Anomaly detection (AD) in medical applications is a promising field, offering a cost-effective alternative to labor-intensive abnormal data collection and labeling. However, the success of feature reconstruction-based methods in AD is often hindered by two critical factors: the domain gap of pre-trained encoders and the exploration of decoder potential. The EA2D method we propose overcomes these challenges, paving the way for more effective AD in medical imaging. In this paper, we present encoder-attention-2decoder (EA2D), a novel method tailored for medical AD. Firstly, EA2D is optimized through two tasks: a primary feature reconstruction task between the encoder and decoder, which detects anomalies based on reconstruction errors, and an auxiliary transformation-consistency contrastive learning task that explicitly optimizes the encoder to reduce the domain gap between natural images and medical images. Furthermore, EA2D intensely exploits the decoder's capabilities to improve AD performance. We introduce a self-attention skip connection to augment the reconstruction quality of normal cases, thereby magnifying the distinction between normal and abnormal samples. Additionally, we propose using dual decoders to reconstruct dual views of an image, leveraging diverse perspectives while mitigating the over-reconstruction issue of anomalies in AD. Extensive experiments across four medical image modalities demonstrates the superiority of our EA2D in various medical scenarios. Our method's code will be released at https://github.com/TumCCC/E2AD.

Defocus blur, due to spatially-varying sizes and shapes, is hard to remove. Existing methods either are unable to effectively handle irregular defocus blur or fail to generalize well on other datasets. In this work, we propose a divide-and-conquer approach to tackling this issue, which gives rise to a novel end-to-end deep learning method, called prior-and-prediction inverse kernel transformer (P2IKT), for single image defocus deblurring. Since most defocus blur can be approximated as Gaussian blur or its variants, we construct an inverse Gaussian kernel module in our method to enhance its generalization ability. At the same time, an inverse kernel prediction module is introduced in order to flexibly address the irregular blur that cannot be approximated by Gaussian blur. We further design a scale recurrent transformer, which estimates mixing coefficients for adaptively combining the results from the two modules and runs the scale recurrent ``coarse-to-fine" procedure for progressive defocus deblurring. Extensive experimental results demonstrate that our P2IKT outperforms previous methods in terms of PSNR on multiple defocus deblurring datasets.

X-ray computed tomography (CT) is a crucial tool for non-invasive medical diagnosis that uses differences in materials' attenuation coefficients to generate contrast and provide 3D information. Grating-based dark-field-contrast X-ray imaging is an innovative technique that utilizes small-angle scattering to generate additional co-registered images with additional microstructural information. 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. However, the susceptibility of Talbot-Lau interferometers to mechanical vibrations coupled with a need to minimize data acquisition times has hindered its application in clinical routines and the combination of X-ray dark-field imaging and large field-of-view (FOV) tomography in the past. In this work, we propose a processing pipeline to address this issue in a human-sized clinical dark-field CT prototype. We present the corrective measures that are applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. This is achieved by identifying spatially and temporally variable vibrations in air reference scans. By translating the found correlations to the sample scan, we can identify and mitigate relevant fluctuation modes for scans with arbitrary sample sizes. This approach effectively eliminates the requirement for sample-free detector area, while still distinctly separating fluctuation and sample information. As a result, samples of arbitrary dimensions can be reconstructed without being affected by vibration artifacts. To demonstrate the viability of the technique for human-scale objects, we present reconstructions of an anthropomorphic thorax phantom.

Grating-based phase- and dark-field-contrast X-ray imaging is a novel technology that aims to extend conventional attenuation-based X-ray imaging by unlocking two additional contrast modalities. The so called phase-contrast and dark-field channels provide enhanced soft tissue contrast and additional microstructural information. Accessing this additional information comes at the expense of a more intricate measurement setup and necessitates sophisticated data processing. A big challenge for translating grating-based dark-field computed tomography to medical applications lies in minimizing the data acquisition time. While a continuously moving detector is ideal, it prohibits conventional phase stepping techniques that require multiple projections under the same angle with different grating positions. One solution to this problem is the so-called sliding window processing approach that is compatible with continuous data acquisition. However, conventional sliding window techniques lead to crosstalk-artifacts between the three image channels, if the projection of the sample moves too fast on the detector within a processing window. In this work we introduce a new interpretation of the phase retrieval problem for continuous acquisitions as a demodulation problem. In this interpretation, we identify the origin of the crosstalk-artifacts as partially overlapping modulation side bands. Furthermore, we present three algorithmic extensions that improve the conventional sliding-window-based phase retrieval and mitigate crosstalk-artifacts. The presented algorithms are tested in a simulation study and on experimental data from a human-scale dark-field CT prototype. In both cases they achieve a substantial reduction of the occurring crosstalk-artifacts.

X-ray computed tomography (CT) is an invaluable imaging technique for non-invasive medical diagnosis. However, for soft tissue in the human body the difference in attenuation is inherently small. 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 an 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 fluctuation modes, allowing for a sample reconstruction free of vibration artifacts.

Light Field Microscopy (LFM) is a scan-less 3D imaging technique capable of capturing fast biological processes, such as neural activity in zebrafish. However, current methods to recover a 3D volume from the raw data require long reconstruction times hampering the usability of the microscope in a closed-loop system. Moreover, because the main focus of zebrafish brain imaging is to isolate and study neural activity, the ideal volumetric reconstruction should be sparse to reveal the dominant signals. Unfortunately, current sparse decomposition methods are computationally intensive and thus introduce substantial delays. This motivates us to introduce a 3D reconstruction method that recovers the spatio-temporally sparse components of an image sequence in real-time. In this work we propose a combination of a neural network (SLNet) that recovers the sparse components of a light field image sequence and a neural network (XLFMNet) for 3D reconstruction. In particular, XLFMNet is able to achieve high data fidelity and to preserve important signals, such as neural potentials, even on previously unobserved samples. We demonstrate successful sparse 3D volumetric reconstructions of the neural activity of live zebrafish, with an imaging span covering 800×800×250Mm3at an imaging rate of 24 - 88Hz, which provides a 1500 fold speed increase against prior work and enables real-time reconstructions without sacrificing imaging resolution.

In this paper, we consider combined TV denoising and diffusion tensor fitting in DTI using the affine-invariant Riemannian metric on the space of diffusion tensors. Instead of first fitting the diffusion tensors, and then denoising them, we define a suitable TV type energy functional which incorporates the measured DWIs (using an inverse problem setup) and which measures the nearness of neighboring tensors in the manifold. To approach this functional, we propose generalized forward- backward splitting algorithms which combine an explicit and several implicit steps performed on a decomposition of the functional. We validate the performance of the derived algorithms on synthetic and real DTI data. In particular, we work on real 3D data. To our knowledge, the present paper describes the first approach to TV regularization in a combined manifold and inverse problem setup.

3D functional nuclear imaging modalities like SPECT or PET provide valuable information, as small structures can be marked with radioactive tracers to be localized before surgery. This positional information is valuable during surgery as well, for example when locating potentially cancerous lymph nodes in the case of breast cancer. However, the volumetric information provided by pre-operative SPECT scans loses validity quickly due to posture changes and manipulation of the soft tissue during surgery. During the intervention, the surgeon has to rely on the acoustic feedback provided by handheld gamma-detectors in order to localize the marked structures. In this paper, we present a method that allows updating the pre-operative image with a very limited number of tracked readings. A previously acquired 3D functional volume serves as prior knowledge and a limited number of new 1D detector readings is used in order to update the prior knowledge. This update is performed by a 1D-3D registration algorithm that registers the volume to the detector readings. This enables the rapid update of the visual guidance provided to the surgeon during a radio-guided surgery without slowing down the surgical workflow. We evaluate the performance of this approach using Monte-Carlo simulations, phantom experiments and patient data, resulting in a positional error of less than 8 mm which is acceptable for surgery. The 1D-3D registration is also compared to a volumetric reconstruction using the tracked detector measurements without taking prior information into account, and achieves a comparable accuracy with significantly less measurements.

Complete projection (360 degrees ) free-space fluorescence tomography of opaque media is poised to enable 3-D imaging through entire small animals in vivo with improved depth resolution compared to 360 degrees -projection fiber-based systems or limited-view angle systems. This approach can lead to a new generation of Fluorescence Molecular Tomography (FMT) performance since it allows high spatial sampling of photon fields propagating through tissue at any projection, employing nonconstricted animal surfaces. Herein, we employ a volume carving method to capture 3-D surfaces of diffusive objects and register the captured surface in the geometry of an FMT 360 degrees -projection acquisition system to obtain 3-D fluorescence image reconstructions. Using experimental measurements we evaluate the accuracy of the surface capture procedure by reconstructing the surfaces of phantoms of known dimensions. We then employ this methodology to characterize the animal movement of anaesthetized animals. We find that the effects of animal movement on the FMT reconstructed image were within system resolution limits (approximately 0.07 cm).