets with a great number of projections can additionally take advantage of the second concept, a split-up into angular wedges. We demonstrate the portability of our projectors to multiple devices and the associated speedup on a high-resolution liver sample measured at the synchrotron. With our splitting approaches, we gained factors of 3.5 - 3.9 on a system with four and 7.5 - 8.0 with eight GPUs. The computing time for our test example decreased from 23:5 s to 2:94 s in the latter case.
In dermatology, photographic imagery is acquired in large volumes in order to monitor the progress of diseases, especially melanocytic skin cancers. For this purpose, overview (macro) images are taken of the region of interest and used as a reference map to re-localize highly magni ed images of individual lesions. The latter are then used for diagnosis. These pictures are acquired at irregular intervals under only partially constrained circumstances, where patient positions as well as camera positions are not reliable. In the presence of a large number of nevi, correct identi cation of the same nevus in a series of such images is thus a time consuming task with ample chances for error. This paper introduces a method for largely automatic and simultaneous identi cation of nevi in di erent images, thus allowing the tracking of a single nevus over time, as well as pattern evaluation. The method uses a rotation-invariant feature descriptor that uses the local neighborhood of a nevus to describe it. The texture, size and shape of the nevus are not used to describe it, as these can change over time, especially in the case of a malignancy. We then use the Random Walks framework to compute the correspondences based on the probabilities derived from comparing the feature vectors. Evaluation is performed on synthetic and patient data at the university clinic.
Nuclear imaging is a commonly used tool in today's diagnostics and therapy planning. For interventional
use however it suffers from drawbacks which limit its application. Freehand SPECT was developed to overcome
these limitations and to provide 3D functional imaging during an intervention. It combines a nuclear
probe with an optical tracking system to obtain its position and orientation in space synchronized with its
reading. This information can be used to compute a 3D tomographic reconstruction of an activity distribution.
However, as there is no fixed geometry the system matrix has to be computed on-the-fly, using ad-hoc
models of the detection process. One solution for such a model is a reference look up table of previously
acquired measurements of a single source at different angles and distances. In this work two look up tables
with a one and four millimeter step size between the entries were acquired. Twelve datasets of a phantom
with two hollow spheres filled with a solution of Tc99wm were acquired with the Freehand SPECT system.
Reconstructions with the look up tables and two analytical models currently in use were performed with these
datasets and compared with each other. The finely sampled look up table achieved the qualitatively best
reconstructions, while one of the analytical models showed the best positional accuracy.
Intra-operative surface imaging with navigated beta probes in conjunction with positron-emitting radiotracers
like 18F-FDG has been shown to enable control of tumor resection borders. We showed previously that
employing iterative reconstruction (MLEM) in conjunction with an ad-hoc model of the detection physics
(based on solid-angle geometry, SA) improves the image quality. In this study, we sampled the beta probe
readings of a point source using a precision step-motor to generate a look-up-table (LUT) model. We also
generated a simplified geometrical model (SG) based on this data set. To see how these two models influence
the image quality compared to the old SA model, we reconstructed images from sparsely sampled datasets of
a phantom with three hotspots using each model. The images yielded 76% (SA), 81% (SG), and 81% (LUT)
mean NCC compared to the ground truth. The SG and LUT models, however, could resolve the hotspots
better in the datasets where the detector-to-phantom distance was larger. Additionally, we compared the
deviations of the SA and SG analytical models to the measured LUT model, where we found that the SG
model gives estimates substantially closer to the actual beta probe readings than the previous SA model.
3D functional imaging in the operating room can be extremely useful for some procedures like SLN mapping
or SLN biopsies. Freehand SPECT is an example of such an imaging modality, combining manually scanned,
hand-held 1D gamma detectors with spatial positioning systems in order to reconstruct localized 3D SPECT
images, for example in the breast or neck region. Standard series expansion methods are applied together with
custom physical models of the acquisition process and custom filtering procedures to perform 3D tomographic
reconstruction from sparse, limited-angle and irregularly sampled data. A Freehand SPECT system can easily be
assembled on a mobile cart suitable for use in the operating room. This work addresses in particular the problem
of objects with low uptake (like sentinel lymph nodes), where reconstruction tends to be difficult due to low signal
to noise ratio. In a neck-like phantom study, we show that four simulated nodes of 250 microliter volume with
0.06% respectively 0.03% uptake of a virtual 70MBq injection of Tc99m (the typical activity for SLN procedures
at our hospital) in a background of water can be reconstructed successfully using careful filtering procedures in
the reconstruction pipeline. Ten independent Freehand SPECT scans of the phantom were performed by several
different operators, with an average scan duration of 5.1 minutes. The resulting reconstructions show an average
spatial accuracy within voxel dimensions (2.5mm) compared to CT and exhibit correct relative quantification.
Diagnosis of benign and malign skin lesions is currently done mostly relying on visual assessment and frequent biopsies
performed by dermatologists. As the timely and correct diagnosis of these skin lesions is one of the most important
factors in the therapeutic outcome, leveraging new technologies to assist the dermatologist seems natural. Optical
spectroscopy is a technology that is being established to aid skin lesion diagnosis, as the multi-spectral nature of this
imaging method allows to detect multiple physiological changes like those associated with increased vasculature, cellular
structure, oxygen consumption or edema in tumors. However, spectroscopy data is typically very high dimensional (on
the order of thousands), which causes difficulties in visualization and classification. In this work we apply different
manifold learning techniques to reduce the dimensions of the input data and get clustering results. Spectroscopic data of
48 patients with suspicious and actually malignant lesions was analyzed using ISOMAP, Laplacian Eigenmaps and
Diffusion Maps with varying parameters and compared to results using PCA. Using optimal parameters, both ISOMAP
and Laplacian Eigenmaps could cluster the data into suspicious and malignant with 96% accuracy, compared to the
diagnosis of the treating physicians.
Cutaneous T-Cell Lymphoma (CTCL) is a cancer type externally characterized by alterations in the coloring of skin.
Optical spectroscopy has been proposed for quantification of minimal changes in skin offering itself as an interesting tool
for monitoring of CTCL in real-time. However, in order to be used in a valid way, measurements on the lesions have to
be taken at the same position and with the same orientation in each session. Combining hand-held optical spectroscopy
devices with tracking and acquiring synchronously spectral information with position and orientation, we introduce a
novel computer-assisted scheme for valid spectral quantification of disease progression. We further present an
implementation for an augmented reality guidance system that allows to find a point previously analyzed with an
accuracy of 0.8[mm] and 5.0[deg] (vs. 1.6[mm] and 6.6[deg] without guidance). The intuitive guidance, as well as the
preliminary results shows that the presented approach has great potential towards innovative computer-assistance
methods for quantification of disease progression.
KEYWORDS: Luminescence, Natural surfaces, Fluorescence tomography, Tomography, 3D acquisition, 3D image processing, Tissue optics, In vivo imaging, Reconstruction algorithms, Free space
Complete projection (360°) free-space fluorescence tomography of opaque media is poised to enable highly performing
three-dimensional imaging through entire small animals in-vivo. This approach can lead to a new generation of
Fluorescence Molecular Tomography (FMT) systems since it allows high spatial sampling of photon fields propagating
through tissue at any projection, employing non-constricted animal surfaces.
Key features of this development is the implementation of non-contact illumination, for example by using beam
scanning techniques for light delivery on the tissue surface and direct non-contact imaging with CCD cameras.
Similarly, the development of free-space geometries, i.e. implementations that do not utilize immersion of the animal in
matching fluids are essential for obtaining appropriate experimental simplicity and avoid unnecessary diffusion through
scattering matching media.
To facilitate these developments it is important to retrieve the three-dimensional surface and a common coordinate
system for the illumination system, the detection system and the animal. Herein, we employ a volume carving method to
capture three-dimensional surfaces of diffusive objects from its silhouettes and register the captured surface in the
geometry of an FMT 360°-projection acquisition system to obtain three-dimensional fluorescence image reconstructions.
Using experimental measurements we evaluate the accuracy of the surface capture procedure by reconstructing the
surfaces of phantoms of known dimensions and demonstrate how this surface extraction method can be utilized in an
FMT inversion scheme. We then employ this methodology to characterize the animal movement of anaesthetized
animals and study the effects of animal movement on the FMT reconstructed image quality.
Fluorescence molecular tomography (FMT) is an emerging modality for the in-vivo imaging of fluorescent probes
which improves upon existing planar photographic imaging techniques by quantitatively reconstructing fluorochrome
distributions in-vivo. We present here results using an FMT system capable of full view imaging for arbitrary surface
geometries. Results are presented comparing single and multiple projection configurations, and illustrating the need for
properly implemented non-negativity constraints.
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