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Total ankle arthroplasty (TAA) is an effective alternative for treating patients with end-stage ankle degeneration, improving mobility, and providing pain relief. Implant survivorship is constantly improving; however, complications occur. Many causes of pain and dysfunction after total ankle arthroplasty can be diagnosed accurately with clinical examination, laboratory, radiography, and computer tomography. However, when there are no or inconclusive imaging findings, magnetic resonance imaging (MRI) is highly accurate in identifying and characterizing bone resorption, osteolysis, infection, osseous stress reactions, nondisplaced fractures, polyethylene damage, nerve injuries and neuropathies, as well as tendon and ligament tears. Multiple vendors offer effective, clinically available MRI techniques for metal artifact reduction MRI of total ankle arthroplasty. This article reviews the MRI appearances of common TAA implant systems, clinically available techniques and protocols for metal artifact reduction MRI of TAA implants, and the MRI appearances of a broad spectrum of TAA-related complications.
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Artroplastia de Reemplazo de Tobillo , Prótesis Articulares , Imagen por Resonancia Magnética , Humanos , Artroplastia de Reemplazo de Tobillo/efectos adversos , Imagen por Resonancia Magnética/métodos , Prótesis Articulares/efectos adversos , Articulación del Tobillo/cirugía , Articulación del Tobillo/diagnóstico por imagen , Dolor Postoperatorio/etiología , Diseño de Prótesis , Masculino , Artefactos , Femenino , Falla de PrótesisRESUMEN
The use of marker-based optical motion capture to estimate joint kinematics during gait is currently limited by errors associated with soft-tissue-induced motion artefacts (STIMA) and ambiguity in landmark palpation. This study therefore presents a novel protocol aiming to Minimize Knee Soft-Tissue Artefacts (MiKneeSoTA) and their effect on kinematic estimates. Relying on an augmented marker set and a new inverse kinematics approach, our method leverages frame-by-frame optimization to adjust best-fit cylinders that have been automatically generated based on the relative position of lower limb markers during an initial static trial. Tibiofemoral rotations and translations are then calculated along the anatomical joint axes based on the relative 3D motion of these cylinders. When compared against the conventional Helen-Hayes approach, in vivo assessment of fifteen healthy subjects revealed the MiKneeSoTA approach led to kinematic profiles with significantly lower standard deviations in joint rotations across trials, and even visibly reduced the presence of high frequency fluctuations presumably associated with e.g. soft-tissue vibration. In addition to agreeing with previously published bone pin and fluoroscopy datasets, our results illustrate MiKneeSoTA's ability to abate the effect of STIMA induced by lateral knee ligaments. Our findings indicate that MiKneeSoTA is in fact a promising approach to mitigate knee joint STIMA and thus enable the previously unattainable accurate estimation of translational knee joint motion with an optoelectronic system.
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Artefactos , Articulación de la Rodilla , Humanos , Fenómenos Biomecánicos , Articulación de la Rodilla/fisiología , Masculino , Adulto , Femenino , Rango del Movimiento Articular/fisiología , Marcha/fisiologíaRESUMEN
The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.
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Algoritmos , Artefactos , Oximetría , Saturación de Oxígeno , Fotopletismografía , Procesamiento de Señales Asistido por Computador , Fotopletismografía/métodos , Fotopletismografía/instrumentación , Oximetría/métodos , Oximetría/instrumentación , Humanos , Análisis de los Mínimos Cuadrados , Movimiento (Física) , Oxígeno/sangreRESUMEN
Different types of noise contaminating the surface electromyogram (EMG) signal may degrade the recognition performance. For noise removal, the type of noise has to first be identified. In this paper, we propose a real-time efficient system for identifying a clean EMG signal and noisy EMG signals contaminated with any one of the following three types of noise: electrocardiogram interference, spike noise, and power line interference. Two statistical descriptors, kurtosis and skewness, are used as input features for the cascading quadratic discriminant analysis classifier. An efficient simplification of kurtosis and skewness calculations that can reduce computation time and memory storage is proposed. The experimental results from the real-time system based on an ATmega 2560 microcontroller demonstrate that the kurtosis and skewness values show root mean square errors between the traditional and proposed efficient techniques of 0.08 and 0.09, respectively. The identification accuracy with five-fold cross-validation resulting from the quadratic discriminant analysis classifier is 96.00%.
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Electromiografía , Procesamiento de Señales Asistido por Computador , Electromiografía/métodos , Factores de Tiempo , Humanos , Análisis Discriminante , Artefactos , Relación Señal-RuidoRESUMEN
The dispersal of Homo sapiens across Eurasia during MIS 3 in the Late Pleistocene is marked by technological shifts and other behavioral changes, known in the archaeological record under the term of Initial Upper Paleolithic (IUP). Bacho Kiro Cave in north Bulgaria, re-excavated by us from 2015 to 2021, is one of the reference sites for this phenomenon. The newly excavated lithic assemblages dated by radiocarbon between 45,040 and 43,280 cal BP and attributed to Homo sapiens encompass more than two thousand lithic artifacts. The lithics, primarily from Layer N1-I, exist amid diverse fauna remains, human fossils, pierced animal teeth pendants, and sediment with high organic content. This article focuses on the technological aspects of the IUP lithics, covering raw material origin and use-life, blank production, on-site knapping activities, re-flaking of lithic implements, and the state of retouched lithic components. We apply petrography for the identification of silicites and other used stones. We employ chaîne opératoire and reduction sequence approaches to profile the lithics techno-typologically and explore the lithic economy, particularly blade production methods, knapping techniques, and artifact curation. Raw material analysis reveals Lower Cretaceous flints from Ludogorie and Upper Cretaceous flints from the Danube region, up to 190 km and 130 km, respectively, from Bacho Kiro Cave, indicating long-distance mobility and finished products transport. Imported lithic implements, were a result of unidirectional and bidirectional non-Levallois laminar technology, likely of volumetric concept. Systematic on-anvil techniques (bipolar knapping) and tool segmentation indicate re-flaking and reshaping of lithic implements, reflecting on-site curation and multifaceted lithic economy. A limited comparison with other IUP sites reveals certain shared features and also regional variations. Bacho Kiro Cave significantly contributes to understanding the technological and behavioral evolution of early Homo sapiens in western Eurasia.
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Arqueología , Cuevas , Fósiles , Humanos , Bulgaria , Animales , Tecnología/historia , Sedimentos Geológicos/análisis , ArtefactosRESUMEN
Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England's 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an "FFPEImpact" score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many.
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Formaldehído , Neoplasias , Adhesión en Parafina , Fijación del Tejido , Secuenciación Completa del Genoma , Humanos , Adhesión en Parafina/métodos , Neoplasias/genética , Neoplasias/patología , Secuenciación Completa del Genoma/métodos , Fijación del Tejido/métodos , Genómica/métodos , Mutación , Genoma Humano , ArtefactosRESUMEN
Objective.We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.Approach.Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.Main results.Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.Significance.The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.
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Fotopletismografía , Procesamiento de Señales Asistido por Computador , Aprendizaje Automático no Supervisado , Fotopletismografía/métodos , Humanos , Femenino , Masculino , Persona de Mediana Edad , Artefactos , Anciano , AdultoRESUMEN
Spectral Photon Counting Computed Tomography (SPCCT), a ground-breaking development in CT technology, has immense potential to address the persistent problem of metal artefacts in CT images. This study aims to evaluate the potential of Mars photon-counting CT technology in reducing metal artefacts. It focuses on identifying and quantifying clinically significant materials in the presence of metal objects. A multi-material phantom was used, containing inserts of varying concentrations of hydroxyapatite (a mineral present in teeth, bones, and calcified plaque), iodine (used as a contrast agent), CT water (to mimic soft tissue), and adipose (as a fat substitute). Three sets of scans were acquired: with aluminium, with stainless steel, and without a metal insert as a reference dataset. Data acquisition was performed using a Mars SPCCT scanner (Microlab 5×120); operated at 118 kVp and 80 µA. The images were subsequently reconstructed into five energy bins: 7-40, 40-50, 50-60, 60-79, and 79-118 keV. Evaluation metrics including signal-to-noise ratio (SNR), linearity of attenuation profiles, root mean square error (RMSE), and area under the curve (AUC) were employed to assess the energy and material-density images with and without metal inserts. Results show decreased metal artefacts and a better signal-to-noise ratio (up to 25%) with increased energy bins as compared to reference data. The attenuation profile also demonstrated high linearity (R2 >0.95) and lower RMSE across all material concentrations, even in the presence of aluminium and steel. Material identification accuracy for iodine and hydroxyapatite (with and without metal inserts) remained consistent, minimally impacting AUC values. For demonstration purposes, the biological sample was also scanned with the stainless steel volar implant and cortical bone screw, and the images were objectively assessed to indicate the potential effectiveness of SPCCT in replicating real-world clinical scenarios.
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Metales , Fantasmas de Imagen , Fotones , Tomografía Computarizada por Rayos X , Tomografía Computarizada por Rayos X/métodos , Metales/análisis , Metales/química , Humanos , Relación Señal-Ruido , Artefactos , Yodo/análisis , Durapatita/análisisRESUMEN
Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.
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Algoritmos , Artefactos , Electromiografía , Mano , Movimiento , Reconocimiento de Normas Patrones Automatizadas , Procesamiento de Señales Asistido por Computador , Humanos , Electromiografía/métodos , Mano/fisiología , Reconocimiento de Normas Patrones Automatizadas/métodos , Masculino , Contracción Muscular , Adulto , Miembros Artificiales , Femenino , Movimiento (Física) , Músculo Esquelético/fisiologíaRESUMEN
BACKGROUND AND OBJECTIVE: Registration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues. METHODS: To overcome the influence of the parenchymal changes, a divide-and-conquer approach rooted in the coherent point drift (CPD) paradigm was proposed. The proposed method was based on two kernel ideas. One was the idea of component structure wise registration. Specifically, the proposed method relaxed the intrinsic assumption of equal isotropic covariances in CPD by decomposing a lung and its surrounding tissues into component structures and independently registering the component structures pairwise by CPD. The other was the idea of defining a vascular subtree centered at a matched branch point as a component structure. This idea could not only provide a sufficient number of matched feature points within a parenchyma, but avoid being corrupted by the false feature points resided in the RILD tissues due to globally and indiscriminately sampling using mathematical operators. The overall deformation model was built by using the Thin Plate Spline based on all matched points. RESULTS: This study recruited 30 pairs of lung CT images with RILD, 15 of which were used for internal validation (leave-one-out cross-validation) and the other 15 for external validation. The experimental results showed that the proposed algorithm achieved a mean and a mean of maximum 1 % of average surface distances <2 and 8 mm, respectively, and a mean and a maximum target registration error <2 mm and 5 mm on both internal and external validation datasets. The paired two-sample t-tests corroborated that the proposed algorithm outperformed a recent method, the Stavropoulou's method, on the external validation dataset (p < 0.05). CONCLUSIONS: The proposed algorithm effectively reduced the influence of parenchymal changes, resulting in a reasonably accurate and artifact-free registration.
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Algoritmos , Enfermedades Pulmonares , Tomografía Computarizada por Rayos X , Humanos , Tomografía Computarizada por Rayos X/métodos , Enfermedades Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/radioterapia , Pulmón/diagnóstico por imagen , Radiografía Torácica/métodos , Procesamiento de Imagen Asistido por Computador/métodos , ArtefactosRESUMEN
Objective. Cone beam CT (CBCT) typically has severe image artifacts and inaccurate HU values, which limits its application in radiation medicines. Scholars have proposed the use of cycle consistent generative adversarial network (Cycle-GAN) to address these issues. However, the generation quality of Cycle-GAN needs to be improved. This issue is exacerbated by the inherent size discrepancies between pelvic CT scans from different patients, as well as varying slice positions within the same patient, which introduce a scaling problem during training.Approach. We introduced the Enhanced Edge and Mask (EEM) approach in our structural constraint Cycle-EEM-GAN. This approach is designed to not only solve the scaling problem but also significantly improve the generation quality of the synthetic CT images. Then data from sixty pelvic patients were used to investigate the generation of synthetic CT (sCT) from CBCT.Main results.The mean absolute error (MAE), the root mean square error (RMSE), the peak signal to noise ratio (PSNR), the structural similarity index (SSIM), and spatial nonuniformity (SNU) are used to assess the quality of the sCT generated from CBCT. Compared with CBCT images, the MAE improved from 53.09 to 37.74, RMSE from 185.22 to 146.63, SNU from 0.38 to 0.35, PSNR from 24.68 to 32.33, SSIM from 0.624 to 0.981. Also, the Cycle-EEM-GAN outperformed Cycle-GAN in terms of visual evaluation and loss.Significance.Cycle-EEM-GAN has improved the quality of CBCT images, making the structural details clear while prevents image scaling during the generation process, so that further promotes the application of CBCT in radiotherapy.
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Algoritmos , Tomografía Computarizada de Haz Cónico , Procesamiento de Imagen Asistido por Computador , Humanos , Tomografía Computarizada de Haz Cónico/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Relación Señal-Ruido , Pelvis/diagnóstico por imagen , Redes Neurales de la Computación , ArtefactosRESUMEN
Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection, diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG signals are essential for the subsequent analysis of these conditions. However, ECG signals obtained during exercise are susceptible to various noise interferences, including electrode motion artifact, baseline wander, and muscle artifact. These interferences can blur the characteristic ECG waveforms, potentially leading to misjudgment by physicians. To suppress noise in ECG signals more effectively, this paper proposes a novel deep learning-based noise reduction method. This method enhances the diffusion model network by introducing conditional noise, designing a multi-kernel convolutional transformer network structure based on noise prediction, and integrating the diffusion model inverse process to achieve noise reduction. Experiments were conducted on the QT database and MIT-BIH Noise Stress Test Database and compared with the algorithms in other papers to verify the effectiveness of the present method. The results indicate that the proposed method achieves optimal noise reduction performance across both statistical and distance-based evaluation metrics as well as waveform visualization, surpassing eight other state-of-the-art methods. The network proposed in this paper demonstrates stable performance in addressing electrode motion artifact, baseline wander, muscle artifact, and the mixed complex noise of these three types, and it is anticipated to be applied in future noise reduction analysis of clinical dynamic ECG signals.
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Algoritmos , Artefactos , Humanos , Electrocardiografía Ambulatoria/instrumentación , Electrocardiografía Ambulatoria/métodos , Relación Señal-Ruido , Procesamiento de Señales Asistido por ComputadorRESUMEN
BACKGROUND AND PURPOSE: The quality of the Cone Beam Computed Tomography (CBCT) images used for patient set-up is essential to avoid geographical miss when narrower margins or shorter fractionation are used for example in Accelerated Partial Breast Irradiation (APBI). This study evaluates deep inspiration breath hold (DIBH) with skin guided radiotherapy as a tool for image improvement reducing motion artifacts. MATERIALS AND METHODS: Daily CBCT images of left and right breast cancer patients with well-defined surgical cavity on CT simulation were used for this study. Only left sided CBCT were acquired with DIBH. Trained and experienced radiation therapists were asked to evaluate the image quality using a cavity visualization score (CVS), an image quality Likert score, and to perform registration shifts. Images were anonymized and therapists were blinded to the use of DIBH. RESULTS: Images from 21 patients, with 15 CBCT each, were evaluated by 6 radiation therapists, generating 4,015 evaluation points. Statistically significant improvements were observed in CVS and image quality (p < 0.001) with DIBH. Also, the rate of surgical cavity identification increased to 76 % with DIBH compared to 56 % without (p < 0.001). DIBH significantly reduced the inter-observer variability in registration shift corrections (p < 0.001) CONCLUSION: Utilizing DIBH for motion control improves both the image quality and the surgical cavity identification. This results in a decrease in registration variability, which is important for APBI accuracy.
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Neoplasias de la Mama , Contencion de la Respiración , Tomografía Computarizada de Haz Cónico , Radioterapia Guiada por Imagen , Humanos , Tomografía Computarizada de Haz Cónico/métodos , Femenino , Neoplasias de la Mama/radioterapia , Neoplasias de la Mama/cirugía , Neoplasias de la Mama/diagnóstico por imagen , Radioterapia Guiada por Imagen/métodos , Planificación de la Radioterapia Asistida por Computador/métodos , Artefactos , Persona de Mediana Edad , InhalaciónRESUMEN
Opto-electrophysiology neural probes targeting single-cell levels offer an important avenue for elucidating the intrinsic mechanisms of the nervous system using different physical quantities, representing a significant future direction for brain-computer interface (BCI) devices. However, the highly integrated structure poses significant challenges to fabrication processes and the presence of photoelectric artifacts complicates the extraction and analysis of target signals. Here, we propose a highly miniaturized and integrated opto-electrophysiology neural probe for electrical recording and optical stimulation at the single-cell/subcellular level. The design of a total internal reflection layer addresses the photoelectric artifacts that are more pronounced in single-cell devices compared to conventional implantable BCI devices. Finite element simulations and electrical signal tests demonstrate that the opto-electrophysiology neural probe eliminates the photoelectric artifacts in the time domain, which represents a significant breakthrough for optoelectrical integrated BCI devices. Our proposed opto-electrophysiology neural probe holds substantial potential for promoting the development of in vivo BCI devices and developing advanced therapeutic strategies for neurological disorders.
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Artefactos , Neuronas , Neuronas/fisiología , Interfaces Cerebro-Computador , Animales , Análisis de la Célula Individual/instrumentación , HumanosRESUMEN
Purpose. Virtual Grid (VG) is an image processing technique designed to address scattered radiation from radiographic systems without a physical grid. It aims to eliminate artifacts caused by grid misalignment and enhance radiographic workflow efficiency. We intend to evaluate image quality between Virtual Grid and grid-based radiographic systems across various patient thicknesses.Methods. A Fujifilm Virtual Grid and GE AMX-4 portable radiographic system was used. Image quality was assessed using MTF, NPS, LCR, and CNR. MTF calculations employed an edge-device method with a 0.1 mmCu sheet. For NPS evaluation, uniform images were acquired with multiple 30 × 30 cm solid water blocks (2 cm thick), overlaid in 2 cm increments to simulate patient sizes from 2cm to 40 cm. LCR and CNR were evaluated using a CIRS test plate with 9-hole depths for a hole diameter of 0.375'. The test object was placed on top of the detector then water blocks, while maintaining the same SID, beam quality, and exposure between the units. Visual assessments were conducted by four readers, quantifying perceived hole numbers. The weighted Cohen's Kappa and Welch's T-test were utilized for statistical analysis.Results. At 80% MTF, VG exhibited high contrast resolution of 1.1 l p/mm compared to 1.2 l p/mm for the grid system. VG demonstrated lower noise levels across all frequencies for equivalent patient thicknesses. Welch's T-test indicated no significant differences in LCR (P = 0.31) and CNR (P = 0.34) between the systems. However, qualitative observation demonstrated VG's better low contrast response for patient sizes ≥10 cm. The average weighted Cohen's Kappa value was 0.78.Conclusion. This work indicates the Virtual Grid technology can effectively mitigate scattered radiation to improve granularity and low-contrast resolution in an image compared to a grid system. Furthermore, it can potentially reduce patient dose.
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Procesamiento de Imagen Asistido por Computador , Humanos , Procesamiento de Imagen Asistido por Computador/métodos , Fantasmas de Imagen , Artefactos , Diseño de Equipo , Dispersión de RadiaciónRESUMEN
Skin surface imaging has been used to examine skin lesions with a microscope for over a century and is commonly known as epiluminescence microscopy, dermatoscopy, or dermoscopy. Skin surface microscopy has been recommended to reduce the necessity of biopsy. This imaging technique could improve the clinical diagnostic performance of pigmented skin lesions. Different imaging techniques are employed in dermatology to find diseases. Segmentation and classification are the two main steps in the examination. The classification performance is influenced by the algorithm employed in the segmentation procedure. The most difficult aspect of segmentation is getting rid of the unwanted artifacts. Many deep-learning models are being created to segment skin lesions. In this paper, an analysis of common artifacts is proposed to investigate the segmentation performance of deep learning models with skin surface microscopic images. The most prevalent artifacts in skin images are hair and dark corners. These artifacts can be observed in the majority of dermoscopy images captured through various imaging techniques. While hair detection and removal methods are common, the introduction of dark corner detection and removal represents a novel approach to skin lesion segmentation. A comprehensive analysis of this segmentation performance is assessed using the surface density of artifacts. Assessment of the PH2, ISIC 2017, and ISIC 2018 datasets demonstrates significant enhancements, as reflected by Dice coefficients rising to 93.49 (86.81), 85.86 (79.91), and 75.38 (51.28) respectively, upon artifact removal. These results underscore the pivotal significance of artifact removal techniques in amplifying the efficacy of deep-learning models for skin lesion segmentation.
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Artefactos , Aprendizaje Profundo , Dermoscopía , Piel , Humanos , Piel/diagnóstico por imagen , Piel/patología , Dermoscopía/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Microscopía/métodos , AlgoritmosRESUMEN
INTRODUCTION: The pulmonary Hot Clot artifact (HCa) on 18F-FDG PET/CT is a poorly understood phenomenon, corresponding to the presence of a focal tracer uptake without anatomical lesion on combined CTscan. The hypothesis proposed in the literature is of microembolic origin. Our objectives were to determine the incidence of HCa, to analyze its characteristics and to identify associated factors. METHODS: All 18F-FDG PET/CT retrieved reports containing the keywords (artifact/vascular adhesion/no morphological abnormality) during the period June 2021-2023 at Brest University Hospital were reviewed for HCa. Each case was associated with 2 control patients (same daily work-list). The anatomical and metabolic characteristics of HCa were analyzed. Factors related to FDG preparation/administration, patient and vascular history were investigated. Case-control differences between variables were tested using Chi-2 test and OR (qualitative) or Student's t-test (quantitative). RESULTS: Of the 22,671 18F-FDG PET/CT performed over 2 years, 211 patients (0.94%) showed HCa. The focus was single in 97.6%, peripheral in 75.3%, and located independently in the right or left lung (51.1% vs. 48.9%). Mean ± SD values for SUVmax, SUVmean, MTV and TLG were 11.3 ± 16.5, 5.1 ± 5.0, 0.3 ± 0.3 ml and 1.5 ± 2.1 g respectively. The presence of vascular adhesion (p < 0.001), patient age (p = 0.002) and proximal venous access (p = 0.001) were statistically associated with the presence of HCa. CONCLUSION: HCa is a real but rare phenomenon (incidence around 1%), mostly unique, intense, small in volume (< 1 ml), and associated with the presence of vascular FDG uptake, confirming the hypothesis of a microembolic origin due to probable vein wall trauma at the injection site.
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Artefactos , Fluorodesoxiglucosa F18 , Tomografía Computarizada por Tomografía de Emisión de Positrones , Radiofármacos , Humanos , Tomografía Computarizada por Tomografía de Emisión de Positrones/métodos , Femenino , Masculino , Persona de Mediana Edad , Estudios de Casos y Controles , Anciano , Factores de Riesgo , Adulto , Estudios Retrospectivos , Embolia Pulmonar/diagnóstico por imagen , Embolia Pulmonar/etiología , Embolia Pulmonar/epidemiología , Anciano de 80 o más Años , Trombosis/diagnóstico por imagen , Trombosis/etiología , Trombosis/epidemiologíaRESUMEN
BACKGROUND: Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harnessing the wealth of complex EEG signals to isolate relevant brain activity. Yet, ML studies in EEG tend to ignore physiological artefacts, which may cause problems for deriving biomarkers specific to the central nervous system (CNS). METHODS: We present a framework for conceptualising machine learning from CNS versus peripheral signals measured with EEG. A signal representation based on Morlet wavelets allowed us to define traditional brain activity features (e.g. log power) and alternative inputs used by state-of-the-art ML approaches based on covariance matrices. Using more than 2600 EEG recordings from large public databases (TUAB, TDBRAIN), we studied the impact of peripheral signals and artefact removal techniques on ML models in age and sex prediction analyses. FINDINGS: Across benchmarks, basic artefact rejection improved model performance, whereas further removal of peripheral signals using ICA decreased performance. Our analyses revealed that peripheral signals enable age and sex prediction. However, they explained only a fraction of the performance provided by brain signals. INTERPRETATION: We show that brain signals and body signals, both present in the EEG, allow for prediction of personal characteristics. While these results may depend on specific applications, our work suggests that great care is needed to separate these signals when the goal is to develop CNS-specific biomarkers using ML. FUNDING: All authors have been working for F. Hoffmann-La Roche Ltd.
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Biomarcadores , Encéfalo , Electroencefalografía , Aprendizaje Automático , Humanos , Electroencefalografía/métodos , Encéfalo/metabolismo , Encéfalo/fisiología , Masculino , Femenino , Adulto , Procesamiento de Señales Asistido por Computador , Artefactos , Adolescente , Adulto Joven , Algoritmos , Persona de Mediana Edad , NiñoRESUMEN
PURPOSE: To evaluate the impact of deep learning-based reconstruction (DLRecon) on bone assessment in zero echo-time (ZTE) MRI of the knee at 1.5 Tesla. METHODS: This retrospective study included 48 consecutive exams of 46 patients (23 females) who underwent clinically indicated knee MRI at 1.5 Tesla. Standard imaging protocol comprised a sagittal prescribed, isotropic ZTE sequence. ZTE image reconstruction was performed with a standard-of-care (non-DL) and prototype DLRecon method. Exams were divided into subsets with and without osseous pathology based on the radiology report. Using a 4-point scale, two blinded readers qualitatively graded features of bone depiction including artifacts and conspicuity of pathology including diagnostic certainty in the respective subsets. Quantitatively, one reader measured signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of bone. Comparative analyses were conducted to assess the differences between the reconstruction methods. In addition, interreader agreement was calculated for the qualitative gradings. RESULTS: DLRecon significantly improved gradings for bone depiction relative to non-DL reconstruction (all, p < 0.05), while there was no significant difference with regards to artifacts (both, median score of 0; p = 0.058). In the subset with pathologies, conspicuity of pathology and diagnostic confidence were also scored significantly higher in DLRecon compared to non-DL (median 3 vs 2; p ≤ 0.03). Interreader agreement ranged from moderate to almost-perfect (κ = 0.54-0.88). Quantitatively, DLRecon demonstrated significantly enhanced CNR and SNR of bone compared to non-DL (p < 0.001). CONCLUSION: ZTE MRI with DLRecon improved bone depiction in the knee, compared to non-DL. Additionally, DLRecon increased conspicuity of osseous findings together with diagnostic certainty.
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Aprendizaje Profundo , Articulación de la Rodilla , Imagen por Resonancia Magnética , Humanos , Femenino , Masculino , Imagen por Resonancia Magnética/métodos , Estudios Retrospectivos , Persona de Mediana Edad , Adulto , Anciano , Articulación de la Rodilla/diagnóstico por imagen , Relación Señal-Ruido , Interpretación de Imagen Asistida por Computador/métodos , Adulto Joven , Anciano de 80 o más Años , ArtefactosRESUMEN
BACKGROUND: Computed tomography (CT) is widely in clinics and is affected by metal implants. Metal segmentation is crucial for metal artifact correction, and the common threshold method often fails to accurately segment metals. PURPOSE: This study aims to segment metal implants in CT images using a diffusion model and further validate it with clinical artifact images and phantom images of known size. METHODS: A retrospective study was conducted on 100 patients who received radiation therapy without metal artifacts, and simulated artifact data were generated using publicly available mask data. The study utilized 11,280 slices for training and verification, and 2,820 slices for testing. Metal mask segmentation was performed using DiffSeg, a diffusion model incorporating conditional dynamic coding and a global frequency parser (GFParser). Conditional dynamic coding fuses the current segmentation mask and prior images at multiple scales, while GFParser helps eliminate high-frequency noise in the mask. Clinical artifact images and phantom images are also used for model validation. RESULTS: Compared with the ground truth, the accuracy of DiffSeg for metal segmentation of simulated data was 97.89% and that of DSC was 95.45%. The mask shape obtained by threshold segmentation covered the ground truth and DSCs were 82.92% and 84.19% for threshold segmentation based on 2500 HU and 3000 HU. Evaluation metrics and visualization results show that DiffSeg performs better than other classical deep learning networks, especially for clinical CT, artifact data, and phantom data. CONCLUSION: DiffSeg efficiently and robustly segments metal masks in artifact data with conditional dynamic coding and GFParser. Future work will involve embedding the metal segmentation model in metal artifact reduction to improve the reduction effect.