OBJECTIVESThe aim of this study was to investigate the robustness and reproducibility of radiomic features in different magnetic resonance imaging sequences.
MATERIALS AND METHODSA phantom was ...scanned on a clinical 3 T system using fluid-attenuated inversion recovery (FLAIR), T1-weighted (T1w), and T2-weighted (T2w) sequences with low and high matrix size. For retest data, scans were repeated after repositioning of the phantom. Test and retest datasets were segmented using a semiautomated approach. Intraobserver and interobserver comparison was performed. Radiomic features were extracted after standardized preprocessing of images. Test-retest robustness was assessed using concordance correlation coefficients, dynamic range, and Bland-Altman analyses. Reproducibility was assessed by intraclass correlation coefficients.
RESULTSThe number of robust features (concordance correlation coefficient and dynamic range ≥ 0.90) was higher for features calculated from FLAIR than from T1w and T2w images. High-resolution FLAIR images provided the highest percentage of robust features (n = 37/45, 81%). No considerable difference in the number of robust features was observed between low- and high-resolution T1w and T2w images (T1w lown = 26/45, 56%; T1w highn = 25/45, 54%; T2 lown = 21/45, 46%; T2 highn = 24/45, 52%). A total of 15 (33%) of 45 features showed excellent robustness across all sequences and demonstrated excellent intraobserver and interobserver reproducibility (intraclass correlation coefficient ≥ 0.75).
CONCLUSIONSFLAIR delivers the most robust substrate for radiomic analyses. Only 15 of 45 features showed excellent robustness and reproducibility across all sequences. Care must be taken in the interpretation of clinical studies using nonrobust features.
Point Clouds (PCs) have recently been adopted as the preferred data structure for representing 3D visual contents. Examples of Point Cloud (PC) applications range from 3D representations of small ...objects up to large scenes, both still or dynamic in time. PC adoption triggered the development of new coding, transmission, and display methodologies that culminated in new international standards for PC compression. Along with these, in the last couple of years, novel methods have been developed for evaluating the visual quality of PC contents. This paper presents a new objective full-reference visual quality assessment metric for static PC contents, named BitDance, which uses color and geometry texture descriptors. The proposed method first extracts the statistics of color and geometry information of the reference and test PCs. Then, it compares the color and geometry statistics and combines them to estimate the perceived quality of the test PC. Using publicly available PC quality assessment datasets, we show that the proposed PC quality assessment metric performs very well when compared to state-of-the-art quality metrics. In particular, the method performs well for different types of PC datasets, including the ones where both geometry and color are not degraded with similar intensities. BitDance is a low complexity algorithm, with an optimized C++ source code that is available for download at github.com/rafael2k/bitdance-pc_metric .
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•Develop a representative model for the welded plate as two plate substructures coupled using dashpots and springs.•Determine weld joint stiffness using IESM based model ...updating.•Identify the weld joint damping using the Lancaster method.•Correlate the weld joint characteristics with material properties obtained from tensile test and texture analysis.
Welded structures are integral to complex bodies such as ships and offshore rigs. Welded joints are frequently used in connecting various plates and substructures, making their assessment pivotal. The numerical model used here consists of plates coupled using springs and dashpots. The coupling models the welded joint and exemplifies the uncertainties in the welding process. The joint identification algorithm systematically defines the coupling in two steps: first, by updating the model for spring stiffness and later, by identifying the dashpot coefficients. The dynamic characteristic is determined using a frequency response function (FRF). After model updating and damping identification, a comparison between the updated FRF and that obtained from the experimental modal test is carried out to ascertain the efficiency of the algorithm. Further, the test structure is subjected to experimental testing to correlate the results from the model updating with material properties. The experimental study includes tensile testing of the weld joints and microstructural analysis. The characterisation is carried out by partitioning the welded joint. The joint stiffness and damping are correlated to the stress–strain response and crystallographic texture of the welded plate.
High-energy synchrotron radiation has been used to study
annealing of cold-rolled Cu and Ti. The measurements were performed using a high-vacuum furnace in transmission geometry and an area detector. ...The diffraction images were subsequently processed to extract the orientation distribution. The recrystallization process could be followed with a time resolution of the order of 10 s, and good pole figures could be obtained from the very limited amount of data in single diffraction images. The pole figures compare favorably with pole figures of the same material measured
with a conventional pole figure goniometer. For Cu, a rapid and complete change from a typical rolling texture to a cube texture was observed after annealing for 20 min at 650 °C. For Ti, changes were more subtle with a tendency for
-axes to diminish near the normal direction, as well as for
-axes to become aligned with the rolling direction. The method makes it feasible to study the kinetics of recrystallization with quantitative texture analysis.
OBJECTIVESThe aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non–contrast-enhanced low radiation dose cardiac ...computed tomography (CCT) images.
MATERIALS AND METHODSIn this institutional review board–approved retrospective study, we included non–contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model Icontrols versus acute MI versus chronic MI; Model IIcontrols versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were useddecision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI.
RESULTSIn Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images.
CONCLUSIONSThis study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologistsʼ eye.
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•A quantitative and conventional method has been developed for FDM filament characterization.•Pure polymer and drug loaded filaments have been used to demonstrate printability ...assessment.•A parameter, toughness, was predictive of successful filament printability.•This assessment tool could be potentially applied to filament development and quality control.
Lack of a conventional quantitative characterization method for filament printability has been recognized as a critical barrier to fused deposition modeling (FDM) 3D printing application. In this study, a small molecule drug, indomethacin, was utilized as a model compound. Polymers with various solubility were mixed with model drug and extruded into filaments using hot melt extrusion method. Thirty-two filaments with or without indomethacin were evaluated by texture analyzer to study the correlation between mechanical properties and the printability. Three different texture analysis methods were utilized and compared, and a parameter “toughness” calculated by stiffness test was identified to quantitatively describe the printability of filaments in the FDM 3D printer. The toughness threshold value of printable filament was defined as a process window of certain FDM printing. This study provides a quantitative way to evaluate and predict filament printability, and it has great potential to be applied to FDM filament development and quality control in the pharmaceutical industry.
The impact of PET image acquisition and reconstruction parameters on SUV measurements or radiomic feature values is widely documented. This scanner effect is detrimental to the design and validation ...of predictive or prognostic models and limits the use of large multicenter cohorts. To reduce the impact of this scanner effect, the ComBat method has been proposed and is now used in various contexts. The purpose of this article is to explain and illustrate the use of ComBat based on practical examples. We also give examples in which the ComBat assumptions are not met and, thus, in which ComBat should not be used.
Retinal Nerve Fiber Layer Optical Texture Analysis Su, Clarice Kai-Ying; Guo, Philip Yawen; Chan, Poemen Pui Man ...
Ophthalmology (Rochester, Minn.),
October 2023, Letnik:
130, Številka:
10
Journal Article
Recenzirano
Odprti dostop
To apply retinal nerve fiber layer (RNFL) optical texture analysis (ROTA) to investigate the prevalence, patterns, and risk factors of RNFL defects in patients with ocular hypertension (OHT) who ...showed normal optic disc and RNFL configuration in clinical examination, normal RNFL thickness on OCT analysis, and normal visual field (VF) results.
Cross-sectional study.
Six hundred eyes of 306 patients with OHT.
All participants underwent clinical examination of the optic disc and RNFL, OCT RNFL imaging, and 24-2 standard automated perimetry. To detect RNFL defects, ROTA was applied. The risk score for glaucoma development was calculated according to the Ocular Hypertension Treatment Study and European Glaucoma Prevention Study (OHTS-EGPS) risk prediction model. Risk factors associated with RNFL defects were analyzed using multilevel logistic regression analysis.
Prevalence of RNFL defects.
The average intraocular pressure (IOP) measured from 3 separate visits within 6 months was 24.9 ± 1.8 mmHg for the eye with higher IOP and 23.7 ± 1.7 mmHg for the eye with lower IOP; the respective central corneal thicknesses were 568.7 ± 30.8 μm and 568.8 ± 31.2 μm. Of 306 patients with OHT, 10.8% (33 patients, 37 eyes) demonstrated RNFL defects in ROTA in at least 1 eye. Of the 37 eyes with RNFL defects, the superior arcuate bundle was the most frequently involved (62.2%), followed by the superior papillomacular bundle (27.0%) and the inferior papillomacular bundle (21.6%). Papillofoveal bundle defects were observed in 10.8% of eyes. The smallest RNFL defect spanned 0.0° along Bruch’s membrane opening margin, whereas the widest RNFL defect extended over 29.3°. Age (years) (odds ratio OR, 1.08; 95% confidence interval CI, 1.03–1.13), VF pattern standard deviation (decibels dB) (OR, 1.82; 95% CI, 1.01–3.29), cup volume (mm3) (OR, 1.24; 95% CI, 1.01–1.53), and the OHTS-EPGS risk score (OR, 1.04; 95% CI, 1.01–1.07) were associated with RNFL defects.
A considerable proportion of patients with OHT who showed no signs of optic disc and RNFL thickness abnormalities on clinical and OCT examination exhibited RNFL defects on ROTA. Axonal fiber bundle defects on ROTA may represent the earliest discernible sign of glaucoma in the glaucoma continuum.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
Purpose
The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological and non-oncological ...applications, in order to assess how far the image mining research stands from routine medical application. To do this, we applied a trial phases classification inspired from the drug development process.
Methods
Among the articles we considered for inclusion from PubMed were multimodality AI and radiomics investigations, with a validation analysis aimed at relevant clinical objectives. Quality assessment of selected papers was performed according to the QUADAS-2 criteria. We developed the phases classification criteria for image mining studies.
Results
Overall 34,626 articles were retrieved, 300 were selected applying the inclusion/exclusion criteria, and 171 high-quality papers (QUADAS-2 ≥ 7) were identified and analysed. In 27/171 (16%), 141/171 (82%), and 3/171 (2%) studies the development of an AI-based algorithm, radiomics model, and a combined radiomics/AI approach, respectively, was described. A total of 26/27(96%) and 1/27 (4%) AI studies were classified as phase II and III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), and 7/141 (5%) radiomics studies were classified as phase 0, I, II, and III, respectively. All three radiomics/AI studies were categorised as phase II trials.
Conclusions
The results of the studies are promising but still not mature enough for image mining tools to be implemented in the clinical setting and be widely used. The transfer learning from the well-known drug development process, with some specific adaptations to the image mining discipline could represent the most effective way for radiomics and AI algorithms to become the standard of care tools.
To explore the use of texture analysis (TA) features of patients’ 3D dose distributions to improve prediction modelling of treatment complication rates in prostate cancer radiotherapy.
Late toxicity ...scores, dose distributions, and non-treatment related (NTR) predictors for late toxicity, such as age and baseline symptoms, of 351 patients of the hypofractionation arm of the HYPRO randomized trial were used in this study. Apart from DVH parameters, also TA features of rectum and bladder 3D dose distributions were used for predictive modelling of gastrointestinal (GI) and genitourinary (GU) toxicities. Logistic Normal Tissue Complication Probability (NTCP) models were derived, using only NTR parameters, NTR + DVH, NTR + TA, and NTR + DVH + TA.
For rectal bleeding, the area under the curve (AUC) for using only NTR parameters was 0.58, which increased to 0.68, and 0.73, when adding DVH or TA parameters respectively. For faecal incontinence, the AUC went up from 0.63 (NTR only), to 0.68 (+DVH) and 0.73 (+TA). For nocturia, adding TA features resulted in an AUC increase from 0.64 to 0.66, while no improvement was seen when including DVH parameters in the modelling. For urinary incontinence, the AUC improved from 0.68 to 0.71 (+DVH) and 0.73 (+TA). For GI, model improvements resulting from adding TA parameters to NTR instead of DVH were statistically significant (p < 0.04).
Inclusion of 3D dosimetric texture analysis features in predictive modelling of GI and GU toxicity rates in prostate cancer radiotherapy improved prediction performance, which was statistically significant for GI.
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