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
- Klíčová slova
- systém Genius,
- MeSH
- časná detekce nádoru metody přístrojové vybavení MeSH
- digitální technologie klasifikace metody MeSH
- lidé MeSH
- nádory děložního čípku diagnostické zobrazování diagnóza prevence a kontrola MeSH
- Papanicolaouův test * metody přístrojové vybavení MeSH
- počítačové zpracování obrazu metody přístrojové vybavení MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- ženské pohlaví MeSH
- Publikační typ
- přehledy MeSH
- Geografické názvy
- Česká republika MeSH
This work illustrates a novel application of a supervised superpixel-based segmentation method for root micrograph classification and total fungal colonization rate estimation. Two procedures relying on successive classifier application on different root micrographs or on the same micrograph but with an increasing number of labels to be assigned to each picture element category are compared to a reference grid-intersect count method. Finally, supervised classification with at least 16 labels on the same picture appears as a convenient method for obtaining rapid and confident colonization rate estimates. We suggest this kind of method may be easily and routinely implemented for research or educational purposes.
The study evaluates the efficacy of RETROICOR (Retrospective Image Correction) in mitigating physiological artifacts within multi-echo (ME) fMRI data. Two RETROICOR implementations were compared: applying corrections to individual echoes (RTC_ind) versus composite multi-echo data (RTC_comp). Data from 50 healthy participants were collected using diverse acquisition parameters, including multiband acceleration factors and varying flip angles, on a Siemens Prisma 3T scanner. Key metrics such as temporal signal-to-noise ratio (tSNR), signal fluctuation sensitivity (SFS), and variance of residuals demonstrated improved data quality in both RETROICOR models, particularly in moderately accelerated runs (multiband factors 4 and 6) with lower flip angles (45°). Differences between RTC_ind and RTC_comp were minimal, suggesting both methods are viable for practical applications. While the highest acceleration (multiband factor 8) degraded data quality, RETROICOR's compatibility with faster acquisition sequences was confirmed. These findings underscore the importance of optimizing acquisition parameters and noise correction techniques for reliable fMRI investigations.
- MeSH
- artefakty * MeSH
- dospělí MeSH
- lidé MeSH
- magnetická rezonanční tomografie * metody MeSH
- mapování mozku * metody MeSH
- mladý dospělý MeSH
- mozek * diagnostické zobrazování fyziologie MeSH
- počítačové zpracování obrazu * metody MeSH
- poměr signál - šum MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
Achieving a reliable and accurate biomedical image segmentation is a long-standing problem. In order to train or adapt the segmentation methods or measure their performance, reference segmentation masks are required. Usually gold-standard annotations, i.e. human-origin reference annotations, are used as reference although they are very hard to obtain. The increasing size of the acquired image data, large dimensionality such as 3D or 3D + time, limited human expert time, and annotator variability, typically result in sparsely annotated gold-standard datasets. Reliable silver-standard annotations, i.e. computer-origin reference annotations, are needed to provide dense segmentation annotations by fusing multiple computer-origin segmentation results. The produced dense silver-standard annotations can then be either used as reference annotations directly, or converted into gold-standard ones with much lighter manual curation, which saves experts' time significantly. We propose a novel full-resolution multi-rater fusion convolutional neural network (CNN) architecture for biomedical image segmentation masks, called DeepFuse, which lacks any down-sampling layers. Staying everywhere at the full resolution enables DeepFuse to fully benefit from the enormous feature extraction capabilities of CNNs. DeepFuse outperforms the popular and commonly used fusion methods, STAPLE, SIMPLE and other majority-voting-based approaches with statistical significance on a wide range of benchmark datasets as demonstrated on examples of a challenging task of 2D and 3D cell and cell nuclei instance segmentation for a wide range of microscopy modalities, magnifications, cell shapes and densities. A remarkable feature of the proposed method is that it can apply specialized post-processing to the segmentation masks of each rater separately and recover under-segmented object parts during the refinement phase even if the majority of inputs vote otherwise. Thus, DeepFuse takes a big step towards obtaining fast and reliable computer-origin segmentation annotations for biomedical images.
- MeSH
- lidé MeSH
- neuronové sítě * MeSH
- počítačové zpracování obrazu * metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVES: Annotating carious lesions on images is challenging. For artificial intelligence (AI) applications, the aggregation of heterogeneous multi-examiner annotations into one single annotation (e.g. via majority voting, MV) is usually needed. We assessed different aggregation strategies for multi-examiner annotations of primary proximal carious lesions on orthoradial radiographs and Near-Infrared Light Transillumination (NILT) images. METHODS: A total of 1007 proximal surfaces from 522 extracted posterior teeth were assessed by five dentists. Histological analysis provided the gold standard. Surfaces were classified as (1) sound, (2) enamel lesion or (3) dentin lesion. Four label aggregation strategies - MV, Weighted Majority Voting (WMV), Dawid-Skene (DS), and multi-annotator competence estimation (MACE) - were applied to unimodal (radiographs, NILT) and multimodal (combined) datasets. The area under the receiver operating characteristic curve (AUROC) was the primary outcome metric. RESULTS: According to the gold standard, 637 (63 %) surfaces were sound, 280 (28 %) showed carious lesions limited to the enamel, and 90 (9 %) showed lesions extending into the dentin. For radiographs, aggregation using MACE outperformed MV, WMV and DS significantly across all lesion depths (p < 0.002). For NILT, MACE significantly outperformed MV across all lesion depths (p < 0.001) and DS for enamel and dentin lesions (p ≤ 0.002). In the multimodal dataset, DS outperformed the other label aggregation strategies across all lesion depths significantly (p < 0.05). CONCLUSIONS: The commonly applied MV may be suboptimal. There is a need for informed application of specific aggregation strategies, depending on the dataset characteristics. CLINICAL SIGNIFICANCE: Most AI applications for dental image analysis are trained on a single annotation, usually resulting from aggregated multi-examiner annotations of each image. However, since these annotations are usually aggregated in an in vivo setting where no definitive ground truth is available, the choice of aggregation strategy plays a crucial role.
- MeSH
- dentin patologie diagnostické zobrazování MeSH
- lidé MeSH
- počítačové zpracování obrazu * metody MeSH
- rentgendiagnostika zubní MeSH
- ROC křivka MeSH
- transiluminace MeSH
- umělá inteligence MeSH
- zubní kaz * diagnostické zobrazování patologie MeSH
- zubní sklovina diagnostické zobrazování patologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3D18F-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site. METHOD: A publicly available lesion-annotated dataset of 1014 whole-body FDG-PET/CT images was used to train, validate, and test (70:10:20) eight configurations with 3D U-Net as the backbone architecture. The best-performing model on the test set was further evaluated on 3 different unseen cohorts consisting of osteosarcoma or neuroblastoma (OS cohort) (n = 13), pediatric solid tumors (ST cohort) (n = 14), and adult Pheochromocytoma/Paraganglioma (PHEO cohort) (n = 40). Both lesion-level and patient-level statistical analyses were conducted to validate the performance of the model on different cohorts. RESULTS: The best performing 3D full resolution nnUNet model achieved a lesion-level sensitivity and DISC of 71.70 % and 0.40 for the test set, 97.83 % and 0.73 for ST, 40.15 % and 0.36 for OS, and 78.37 % and 0.50 for the PHEO cohort. For the test set and PHEO cohort, the model has missed small volume and lower uptake lesions (p < 0.01), whereas no statistically significant differences (p > 0.05) were found in the false positive (FP) and false negative lesions volume and uptake for the OS and ST cohort. The predicted total lesion glycolysis is slightly higher than the ground truth because of FP calls, which experts can easily check and reject. CONCLUSION: The developed deep learning-based automated lesion segmentation AI model which utilizes 3D_FullRes configuration of the nnUNet framework showed promising and reliable performance for the whole-body FDG-PET/CT images.
- MeSH
- celotělové zobrazování * metody MeSH
- deep learning * MeSH
- dítě MeSH
- dospělí MeSH
- fluorodeoxyglukosa F18 * MeSH
- kohortové studie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- nádory * diagnostické zobrazování MeSH
- PET/CT * metody MeSH
- počítačové zpracování obrazu * metody MeSH
- Check Tag
- dítě MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- validační studie MeSH
OBJECTIVES: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML. METHODS: After segmenting bitewings into classes, i.e. dental structures, restorations, and background, the pixel-level representation of implants in the training set (1543 bitewings) and testing set (177 bitewings) was 0.03 % and 0.07 %, respectively. A diffusion model and a generative adversarial network (pix2pix) were used to generate a dataset synthetically enriched in implants. A U-Net segmentation model was trained on (1) the original dataset, (2) the synthetic dataset, (3) on the synthetic dataset and fine-tuned on the original dataset, or (4) on a dataset which was naïvely oversampled with images containing implants. RESULTS: U-Net trained on the original dataset was unable to segment implants in the testing set. Model performance was significantly improved by naïve over-sampling, achieving the highest precision. The model trained only on synthetic data performed worse than naïve over-sampling in all metrics, but with fine-tuning on original data, it resulted in the highest Dice score, recall, F1 score and ROC AUC, respectively. The performance on other classes than implants was similar for all strategies except training only on synthetic data, which tended to perform worse. CONCLUSIONS: The use of synthetic data alone may deteriorate the performance of segmentation models. However, fine-tuning on original data could significantly enhance model performance, especially for heavily underrepresented classes. CLINICAL SIGNIFICANCE: This study explored the use of synthetic data to enhance segmentation of bitewing radiographs, focusing on underrepresented classes like implants. Pre-training on synthetic data followed by fine-tuning on original data yielded the best results, highlighting the potential of synthetic data to advance AI-driven dental imaging and ultimately support clinical decision-making.
- MeSH
- lidé MeSH
- počítačové zpracování obrazu * metody MeSH
- strojové učení * MeSH
- zubní implantáty MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
