High Energy Resolution Hyperspectral X-Ray Imaging for Low-Dose Contrast-Enhanced Digital Mammography

Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising

Computer tomography (CT) has played an essential role in the field of medical diagnosis. Considering the potential risk of exposing patients to X-ray radiations, low-dose CT (LDCT) images have been widely applied in the medical imaging field. Since reducing the radiation dose may result in increased noise and artifacts, methods that can eliminate the noise and artifacts in the LDCT image have drawn increasing attentions and produced impressive results over the past decades. However, recent proposed methods mostly suffer from noise remaining, over-smoothing structures, or false lesions derived from noise. To tackle these issues, we propose a novel degradation adaption local-to-global transformer (DALG-Transformer) for restoring the LDCT image. Specifically, the DALG-Transformer is built on self-attention modules which excel at modeling long-range information between image patch sequences. Meanwhile, an unsupervised degradation representation learning scheme is first developed in medical image processing to learn abstract degradation representations of the LDCT images, which can distinguish various degradations in the representation space rather than the pixel space. Then, we introduce a degradation-aware modulated convolution and gated mechanism into the building modules (i.e., multi-head attention and feed-forward network) of each Transformer block, which can bring in the complementary strength of convolution operation to emphasize on the spatially local context. The experimental results show that the DALG-Transformer can provide superior performance in noise removal, structure preservation, and false lesions elimination compared with five existing representative deep networks. The proposed networks may be readily applied to other image processing tasks including image reconstruction, image deblurring, and image super-resolution.

Joint Texture Search and Histogram Redistribution for Hyperspectral Image Quality Improvement

Due to optical noise, electrical noise, and compression error, data hyperspectral remote sensing equipment is inevitably contaminated by various noises, which seriously affect the applications of hyperspectral data. Therefore, it is of great significance to enhance hyperspectral imaging data quality. To guarantee the spectral accuracy during data processing, band-wise algorithms are not suitable for hyperspectral data. This paper proposes a quality enhancement algorithm based on texture search and histogram redistribution combined with denoising and contrast enhancement. Firstly, a texture-based search algorithm is proposed to improve the accuracy of denoising by improving the sparsity of 4D block matching clustering. Then, histogram redistribution and Poisson fusion are used to enhance spatial contrast while preserving spectral information. Synthesized noising data from public hyperspectral datasets are used to quantitatively evaluate the proposed algorithm, and multiple criteria are used to analyze the experimental results. At the same time, classification tasks were used to verify the quality of the enhanced data. The results show that the proposed algorithm is satisfactory for hyperspectral data quality improvement.

Motion Artifact Reduction in Contrast-Enhanced Dual-Energy Mammography - A Multireader Study about the Effect of Nonrigid Registration as Motion Correction on Image Quality

PURPOSE

 The technically caused delay between low-energy (LE) and high-energy (HE) acquisitions allows motion artifacts in contrast-enhanced dual-energy mammography (CEDEM). In this study the effect of motion correction by nonrigid registration on image quality of the recombined images was investigated.

MATERIALS AND METHODS

 Retrospectively for 354 recombined CEDEM images an additional recombined image was processed from the raw data of LE and HE images using the motion correction algorithm. Five radiologists with many years of experience in breast cancer diagnostic imaging compared side-by-side one conventional processed CEDEM image with the corresponding image processed by the motion correction algorithm. Every pair of images was compared based on six criteria: General image quality (1), sharpness of skin contour (2), reduction of image artifacts (3), sharpness of lesion contour (4), contrast of lesion (5), visibility of lymph nodes (6). These criteria were rated on a Likert scale (improvement: + 1, + 2; deterioration: -1, -2).

RESULTS

 The mean ratings concerning criteria 1-5 showed a superiority of the recombined images processed by the motion correction algorithm. For example, the mean rating of general image quality was 0.86 (95 % CI: 0.78; 0.93). Only the mean rating concerning criterion 6 showed an inferiority of the recombined images processed by the motion correction algorithm (-0.29 (-0.46; -0.13)).

CONCLUSION

 The usage of nonrigid registration for motion correction significantly improves the general image quality and the quality of subordinate criteria on the recombined CEDEM images at the expense of somewhat reduced lymph node visibility in some cases.

KEY POINTS

  · The usage of motion correction in CEDEM improves the general image quality. · Motion correction might have the potential to increase diagnostic accuracy. · Alternative methods of motion artifact reduction are not yet available in clinical practice.

CITATION FORMAT

· Sistermanns M, Kowall B, Hörnig M et al. Motion Artifact Reduction in Contrast-Enhanced Dual-Energy Mammography - A Multireader Study about the Effect of Nonrigid Registration as Motion Correction on Image Quality. Fortschr Röntgenstr 2021; 193: 1183 - 1188.

Investigation of CdTe, GaAs, Se and Si as Sensor Materials for Mammography

Despite the benefits of mammography investigations, some studies have shown that X-ray exposure from the mammography screening itself can statistically cause breast cancer in a small fraction of women. Therefore, a dose reduction in mammography is desirable. At the same time, there is a demand for a higher spatial resolution in mammographic imaging. The most promising way to achieve these goals is the use of advanced photon-processing semiconductor X-ray detectors with optimum sensor materials. This study addresses the investigation of the optimum semiconductor sensor material for mammography in combination with the photon-processing detector Medipix3RX. The influence of K-shell fluorescence from the sensor material on the achievable contrast-to-noise ratio is investigated, as well as the attenuation efficiency. The three different sensor materials, CdTe, GaAs, and Si are studied, showing advances of CdTe-sensors for mammography. Furthermore, a comparison of the contrast-to-noise ratio between a clinical Se-detector and Medipix3RX detectors with Si- and CdTe-sensors is shown using a self-produced mammography phantom that is based on real human tissue.

Indirect X-ray Detectors Based on Inkjet-Printed Photodetectors with a Screen-Printed Scintillator Layer

Organic photodetectors (PDs) based on printing technologies will allow to expand the current field of PD applications toward large-area and flexible applications in areas such as medical imaging, security, and quality control, among others. Inkjet printing is a powerful digital tool for the deposition of smart and functional materials on various substrates, allowing the development of electronic devices such as PDs on various substrates. In this work, inkjet-printed PD arrays, based on the organic thin-film transistor architecture, have been developed and applied for the indirect detection of X-ray radiation using a scintillator ink as an X-ray absorber. The >90% increase of the photocurrent of the PDs under X-ray radiation, from about 53 nA without the scintillator film to about 102 nA with the scintillator located on top of the PD, proves the suitability of the developed printed device for X-ray detection applications.