LIVIVO - Search results -

Search results

Result 1 - 10 of total 29

Search options

Article ; Online: Dose development in sinonasal imaging over the last decade – a retrospective patient study

Carsten Hackenbroch / Joachim Rudolf Balthasar Strobel / Kai Johannes Lorenz / Meinrad Beer / Simone Schüle

Head & Face Medicine, Vol 19, Iss 1, Pp 1-

2023 Volume 12

Abstract: Abstract Background Computed tomography (CT) has become the primary imaging modality for visualization of the paranasal sinuses. In this retrospective, single center patient study the radiation dose development in the past 12 years in CT imaging of the ... ...

Abstract	Abstract Background Computed tomography (CT) has become the primary imaging modality for visualization of the paranasal sinuses. In this retrospective, single center patient study the radiation dose development in the past 12 years in CT imaging of the paranasal sinuses was assessed. Methods The computed tomography dose index (CTDIVol) and dose length product (DLP) of a total of 1246 patients (average age: 41 ± 18 years, 361 females, 885 males) were evaluated, who received imaging of the paranasal sinuses either for chronic sinusitis diagnostic, preoperatively or posttraumatically. Scans were performed on three different CT scanners (Somatom Definition AS, Somatom Definition AS+, Somatom Force, all from Siemens Healthineers) and on one CBCT (Morita) ranging from 2010 to 2022. Reconstruction techniques were filtered back projection and three generations of iterative reconstruction (IRIS, SAFIRE, ADMIRE, all from Siemens Healthineers). Group comparisons were performed using either parametrical (ANOVA) or non-parametrical tests (Kruskal-Wallis Test), where applicable. Results Over the past 12 years, there was a 73%, 54%, and 66% CTDIVol reduction and a significant (p < 0.001) 72%, 33%, and 67% DLP reduction in assessing the paranasal sinuses for chronic sinusitis, preoperatively and posttraumatically, respectively. Conclusion Technological developments in CT imaging, both hardware and software based, have led to a significant reduction in dose exposure in recent years. Particularly in imaging of the paranasal sinuses, the reduction of radiation exposure is of great interest due to the often young patient age and radiation-sensitive organs in the area of radiation exposure.
Keywords	Dose development ; Sinonasal imaging ; Computed tomography ; Chronic sinusitis ; Preoperative ; Posttraumatic ; Specialties of internal medicine ; RC581-951
Subject code	616
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Imaging of Pathologies of the Temporal Bone and Middle Ear

Christopher Kloth / Annika Beck / Nico Sollmann / Meinrad Beer / Marius Horger / Wolfgang Maximilian Thaiss

Tomography, Vol 9, Iss 6, Pp 2190-

Inflammatory Diseases, Their Mimics and Potential Complications—Pictorial Review

2023 Volume 2210

Abstract: Imaging of the temporal bone and middle ear is challenging for radiologists due to the abundance of distinct anatomical structures and the plethora of possible pathologies. The basis for a precise diagnosis is knowledge of the underlying anatomy as well ... ...

Abstract	Imaging of the temporal bone and middle ear is challenging for radiologists due to the abundance of distinct anatomical structures and the plethora of possible pathologies. The basis for a precise diagnosis is knowledge of the underlying anatomy as well as the clinical presentation and the individual patient’s otological status. In this article, we aimed to summarize the most common inflammatory lesions of the temporal bone and middle ear, describe their specific imaging characteristics, and highlight their differential diagnoses. First, we introduce anatomical and imaging fundamentals. Additionally, a point-to-point comparison of the radiological and histological features of the wide spectrum of inflammatory diseases of the temporal bone and middle ear in context with a review of the current literature and current trends is given.
Keywords	temporal bone ; ear ; external auditory canal ; middle ear ; inner ear ; inflammation ; Computer applications to medicine. Medical informatics ; R858-859.7
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Accelerated model-based quantitative diffusion MRI

Thomas Hüfken / Jannik M. Arbogast / Anna-Katinka Bracher / Meinrad Beer / Henning Neubauer / Volker Rasche

Zeitschrift für Medizinische Physik, Vol 32, Iss 2, Pp 240-

A feasibility study for musculoskeletal application

2022 Volume 247

Abstract: Purpose: To develop a model-based reconstruction technique for diffusion quantification based on accelerated two-dimensional echo planar data, obtained with multiple b-weightings. In combination with a dedicated undersampling pattern, acceleration ... ...

Abstract	Purpose: To develop a model-based reconstruction technique for diffusion quantification based on accelerated two-dimensional echo planar data, obtained with multiple b-weightings. In combination with a dedicated undersampling pattern, acceleration factors above three were proven feasible in a clinical setting. Methods: The proposed model-based method minimizes a cost function considering the l2-norm of the difference between the Fourier transformation of a synthetic diffusion-model-generated k-space and the measured k-space data. Further regularization is performed by introduction of a total variation (TV) constraint to the cost function. Acceleration is achieved by a non-random undersampling pattern using acceleration factors that correspond to the total number of b-values. A rectangular region of variable size, centered in k-space, remains fully sampled for correction of phase variations, introduced by the different diffusion-encoding strengths. Results: Qualitative analysis of the resulting images (S0 and ADC) demonstrates the potential of the suggested undersampling pattern in combination with a model-based iterative reconstruction. An edge analysis highlights the preservation of high-frequency information for all investigated undersampling factors. In comparison to a conventional SENSE-accelerated reconstruction, the quantitative analysis of the ADC maps revealed a significantly (P < 0.05) superior performance of the suggested technique, enabling acceleration factors of R = 3.65 without compromising diffusion data fidelity. Conclusion: The presented work shows the potential of model-based ADC quantification, which, in combination with a suited undersampling pattern for multiple b-values, enables more than three-fold acceleration using two-dimensional EPI without sacrificing ADC fidelity.
Keywords	Diffusion ; Model-based reconstruction ; Iterative reconstruction ; ADC ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset

Daniel Schaudt / Reinhold von Schwerin / Alexander Hafner / Pascal Riedel / Manfred Reichert / Marianne von Schwerin / Meinrad Beer / Christopher Kloth

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 16

Abstract: Abstract Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for ... ...

Abstract	Abstract Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease severity suggests more suitable and specific treatment options. In this study, we publish severity scores for the 2358 COVID-19 positive images in the COVIDx8B dataset, creating one of the largest collections of publicly available COVID-19 severity data. Furthermore, we train and evaluate deep learning models on the newly created dataset to provide a first benchmark for the severity classification task. One of the main challenges of this dataset is the skewed class distribution, resulting in undesirable model performance for the most severe cases. We therefore propose and examine different augmentation strategies, specifically targeting majority and minority classes. Our augmentation strategies show significant improvements in precision and recall values for the rare and most severe cases. While the models might not yet fulfill medical requirements, they serve as an appropriate starting point for further research with the proposed dataset to optimize clinical resource allocation and treatment.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Impact of radiation dose reduction and iterative image reconstruction on CT-guided spine biopsies

Karolin J. Paprottka / Karina Kupfer / Vivian Schultz / Meinrad Beer / Claus Zimmer / Thomas Baum / Jan S. Kirschke / Nico Sollmann

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 11

Abstract: Abstract This study aimed to systematically evaluate the impact of dose reduction on image quality and confidence for intervention planning and guidance regarding computed tomography (CT)-based intervertebral disc and vertebral body biopsies. We ... ...

Abstract	Abstract This study aimed to systematically evaluate the impact of dose reduction on image quality and confidence for intervention planning and guidance regarding computed tomography (CT)-based intervertebral disc and vertebral body biopsies. We retrospectively analyzed 96 patients who underwent multi-detector CT (MDCT) acquired for the purpose of biopsies, which were either derived from scanning with standard dose (SD) or low dose (LD; using tube current reduction). The SD cases were matched to LD cases considering sex, age, level of biopsy, presence of spinal instrumentation, and body diameter. All images for planning (reconstruction: “IMR1”) and periprocedural guidance (reconstruction: “iDose4”) were evaluated by two readers (R1 and R2) using Likert scales. Image noise was measured using attenuation values of paraspinal muscle tissue. The dose length product (DLP) was statistically significantly lower for LD scans regarding the planning scans (SD: 13.8 ± 8.2 mGycm, LD: 8.1 ± 4.4 mGycm, p < 0.01) and the interventional guidance scans (SD: 43.0 ± 48.8 mGycm, LD: 18.4 ± 7.3 mGycm, p < 0.01). Image quality, contrast, determination of the target structure, and confidence for planning or intervention guidance were rated good to perfect for SD and LD scans, showing no statistically significant differences between SD and LD scans (p > 0.05). Image noise was similar between SD and LD scans performed for planning of the interventional procedures (SD: 14.62 ± 2.83 HU vs. LD: 15.45 ± 3.22 HU, p = 0.24). Use of a LD protocol for MDCT-guided biopsies along the spine is a practical alternative, maintaining overall image quality and confidence. Increasing availability of model-based iterative reconstruction in clinical routine may facilitate further radiation dose reductions.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images

Patrick Thiam / Ludwig Lausser / Christopher Kloth / Daniel Blaich / Andreas Liebold / Meinrad Beer / Hans A. Kestler

Frontiers in Artificial Intelligence, Vol

2023 Volume 6

Abstract: In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various ... ...

Abstract	In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation.
Keywords	chest X-ray ; cardiomegaly ; deep learning ; transfer learning ; unsupervised domain adaptation ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Leveraging human expert image annotations to improve pneumonia differentiation through human knowledge distillation

Daniel Schaudt / Reinhold von Schwerin / Alexander Hafner / Pascal Riedel / Christian Späte / Manfred Reichert / Andreas Hinteregger / Meinrad Beer / Christopher Kloth

Scientific Reports, Vol 13, Iss 1, Pp 1-

2023 Volume 13

Abstract: Abstract In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of ... ...

Abstract	Abstract In medical imaging, deep learning models can be a critical tool to shorten time-to-diagnosis and support specialized medical staff in clinical decision making. The successful training of deep learning models usually requires large amounts of quality data, which are often not available in many medical imaging tasks. In this work we train a deep learning model on university hospital chest X-ray data, containing 1082 images. The data was reviewed, differentiated into 4 causes for pneumonia, and annotated by an expert radiologist. To successfully train a model on this small amount of complex image data, we propose a special knowledge distillation process, which we call Human Knowledge Distillation. This process enables deep learning models to utilize annotated regions in the images during the training process. This form of guidance by a human expert improves model convergence and performance. We evaluate the proposed process on our study data for multiple types of models, all of which show improved results. The best model of this study, called PneuKnowNet, shows an improvement of + 2.3% points in overall accuracy compared to a baseline model and also leads to more meaningful decision regions. Utilizing this implicit data quality-quantity trade-off can be a promising approach for many scarce data domains beyond medical imaging.
Keywords	Medicine ; R ; Science ; Q
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data

Daniel Schaudt / Christian Späte / Reinhold von Schwerin / Manfred Reichert / Marianne von Schwerin / Meinrad Beer / Christopher Kloth

Bioengineering, Vol 10, Iss 12, p

2023 Volume 1421

Abstract: In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial ... ...

Abstract	In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial quantities of high-quality data, a resource often lacking in numerous medical imaging scenarios. One way to overcome this deficiency is to artificially generate such images. Therefore, in this comparative study we train five generative models to artificially increase the amount of available data in such a scenario. This synthetic data approach is evaluated on a a downstream classification task, predicting four causes for pneumonia as well as healthy cases on 1082 chest X-ray images. Quantitative and medical assessments show that a Generative Adversarial Network (GAN)-based approach significantly outperforms more recent diffusion-based approaches on this limited dataset with better image quality and pathological plausibility. We show that better image quality surprisingly does not translate to improved classification performance by evaluating five different classification models and varying the amount of additional training data. Class-specific metrics like precision, recall, and F1-score show a substantial improvement by using synthetic images, emphasizing the data rebalancing effect of less frequent classes. However, overall performance does not improve for most models and configurations, except for a DreamBooth approach which shows a +0.52 improvement in overall accuracy. The large variance of performance impact in this study suggests a careful consideration of utilizing generative models for limited data scenarios, especially with an unexpected negative correlation between image quality and downstream classification improvement.
Keywords	deep learning ; generative models ; medical imaging ; pneumonia ; synthetic data ; Technology ; T ; Biology (General) ; QH301-705.5
Subject code	006
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Detection of caries lesions using a water-sensitive STIR sequence in dental MRI

Egon Burian / Nicolas Lenhart / Tobias Greve / Jannis Bodden / Gintare Burian / Benjamin Palla / Florian Probst / Monika Probst / Meinrad Beer / Matthias Folwaczny / Julian Schwarting

Scientific Reports, Vol 14, Iss 1, Pp 1-

2024 Volume 9

Abstract: Abstract In clinical practice, diagnosis of suspected carious lesions is verified by using conventional dental radiography (DR), including panoramic radiography (OPT), bitewing imaging, and dental X-ray. The aim of this study was to evaluate the use of ... ...

Abstract	Abstract In clinical practice, diagnosis of suspected carious lesions is verified by using conventional dental radiography (DR), including panoramic radiography (OPT), bitewing imaging, and dental X-ray. The aim of this study was to evaluate the use of magnetic resonance imaging (MRI) for caries visualization. Fourteen patients with clinically suspected carious lesions, verified by standardized dental examination including DR and OPT, were imaged with 3D isotropic T2-weighted STIR (short tau inversion recovery) and T1 FFE Black bone sequences. Intensities of dental caries, hard tissue and pulp were measured and calculated as aSNR (apparent signal to noise ratio) and aHTMCNR (apparent hard tissue to muscle contrast to noise ratio) in both sequences. Imaging findings were then correlated to clinical examination results. In STIR as well as in T1 FFE black bone images, aSNR and aHTMCNR was significantly higher in carious lesions than in healthy hard tissue (p < 0.001). Using water-sensitive STIR sequence allowed for detecting significantly lower aSNR and aHTMCNR in carious teeth compared to healthy teeth (p = 0.01). The use of MRI for the detection of caries is a promising imaging technique that may complement clinical exams and traditional imaging.
Keywords	Medicine ; R ; Science ; Q
Subject code	610 ; 616
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: Low-dose multi-detector computed tomography for periradicular infiltrations at the cervical and lumbar spine

Karolin J. Paprottka / Karina Kupfer / Vivian Schultz / Meinrad Beer / Claus Zimmer / Thomas Baum / Jan S. Kirschke / Nico Sollmann

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 11

Abstract: Abstract Periradicular infiltrations are frequently performed in daily neuroradiological routine and are often guided by multi-detector computed tomography (MDCT), thus leading to radiation exposure. The purpose of this study was to evaluate MDCT with ... ...

Abstract	Abstract Periradicular infiltrations are frequently performed in daily neuroradiological routine and are often guided by multi-detector computed tomography (MDCT), thus leading to radiation exposure. The purpose of this study was to evaluate MDCT with low dose (LD) and model-based iterative reconstruction for image-guided periradicular infiltrations at the cervical and lumbosacral spine. We retrospectively analyzed 204 MDCT scans acquired for the purpose of cervical or lumbosacral periradicular interventions, which were either derived from scanning with standard dose (SD; 40 mA and 120 kVp) or LD (20–30 mA and 120 kVp) using a 128-slice MDCT scanner. The SD cases were matched to the LD cases considering sex, age, level of infiltration, presence of spinal instrumentation, and body diameter. All images were reconstructed using model-based iterative image reconstruction and were evaluated by two readers (R1 and R2) using 5- or 3-point Likert scales (score of 1 reflects the best value per category). Furthermore, noise in imaging data was quantitatively measured by the standard deviation (StDev) of muscle tissue. The dose length product (DLP) was statistically significantly lower for LD scans (6.75 ± 6.43 mGycm vs. 10.16 ± 7.70 mGycm; p < 0.01; reduction of 33.5%). Image noise was comparable between LD and SD scans (13.13 ± 3.66 HU vs. 13.37 ± 4.08 HU; p = 0.85). Overall image quality was scored as good to very good with only minimal artifacts according to both readers, and determination of the nerve root was possible in almost all patients (LD vs. SD: p > 0.05 for all items). This resulted in high confidence for intervention planning as well as periprocedural intervention guidance for both SD and LD scans. The inter-reader agreement was at least substantial (weighted Cohen’s κ ≥ 0.62), except for confidence in intervention planning for LD scans (κ = 0.49). In conclusion, considerable dose reduction for planning and performing periradicular infiltrations with MDCT using model-based iterative image ...
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2022-03-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

To top

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED

Full text online

More links

Kategorien

Inter-library loan at ZB MED