Optimization of tube voltage in X-ray dark-field chest radiography

Sauter, Andreas; Andrejewski, Jana; Marco, F. De; Willer, Konstantin; Gromann, Lukas B.; Noichl, Wolfgang; Kriner, Fabian; Fischer, Florian; Braun, Christian; Kœhler, Thomas; Meurer, Felix; Fingerle, Alexander A.; Pfeiffer, Daniela; Rummeny, Ernst J.; Herzen, Julia; Pfeiffer, Franz

doi:10.1038/s41598-019-45256-2

Cited by 31 publications

(25 citation statements)

References 32 publications

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“…The machine could be used to aid slide selection in pathology, especially for multi-focal lesions, or simply by providing a three-dimensional "digital histology" support. It would also help accelerating the clinical uptake of XPCI, alongside pilots such as the mammography study with synchrotron radiation in Trieste, Italy 17 and the lung study underway in Munich, Germany 27 .…”

Section: Discussionmentioning

confidence: 99%

Detection of involved margins in breast specimens with X-ray phase-contrast computed tomography

Massimi

Suaris

Hagen

et al. 2021

Sci Rep

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Margins of wide local excisions in breast conserving surgery are tested through histology, which can delay results by days and lead to second operations. Detection of margin involvement intraoperatively would allow the removal of additional tissue during the same intervention. X-ray phase contrast imaging (XPCI) provides soft tissue sensitivity superior to conventional X-rays: we propose its use to detect margin involvement intraoperatively. We have developed a system that can perform phase-based computed tomography (CT) scans in minutes, used it to image 101 specimens approximately half of which contained neoplastic lesions, and compared results against those of a commercial system. Histological analysis was carried out on all specimens and used as the gold standard. XPCI-CT showed higher sensitivity (83%, 95% CI 69–92%) than conventional specimen imaging (32%, 95% CI 20–49%) for detection of lesions at margin, and comparable specificity (83%, 95% CI 70–92% vs 86%, 95% CI 73–93%). Within the limits of this study, in particular that specimens obtained from surplus tissue typically contain small lesions which makes detection more difficult for both methods, we believe it likely that the observed increase in sensitivity will lead to a comparable reduction in the number of re-operations.

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Section: Discussionmentioning

confidence: 99%

Detection of involved margins in breast specimens with X-ray phase-contrast computed tomography

Massimi

Suaris

Hagen

et al. 2021

Sci Rep

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“…The acceleration voltage was set to 70 kV p , which is a reasonable compromise between interferometer performance and X-ray transmission. 24…”

Section: Methodsmentioning

confidence: 99%

X-ray Dark-Field Chest Imaging can Detect and Quantify Emphy-sema in COPD Patients

Willer

Fingerle

Noichl

et al. 2021

Preprint

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SummaryBackgroundDiseases of the respiratory system are leading global causes of chronic morbidity and mortality. While advanced medical imaging technologies of today deliver detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection and/or follow-ups is still lacking. Here, we report on the first human application of a novel modality, namely X-ray dark-field chest imaging, which might fill this gap. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest X-rays, and yet requires only a fraction of the dose applied in computed tomography (CT).MethodsFor this first clinical evaluation, we have built a novel dark-field chest X-ray system, which is also capable of simultaneously acquiring a conventional thorax radiograph (7 seconds, 0·035 mSv effective dose). Representing a major medical condition, we selected chronic obstructive pulmonary disease as study subject to obtain a first impression of potential diagnostic benefits relevant to humans. For a collective of 77 patients with different disease stages, X-ray dark-field- and CT-images were acquired and visually assessed by 5 readers. In addition, pulmonary function tests were performed for every patient. The individual data sets were evaluated in a statistical work-up using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison.FindingsCompared to CT-based parameters (quantitative emphysema: ρ=–0·27, p=0·0893 and visual emphysema: ρ=–0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with diffusion capacity in the evaluated collective. Emphysema assessment based on dark-field chest X-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance in comparison to conventional clinical tests characterizing emphysema.InterpretationX-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema as it provides relevant information representing the structural condition of lung parenchyma. Significant diagnostic benefits are also expected for other lung disorders.FundingEuropean Research Council, Royal Philips, Karlsruhe Nano Micro Facility.Research in contextEvidence before this studyWith a rising number of examinations in the last decades, X-rays play an indispensable role in clinical routine. Contrast formation in medical X-ray imaging such as radiography, fluoroscopy, and computed tomography is based on attenuation, which generally benefits from large differences in atomic number and/or mass density between involved materials. If these conditions are not prevalent, or the resolution of the imaging system is not sufficient, diagnostic capabilities are limited. However, attenuation is not the only physical effect X-rays are subjected to when penetrating matter. Variations in an object’s electron density lead to refraction and coherent small-angle scattering of incident X-rays. Phase-sensitive imaging techniques can detect these wave-optical phenomena, yielding additional object information. The dark-field signal, being a function of small-angle scattering, can provide structural information on the micron scale, generally below the resolution limit of the imaging system. Due to their very stringent requirements to X-ray source coherence, these techniques were originally limited to large-scale synchrotron facilities. The proposal of a three-grating interferometer in 2006, however, enabled the use of low-brilliance sources for X-ray phase-contrast imaging and thereby paved the way into the clinics. Such an apparatus elegantly allows the simultaneous acquisition of the conventional attenuation, differential phase-contrast, and novel dark-field signals. In a compact table-top system suitable for investigating murine disease models, numerous studies on pulmonary disorders such as chronic obstructive pulmonary disease (COPD), pulmonary fibrosis, pneumothorax, ventilator-associated lung injury, lung cancer, and pneumonia have been conducted and demonstrated a broad diagnostic value of the dark-field modality in particular. Adapting the system to enable imaging of the human body is a technical challenge due to limitations of the micrometer-fine, high aspect ratio grating structures in terms of fabricable size and performance at clinically relevant X-ray energies. The first evidences that these limitations are manageable were delivered in 2017 and 2018 by in-vivo porcine and human cadaver studies with an experimental prototype system.Added value of this studyWith this work we present the first X-ray dark-field chest images of human subjects in-vivo and demonstrate the method’s feasibility in a clinical surrounding. To enable this study, we have conceived, constructed, and commissioned a custom-built first demonstrator system suitable for patient use. This includes satisfying clinical demands regarding safety, usability, acquisition time, radiation dose, field of view, and image quality. This study marks the transition from investigating artificially induced disease models to evaluating the modality’s actual diagnostic performance in patients.Implications of all available evidenceOur findings indicate that X-ray dark-field radiography provides image-type information of the lungs’ underlying microstructure in humans. In view of the strong link between alveolar structure and the functional condition of the lung, this capability is highly relevant for respiratory medicine and might help to establish a better understanding of pulmonary disorders. With regard to early detection of COPD, which is generally accompanied by structural impairments of the lung, this novel technique might support resolving the prevalent under-diagnosis reported in literature. With an effective dose significantly lower (about a factor of hundred) compared to thorax computed tomography, dark-field radiography could be used as broadly deployed screening tool.

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“…where L sr ∈ L and P sr ∈ P(k). The class probability matrix P(k) is calculated according to equation (9). Suppose the threshold of training error is ε ′ ∈ R. If ε(k) < ε ′ , stop and quit this training process.…”

Section: /15mentioning

confidence: 99%

“…As a vital verification way with simpler operations, recognition of X-ray chest radiography images can be done to screening out part of COVID-19 patients to make diagnosis results and the severity of disease more intuitional 8,9 . For X-ray chest radiography images, the required equipments can be lightweight and transportable 10 .…”

Section: Introductionmentioning

confidence: 99%

A Bagging Dynamic Deep Learning Network for Diagnosing COVID-19

Zhang

Chen

Sun

2021

Preprint

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COVID-19 is a serious epidemic all over the world. As an efficient way in intelligent medical services, using X-ray chest radiography image for automatically diagnosing COVID-19 provides huge assistances and conveniences for clinicians in practice. In this paper, a bagging dynamic deep learning network (B-DDLN) is proposed for diagnosing COVID-19 by intelligently recognizing X-ray chest radiography images. After a series of preprocessing steps for images, we pre-train convolution blocks as a feature extractor. For the extracted features, bagging dynamic learning network classifier is trained based on neural dynamic learning algorithm and bagging algorithm. B-DDLN connects feature extractor and bagging classifier in series. Experimental results verify that using the proposed B-DDLN can achieve 98.8889% testing accuracy, which illustrates the best diagnosis performances among existing methods on the open image set and provides evidences for further detection and treatment.

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Optimization of tube voltage in X-ray dark-field chest radiography

Cited by 31 publications

References 32 publications

Detection of involved margins in breast specimens with X-ray phase-contrast computed tomography

Detection of involved margins in breast specimens with X-ray phase-contrast computed tomography

X-ray Dark-Field Chest Imaging can Detect and Quantify Emphy-sema in COPD Patients

A Bagging Dynamic Deep Learning Network for Diagnosing COVID-19

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