PURPOSE: The aim of this study was to develop a simple, robust, and easy-to-use calibration procedure for correcting misalignments in rosette MRI k-space sampling, with the objective of producing images with minimal artifacts. METHODS: Quick automatic calibration scans were proposed for the beginning of the measurement to collect information on the time course of the rosette acquisition trajectory. A two-parameter model was devised to match the measured time-varying readout gradient delays and approximate the actual rosette sampling trajectory. The proposed calibration approach was implemented, and performance assessment was conducted on both phantoms and human subjects. RESULTS: The fidelity of phantom and in vivo images exhibited significant improvement compared with uncorrected rosette data. The two-parameter calibration approach also demonstrated enhanced precision and reliability, as evidenced by quantitative T2*$$ {\mathrm{T}}_2^{\ast } $$ relaxometry analyses. CONCLUSION: Adequate correction of data sampling is a crucial step in rosette MRI. The presented experimental results underscore the robustness, ease of implementation, and suitability for routine experimental use of the proposed two-parameter rosette trajectory calibration approach.
- MeSH
- algoritmy * MeSH
- artefakty * MeSH
- fantomy radiodiagnostické * MeSH
- kalibrace MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- mozek diagnostické zobrazování MeSH
- počítačové zpracování obrazu * metody MeSH
- reprodukovatelnost výsledků MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
PURPOSE: Dual velocity encoding PC-MRI can produce spurious artifacts when using high ratios of velocity encoding values (VENCs), limiting its ability to generate high-quality images across a wide range of encoding velocities. This study aims to propose and compare dual-VENC correction methods for such artifacts. THEORY AND METHODS: Two denoising approaches based on spatiotemporal regularization are proposed and compared with a state-of-the-art method based on sign correction. Accuracy is assessed using simulated data from an aorta and brain aneurysm, as well as 8 two-dimensional (2D) PC-MRI ascending aorta datasets. Two temporal resolutions (30,60) ms and noise levels (9,12) dB are considered, with noise added to the complex magnetization. The error is evaluated with respect to the noise-free measurement in the synthetic case and to the unwrapped image without additional noise in the volunteer datasets. RESULTS: In all studied cases, the proposed methods are more accurate than the Sign Correction technique. Using simulated 2D+T data from the aorta (60 ms, 9 dB), the Dual-VENC (DV) error 0.82±0.07$$ 0.82\pm 0.07 $$ is reduced to: 0.66±0.04$$ 0.66\pm 0.04 $$ (Sign Correction); 0.34±0.04$$ 0.34\pm 0.04 $$ and 0.32±0.04$$ 0.32\pm 0.04 $$ (proposed techniques). The methods are found to be significantly different (p-value <0.05$$ <0.05 $$ ). Importantly, brain aneurysm data revealed that the Sign Correction method is not suitable, as it increases error when the flow is not unidirectional. All three methods improve the accuracy of in vivo data. CONCLUSION: The newly proposed methods outperform the Sign Correction method in improving dual-VENC PC-MRI images. Among them, the approach based on temporal differences has shown the highest accuracy.
- MeSH
- algoritmy * MeSH
- aorta * diagnostické zobrazování MeSH
- artefakty * MeSH
- fantomy radiodiagnostické MeSH
- interpretace obrazu počítačem metody MeSH
- intrakraniální aneurysma diagnostické zobrazování MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- mozek diagnostické zobrazování MeSH
- počítačová simulace MeSH
- počítačové zpracování obrazu * metody MeSH
- poměr signál - šum * MeSH
- reprodukovatelnost výsledků MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
The topic of the diagnosis of phaeochromocytomas remains highly relevant because of advances in laboratory diagnostics, genetics, and therapeutic options and also the development of imaging methods. Computed tomography still represents an essential tool in clinical practice, especially in incidentally discovered adrenal masses; it allows morphological evaluation, including size, shape, necrosis, and unenhanced attenuation. More advanced post-processing tools to analyse digital images, such as texture analysis and radiomics, are currently being studied. Radiomic features utilise digital image pixels to calculate parameters and relations undetectable by the human eye. On the other hand, the amount of radiomic data requires massive computer capacity. Radiomics, together with machine learning and artificial intelligence in general, has the potential to improve not only the differential diagnosis but also the prediction of complications and therapy outcomes of phaeochromocytomas in the future. Currently, the potential of radiomics and machine learning does not match expectations and awaits its fulfilment.
- MeSH
- feochromocytom * diagnostické zobrazování MeSH
- lidé MeSH
- nádory nadledvin * diagnostické zobrazování MeSH
- paragangliom * diagnostické zobrazování MeSH
- počítačová rentgenová tomografie metody MeSH
- počítačové zpracování obrazu metody MeSH
- radiomika MeSH
- strojové učení MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
PURPOSE: This study aimed to compare the image quality of the Siemens Biograph mCT40 (photomultiplier-based system - PMT) and the Siemens Vision600 (silicon photomultiplier-based system - SiPM) using a modified NEMA IEC Body phantom (Data Spectrum). METHODS: SiPM-based Vision600 has a smaller crystal size (3.2 × 3.2 mm vs. 4.0 × 4.0 mm in the PMT-based mCT40), resulting in better spatial resolution. Enhanced time-of-flight (TOF) timing and higher sensitivity leads to nearly four times higher effective sensitivity. The standard NEMA IEC Body phantom was modified with a 3D-printed holder to accommodate also Hollow and Micro Hollow Spheres of 15.4 mm, 12.4 mm, 7.9 mm, 6.2 mm, 5.0 mm, and 4.0 mm. Each of the three acquisition sessions per scanner included 18 time points and spanned 5.6 half-lives to assess system performance at varying activity concentrations in the field of view. RESULTS: Recovery curves for both systems were similar when identical post-reconstruction filters were applied. The SiPM-based Vision600 system detected smaller sources at significantly lower activity concentrations, and the variations in standardized uptake value (SUVmax, SUVA50) measurements were generally smaller compared to those of the PMT-based system. The two smallest sources became undetectable below 63 MBq and 16 MBq on the PMT system, versus 20 MBq and 6.5 MBq on the SiPM system. CONCLUSIONS: SiPM technology demonstrated superior performance compared to PMT in detecting small sources in low-activity scenarios and provided more robust quantification results. It is recommended to use averaged SUV metrics, such as SUVA50 or SUVpeak.
The evaluation of large experimental datasets is a fundamental aspect of research in every scientific field. Streamlining this process can improve the reliability of results while making data analysis more efficient and faster to execute. In biomedical research it is often very important to determine the type of cell death after various treatments. Thus, differentiating between viable, apoptotic, and necrotic cells provide critical insights into the treatment efficacy, a key aspect in the field of drug development. Fluorescent microscopy is perceived as a widely used technique for cell metabolism assessment and can therefore be used to investigate treatment outcomes after staining samples with cell death detection kit. However, accurate evaluation of therapeutic results requires quantitative analysis, often necessitating extensive postprocessing of imaging data. In this study, we introduce a complementary tool designed as a macro for the Fiji platform, enabling the automated postprocessing of fluorescent microscopy images to accurately distinguish and quantify viable, apoptotic, and necrotic cells.
- MeSH
- apoptóza MeSH
- buněčná smrt MeSH
- fluorescenční mikroskopie * metody MeSH
- lidé MeSH
- nekróza MeSH
- počítačové zpracování obrazu * metody MeSH
- software MeSH
- viabilita buněk MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Radiologists utilize pictures from X-rays, magnetic resonance imaging, or computed tomography scans to diagnose bone cancer. Manual methods are labor-intensive and may need specialized knowledge. As a result, creating an automated process for distinguishing between malignant and healthy bone is essential. Bones that have cancer have a different texture than bones in unaffected areas. Diagnosing hematological illnesses relies on correct labeling and categorizing nucleated cells in the bone marrow. However, timely diagnosis and treatment are hampered by pathologists' need to identify specimens, which can be sensitive and time-consuming manually. Humanity's ability to evaluate and identify these more complicated illnesses has significantly been bolstered by the development of artificial intelligence, particularly machine, and deep learning. Conversely, much research and development is needed to enhance cancer cell identification-and lower false alarm rates. We built a deep learning model for morphological analysis to solve this problem. This paper introduces a novel deep convolutional neural network architecture in which hybrid multi-objective and category-based optimization algorithms are used to optimize the hyperparameters adaptively. Using the processed cell pictures as input, the proposed model is then trained with an optimized attention-based multi-scale convolutional neural network to identify the kind of cancer cells in the bone marrow. Extensive experiments are run on publicly available datasets, with the results being measured and evaluated using a wide range of performance indicators. In contrast to deep learning models that have already been trained, the total accuracy of 99.7% was determined to be superior.
PURPOSE: The aim of this study is to design a method of myocardial T1 quantification in small laboratory animals and to investigate the effects of spatiotemporal regularization and the needed acquisition duration. METHODS: We propose a compressed-sensing approach to T1 quantification based on self-gated inversion-recovery radial two/three-dimensional (2D/3D) golden-angle stack-of-stars acquisition with image reconstruction performed using total-variation spatiotemporal regularization. The method was tested on a phantom and on a healthy rat, as well as on rats in a small myocardium-remodeling study. RESULTS: The results showed a good match of the T1 estimates with the results obtained using the ground-truth method on a phantom and with the literature values for rats myocardium. The proposed 2D and 3D methods showed significant differences between normal and remodeling myocardium groups for acquisition lengths down to approximately 5 and 15 min, respectively. CONCLUSIONS: A new 2D and 3D method for quantification of myocardial T1 in rats was proposed. We have shown the capability of both techniques to distinguish between normal and remodeling myocardial tissue. We have shown the effects of image-reconstruction regularization weights and acquisition length on the T1 estimates.
- MeSH
- fantomy radiodiagnostické MeSH
- krysa rodu rattus MeSH
- magnetická rezonanční tomografie metody MeSH
- myokard * MeSH
- počítačové zpracování obrazu metody MeSH
- reprodukovatelnost výsledků MeSH
- zobrazování trojrozměrné * metody MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.
Pituitary adenomas (PA) represent the most common type of sellar neoplasm. Extracting relevant information from radiological images is essential for decision support in addressing various objectives related to PA. Given the critical need for an accurate assessment of the natural progression of PA, computer vision (CV) and artificial intelligence (AI) play a pivotal role in automatically extracting features from radiological images. The field of "Radiomics" involves the extraction of high-dimensional features, often referred to as "Radiomic features," from digital radiological images. This survey offers an analysis of the current state of research in PA radiomics. Our work comprises a systematic review of 34 publications focused on PA radiomics and other automated information mining pertaining to PA through the analysis of radiological data using computer vision methods. We begin with a theoretical exploration essential for understanding the theoretical background of radionmics, encompassing traditional approaches from computer vision and machine learning, as well as the latest methodologies in deep radiomics utilizing deep learning (DL). Thirty-four research works under examination are comprehensively compared and evaluated. The overall results achieved in the analyzed papers are high, e.g., the best accuracy is up to 96% and the best achieved AUC is up to 0.99, which establishes optimism for the successful use of radiomic features. Methods based on deep learning seem to be the most promising for the future. In relation to this perspective DL methods, several challenges are remarkable: It is important to create high-quality and sufficiently extensive datasets necessary for training deep neural networks. Interpretability of deep radiomics is also a big open challenge. It is necessary to develop and verify methods that will explain to us how deep radiomic features reflect various physics-explainable aspects.
- MeSH
- adenom * diagnostické zobrazování MeSH
- deep learning MeSH
- lidé MeSH
- nádory hypofýzy * diagnostické zobrazování MeSH
- počítačové zpracování obrazu metody MeSH
- radiomika MeSH
- strojové učení MeSH
- umělá inteligence MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- systematický přehled MeSH
In today's biometric and commercial settings, state-of-the-art image processing relies solely on artificial intelligence and machine learning which provides a high level of accuracy. However, these principles are deeply rooted in abstract, complex "black-box systems". When applied to forensic image identification, concerns about transparency and accountability emerge. This study explores the impact of two challenging factors in automated facial identification: facial expressions and head poses. The sample comprised 3D faces with nine prototype expressions, collected from 41 participants (13 males, 28 females) of European descent aged 19.96 to 50.89 years. Pre-processing involved converting 3D models to 2D color images (256 × 256 px). Probes included a set of 9 images per individual with head poses varying by 5° in both left-to-right (yaw) and up-and-down (pitch) directions for neutral expressions. A second set of 3,610 images per individual covered viewpoints in 5° increments from -45° to 45° for head movements and different facial expressions, forming the targets. Pair-wise comparisons using ArcFace, a state-of-the-art face identification algorithm yielded 54,615,690 dissimilarity scores. Results indicate that minor head deviations in probes have minimal impact. However, the performance diminished as targets deviated from the frontal position. Right-to-left movements were less influential than up and down, with downward pitch showing less impact than upward movements. The lowest accuracy was for upward pitch at 45°. Dissimilarity scores were consistently higher for males than for females across all studied factors. The performance particularly diverged in upward movements, starting at 15°. Among tested facial expressions, happiness and contempt performed best, while disgust exhibited the lowest AUC values.
- MeSH
- algoritmy * MeSH
- automatizované rozpoznávání obličeje * metody MeSH
- biometrická identifikace metody MeSH
- dospělí MeSH
- hlava - pohyby fyziologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- obličej anatomie a histologie MeSH
- počítačové zpracování obrazu metody MeSH
- postura těla fyziologie MeSH
- výraz obličeje * MeSH
- zobrazování trojrozměrné MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
