Spectrum Imaging Research Articles

Model-Fitting Weighted Least Squares as an Alternative to Principal Component Analysis for Analyzing Energy-Dispersive X-ray Spectroscopy Spectrum Images.

Spectrum imaging with energy-dispersive X-ray spectroscopy (EDS) has become ubiquitous in material characterization using electron microscopy. Multivariate statistical methods, commonly principal component analysis (PCA), are often used to aid analysis of the resulting multidimensional datasets; PCA can provide denoising prior to further analysis or grouping of pixels into distinct phases with similar signals. However, it is well known that PCA can introduce artifacts at low signal-to-noise ratios. Unfortunately, when evaluating the benefits and risks with PCA, it is often compared only against raw data, where it tends to shine; alternative data analysis methods providing a fair point of comparison are often lacking. Here, we directly compare PCA with a strategy based on (the conceptually and computationally simpler) weighted least squares (WLS). We show that for four representative cases, model fitting of the sum spectrum followed by WLS (mfWLS) consistently outperforms PCA in terms of finding and accurately describing compositional gradients and inclusions and as a preprocessing step to clustering. Additionally, we demonstrate that some common artifacts and biases displayed by PCA are avoided with the mfWLS approach. In summary, mfWLS can provide a superior option to PCA for analysis of EDS spectrum images as the signal is simply and accurately modeled.

Fracture mode classification of UHPC based on deep learning and wavelet time–frequency spectrum derived from acoustic emission waveform

Fracture mode categorization is essential in identifying the failure stage, evaluating the damage characteristics, and providing references for structural maintenance. Acoustic emission (AE), as a passive nondestructive technique, presents great potential in damage characterization of cementitious materials and structures. In this study, a convolutional neural network (CNN) and gated recurrent unit (GRU) integrated model for tensile, shear, and mixed cracking mode classification of ultra-high-performance concrete (UHPC) was proposed and verified based on AE waveform features. First, four-point bending and direct shear tests for UHPC specimens were carried out, respectively, to generate AE waveform signals corresponding to different cracking modes (tensile, shear, and mixed ones). Subsequently, the collected one-dimensional AE signals were converted into two-dimensional time–frequency spectrum by continuous wavelet transform. And an AE dataset was constructed after manual labeling based on the corresponding relationships between cracking modes and wavelet time–frequency spectra. Then, an automatic CNN-GRU automatic classification model was established using wavelet time–frequency spectrum images as input variables, where the CNN extracts spatial image features, and the GRU screens time dependencies and realizes final classification. The proposed CNN-GRU model achieves an accuracy of 96.17% for cracking mode classification of UHPC, which is superior both in computational accuracy and efficiency to lightweight CNN and AlexNet. Classification outcomes for UHPC specimen under four-point bending and direct shear tests revealed the proportion of tensile cracks progressively decreased with the increase of damage, while shear cracks exhibited the opposite trend. The percentage of mixed mode cracks maintained relatively stable throughout the failure stages. The model proposed in this study presents favorable performance to reveal the underlying damage evolution mechanism during UHPC fracture, which can also provide references for the cracking mode classification of other cementitious materials and structures.

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Abstract We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-guided factorization model. It optimizes a Poisson likelihood, under additional simplex constraint together with user-chosen sparsity-inducing and smoothing regularizations, and is based on iterative multiplicative updates. The fluorescence of X-rays is fully modeled thanks to state-of-the-art theoretical work. It is shown that the output of the algorithm can be used for a direct chemical quantification. With this approach, it is straightforward to include a priori knowledge on the specimen such as the presence or absence of certain chemical elements in some of its phases. This work is implemented within two open-source Python packages, espm and emtables, which are used here for data simulation, data analysis and quantification. Using simulated data, we demonstrate that incorporating physical modeling in the decomposition helps retrieve meaningful components from spatially and spectrally mixed phases, even when the data are very noisy. For synthetic data with a higher signal, the regularizations yield a tenfold increase in the quality of the reconstructed abundance maps compared to standard NMF. Our approach is further validated on experimental data with a known ground truth, where state-of-the art results are achieved by using prior knowledge about the sample. Our model can be generalized to any other scanning spectroscopy techniques where underlying physical modeling can be linearized.

Evaluation of Diagnostics of Endobronchial Tumor Pathology Using a Narrow Spectrum Imaging System

Modern medicine has new equipment and high-tech diagnostic methods. At the same time, when using standard X-ray endoscopic diagnostic methods, 70-80% of newly diagnosed patients with lung cancer have stage III-IV disease. Among the radiation methods of examination for detecting lung cancer, fluorography, chest radiography, and computed tomography are used. The main disadvantage for diagnosing lung cancer is the low level of sensitivity and accuracy, and the proportion of false-positive and false-negative conclusions reaches 30%. According to AJCC recommendations, chest radiography is not recommended for lung cancer screening due to its low efficiency. Computed tomography is recommended for screening in risk groups. However, CT reveals tumor lesions already at the stage of peripheral lymphadenopathy. Modern bronchological examination is the main method for detecting hidden, early forms of lung cancer in the diagnosis.

The role of histohematologic barriers and the possibility of using polarization biomedical optics methods in the diagnosis of autoimmune thyroiditis

Background. Violation of the integrity of the histohematologic barriers (blood-brain, blood-testis, blood-ocular, blood-labyrinth, blood-thyroid) leads to autoimmune damage to these organs. One of the manifestations of the latter is autoimmune thyroiditis, the structural and quantitative changes of which can be more informatively accurately assessed by polarization biomedical optics. The purpose of the study was to substantiate the possibility of using polarization biomedical optics methods in the diagnosis of autoimmune thyroiditis based on the use of pathophysiological analysis of blood-brain barrier integrity disorders. Materials and methods. Two groups of patients were studied: control group 1 — healthy donors (n = 51), study group 2 — people with autoimmune thyroiditis (n = 51) who underwent a puncture biopsy of the thyroid gland for diagnostic purposes. Instrumental laser methods were used: polarization, interference, multifractal. The statistical parameters of polarization ellipticity maps, polarization ellipticity of phase and multifractal spectra of digital microscopic images of native thyroid histological sections in patients with autoimmune thyroiditis were quantified: mean, dispersion, asymmetry, and kurtosis. The probability of differences compared to the controls, taken as 100 %, was evaluated using the Student’s parametric test (p < 0.05). Results. A significant increase in the mean and variance at inhibition of the asymmetry and kurtosis of polarization ellipticity, as well as in the mean and variance at reduction of the asymmetry and kurtosis of polarization ellipticity of phase digital microscopic images of thyroid native histological sections was revealed. There were a significant increase in dispersion and a decrease in the asymmetry and kurtosis of multifractal spectra of polarization ellipticity maps of digital microscopic images of native histological sections. Conclusions. A significant increase in the biophysical optical parameters of digital microscopic images of thyroid native histological sections from patients with autoimmune thyroiditis was found due to the growth of connective tissue in the interstitium as a result of an autoimmune inflammation. There was a significant inhibition of the asymmetry and kurtosis of the ellipticity of polarization of phase digital and multifractal spectra of polarization ellipticity maps of microscopic images of native histological sections in patients with autoimmune thyroiditis due to a decrease in the amount of colloids as a crystalline component caused by damage to the blood-thyroid barrier.

Relationships between abdominal adipose tissue and neuroinflammation with diffusion basis spectrum imaging in midlife obesity.

This study investigated how obesity, BMI ≥ 30 kg/m2, abdominal adiposity, and systemic inflammation relate to neuroinflammation using diffusion basis spectrum imaging. We analyzed data from 98 cognitively normal midlife participants (meanage: 49.4 [SD 6.2] years; 34 males [34.7%]; 56 with obesity [57.1%]). Participants underwent brain and abdominal magnetic resonance imaging (MRI), blood tests, and amyloid positron emission tomography (PET) imaging. Abdominal visceral and subcutaneous adipose tissue (VAT and SAT, respectively) was segmented, and Centiloids were calculated. Diffusion basis spectrum imaging parameter maps were created using an in-house script, and tract-based spatial statistics assessed white matter differences in high versus low BMI values, VAT, SAT, insulin resistance, systemic inflammation, and Centiloids, with age and sex as covariates. Obesity, high VAT, and high SAT were linked to lower axial diffusivity, reduced fiber fraction, and increased restricted fraction in white matter. Obesity was additionally associated with higher hindered fraction and lower fractional anisotropy. Also, individuals with high C-reactive protein showed lower axial diffusivity. Higher restricted fraction correlated with continuous BMI and SAT particularly in male individuals, whereas VAT effects were similar in male and female individuals. The findings suggest that, at midlife, obesity and abdominal fat are associated with reduced brain axonal density and increased inflammation, with visceral fat playing a significant role in both sexes.

Improved Polar Current Shell Algorithm for Ocean Current Retrieval from X-Band Radar Data

This paper presents an improved algorithm for retrieving ocean surface currents from X-band marine radar images. The original polar current shell (PCS) method begins with a 3D fast Fourier transform (FFT) of the radar image sequence, followed by the extraction of the dispersion shell from the 3D image spectrum, which is then transformed into a PCS using polar coordinates. Building on this foundation, the improved approach is to analyze all data points corresponding to different wavenumber magnitudes in the PCS domain rather than analyzing each specific wavenumber magnitude separately. In addition, kernel density estimation (KDE) to identify high-density directions, interquartile range filtering to remove outliers, and symmetry-based filtering to further reduce noise by comparing data from opposite directions are also utilized for further improvement. Finally, a single curve fitting is applied to the filtered data rather than conducting multiple curve fittings as in the original method. The algorithm is validated using simulated data and real radar data from both the Decca radar, established in 2008, and the Koden radar, established in 2017. For the 2008 Decca radar data, the improved PCS method reduced the root-mean-square deviation (RMSD) for speed estimation by 0.06 m/s and for direction estimation by 3.8° while improving the correlation coefficients (CCs) for current speed by 0.06 and direction by 0.07 compared to the original PCS method. For the 2017 Koden radar data, the improved PCS method reduced the RMSD for speed by 0.02 m/s and for direction by 4.6°, with CCs being improved for current speed by 0.03 and direction by 0.05 compared to the original PCS method.

Research on single-point fast three-dimensional concentration reconstruction of a smoke plume based on the static interference infrared imaging spectroscopy system

To meet the requirements of real-time and effectiveness of three-dimensional concentration telemetry of gas clouds and address the difficulty of fast three-dimensional concentration reconstruction of single instrument smoke plumes, the paper proposes a single-point fast three-dimensional concentration reconstruction of the smoke plume based on a static interference infrared Fourier transform imaging spectrometer (ISIFTS) by using the advantages of high throughput, large field of view, high stability, and rapid detection. Based on the scanning field switching characteristics of ISIFTS, a single instrument quickly scans the dual-field map data to obtain the three-dimensional point cloud of the target scene. Combined with the three-dimensional spatial parameters of the scene, a multi-dimensional re-projection algorithm is constructed based on the Gaussian plume continuous diffusion model to simulate the concentration-path-length product (CL) image, and the measured CL image is solved by analyzing the smoke plume image spectra data. The normalized cross-correlation fitting algorithm is used to perform the optimal matching of simulated-measured CL images, and then the three-dimensional concentration distribution of the plume scene is reconstructed based on the dimension mapping relationship. The field smoke plume telemetry experiment was designed and carried out, and the three-dimensional concentration distribution of plume gas at three emission points in the scene was obtained. The CO2 emission rates were calculated to be 13.572 kg / s, 12.225 kg / s, and 14.114 kg / s, respectively. The data are consistent with the emission rate of the coal-fired thermal power plant, which verifies the effectiveness of the single-point fast three-dimensional concentration reconstruction of the smoke plume method based on the ISIFTS system.

Performance of chatbots in queries concerning fundamental concepts in photochemistry.

The advent of chatbots raises the possibility of a paradigm shift across society including the most technical of fields with regard to access to information, generation of knowledge, and dissemination of education and training. Photochemistry is a scientific endeavor with roots in chemistry and physics and branches that encompass diverse disciplines ranging from astronomy to zoology. Here, five chatbots have each been challenged with 13 photochemically relevant queries. The chatbots included ChatGPT 3.5, ChatGPT 4.0, Copilot, Gemini Advanced, and Meta AI. The queries encompassed fundamental concepts (e.g., "Why is the fluorescence spectrum typically the mirror image of the absorption spectrum?"), practical matters (e.g., "What is the inner filter effect and how to avoid it?"), philosophical matters ("Please create the most important photochemistry questions."), and specific molecular features (e.g., "Why are azo dyes non-fluorescent?"). The chatbots were moderately effective in answering queries concerning fundamental concepts in photochemistry but were glaringly deficient in specialized queries for dyes and fluorophores. In some instances, a correct response was embedded in verbose scientific nonsense whereas in others the entire response, while grammatically correct, was utterly meaningless. The unreliable accuracy makes present chatbots poorly suited for unaided educational purposes and highlights the importance of domain experts.

First Results from the Dragonfly Ultrawide Survey: The Largest 11 Quenched Diffuse Dwarf Galaxies in 3100 deg2 with Spectroscopic Confirmation

The Dragonfly Telephoto Array employs a unique design to detect very large and diffuse galaxies that might be missed with conventional telescopes. The Dragonfly Ultrawide Survey (DFUWS) is a new wide-field survey that will cover 10,000 deg2 of the northern sky, and it provides an ideal data set to find these large diffuse galaxies. From 3100 deg2 of DFUWS data, we identified 11 large, low surface brightness galaxies as a pilot sample for spectroscopic follow-up. These are the largest galaxies in the examined area that appear smooth and isolated, with effective radii of 12″–27″. Eight are below 24 magarcsec−2 in central g-band surface brightness. Keck Cosmic Web Imager spectra of the diffuse light show that all 11 galaxies in this sample are quiescent, and seven qualify as ultradiffuse galaxies (UDGs). Eight galaxies have distances between 15 and 30 Mpc, while the other three are in the Pegasus cluster at 50 Mpc. Their spectra show evidence of a ∼1 Gyr old stellar population in addition to an even older stellar population. The intermediate-age component is present in group and satellite galaxies but not in the Pegasus cluster UDGs. All galaxies in this sample are detected in both Dragonfly and Legacy imaging, and the sample partially overlaps with existing UDG catalogs. This pilot sample provides an excellent training set for our analysis of the upcoming full 10,000 deg2 DFUWS data, from which we may expect to discover even larger, previously unknown galaxies.

Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery

In reconstructive surgery, flaps are the cornerstone for repairing tissue defects, but postoperative monitoring of their viability remains a challenge. Among the imagistic techniques for monitoring flaps, the thermal camera has demonstrated its value as an efficient indirect method that is easy to use and easy to integrate into clinical practice. This provides a narrow color spectrum image that is amenable to the development of an artificial neural network in the context of current technological progress. In the present study, we introduce a novel attention-enhanced recurrent residual U-Net (AER2U-Net) model that is able to accurately segment flaps on thermographic images. This model was trained on a uniquely generated database of thermographic images obtained by monitoring 40 patients who required flap surgery. We compared the proposed AER2U-Net with several state-of-the-art neural networks used for multi-modal segmentation of medical images, all of which are based on the U-Net architecture (U-Net, R2U-Net, AttU-Net). Experimental results demonstrate that our model (AER2U-Net) achieves significantly better performance on our unique dataset compared to these existing U-Net variants, showing an accuracy of 0.87. This deep learning-based algorithm offers a non-invasive and precise method to monitor flap vitality and detect postoperative complications early, with further refinement needed to enhance its clinical applicability and effectiveness.

Phenotypic, Electrophysiologic, and Imaging Spectrum of Hirayama Disease from Northern India.

Cervical flexion-induced myelopathy, also known as Hirayama disease (HD), is a lower motor neuron disorder seen mainly in adolescents and young adults, affecting the C7-T1 myotomes, presenting as asymmetric weakness with wasting of one or both the distal upper limbs. We aimed to describe the clinical features, electrophysiology, and radiologic features of HD in a tertiary care institute in northern India. One hundred and forty patients between 2017 and 2022 with clinical and imaging features consistent with HD were retrospectively reviewed from the All India Institute of Medical Sciences-Comprehensive Neuromuscular Diseases center database. Majority were males with the mean age of onset of illness being 17.8 years. The median duration of the symptoms was 3 (1.5-4) years. Sixty-nine (49%) patients had unilateral involvement, and the disease was actively progressing in 88 (63%) patients at presentation. Two families had history of HD in two (1.4%) siblings. Electromyography showed abnormal findings in the clinically involved limb in all the patients and in the clinically uninvolved limb in 17/50 (34%) patients. Flexion magnetic resonance imaging (MRI) demonstrated forward dural displacement in 134 (96%) patients and asymmetric cord flattening in 124 (88.5%) patients. Disability was graded as mild, moderate, and severe; 12 (13%) had severe disability. The majority were managed conservatively, and four underwent surgery for HD. A high index of suspicion of HD needs to be kept in a young male presenting with distal upper limb weakness and atrophy. Dynamic MRI of the cervical spine in young adults presenting with hand wasting is inevitable. This disease needs to be managed aggressively and early to prevent serious dysfunction and loss of productivity.

Identification of High-Photosynthetic-Efficiency Wheat Varieties Based on Multi-Source Remote Sensing from UAVs

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time and effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning high-photosynthetic-efficiency wheat varieties. The objective of this research is to develop a multi-stage predictive model encompassing nine photosynthetic indicators at the field scale for wheat breeding. These indices include soil and plant analyzer development (SPAD), leaf area index (LAI), net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture efficiency (Fv’/Fm’), and photochemical quenching coefficient (qP). The ultimate goal is to differentiate high-photosynthetic-efficiency wheat varieties through model-based predictions. This research gathered red, green, and blue spectrum (RGB) and multispectral (MS) images of eleven wheat varieties at the stages of jointing, heading, flowering, and filling. Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. Furthermore, the research conducted principal component analysis (PCA) and membership function analysis on the predicted values of the optimal models for each indicator, established a comprehensive evaluation index for high photosynthetic efficiency, and employed cluster analysis to screen the test materials. The cluster analysis categorized the eleven varieties into three groups, with SH06144 and Yannong 188 demonstrating higher photosynthetic efficiency. The moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, and Guigu 820, totaling seven varieties. Xinmai 916 and Jinong 114 fall into the category of lower photosynthetic efficiency, aligning closely with the results of the clustering analysis based on actual measurements. The findings suggest that employing UAV-based multi-source remote sensing technology to identify wheat varieties with high photosynthetic efficiency is feasible. The study results provide a theoretical basis for winter wheat phenotypic monitoring at the breeding field scale using UAV-based multi-source remote sensing, offering valuable insights for the advancement of smart breeding practices for high-photosynthetic-efficiency wheat varieties.

Differentiation between high-grade gliomas and solitary brain metastases based on multidiffusion MRI model quantitative analysis.

Differentiating high-grade gliomas (HGGs) from solitary brain metastases (SBMs) using conventional magnetic resonance imaging (MRI) remains challenging due to their similar imaging features. This study aimed to evaluate the diagnostic performance of advanced diffusion models, such as neurite orientation dispersion and density imaging (NODDI) and mean apparent propagator magnetic resonance imaging (MAP-MRI), incomparison to traditional techniques like diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) for distinguishing HGGs from SBMs. In total, 17 patients with HGGs and 26 patients with SBMs were prospectively recruited based on the established inclusion and exclusion criteria. Structural MRI sequences and diffusion spectrum imaging (DSI) were utilized to assess quantitative parameter models, including NODDI, MAP-MRI, DWI, DTI, and DKI. Quantitative parameters were measured for both the tumor parenchymal area and the peritumoral edema area. The quantitative parameters of the two patient groups were compared using either the independent Student's t-test or the Mann-Whitney U test. The effectiveness of each model was evaluated using receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC). Finally, the DeLong test was employed to compare the diagnostic performance of each model through pairwise comparisons of ROC curves. Isotropic volume fraction (Viso) based on NODDI; mean squared displacement (MSD) and the return to plane probabilities (RTPP) based on MAP-MRI; radial diffusivity (RDk) and mean diffusivity (MDk) based on DKI; and axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) based on DTI of the peritumoral edema tumor were significantly different between HGGs and SBMs (p < 0.05). The optimal single discriminant parameters for each model are NODDI_Viso, MAP-MRI_MSD, DKI_MDk, and DTI_AD. Among these, the AUC of Viso (0.809) exceeds that of MSD (0.733), MDk (0.718), and AD (0.779). The combined model, which incorporates DTI_AD, DKI_RD, and NODDI_Viso, demonstrated superior diagnostic performance (0.897). Advanced diffusion MRI quantitative parameters derived from NODDI, such as Viso, have the potential to enhance the differentiation between HGGs and SBMs. The integrated utilization of these models is anticipated to enhance diagnostic accuracy and refine MRI protocols for brain tumor assessment.

A Pair of Indicators for Characterizing Cerebral Microbleeds Based on Raman Spectrum and Two-Photon Imaging.

Cerebral microbleeds (CMBs) lead to cognitive decline, linked to the axonal structure composed of phospholipid bilayers. Current methods are difficult to obtain in situ changes of biochemical component concentration during CMB. In this study,by Raman spectrum and two-photon imaging, we achieve in situ changes in the information of biochemical components concentration during CMB. The overall concentration of phospholipids in the damaged tissue significantly decreases after CMB, forming a large region of low concentration, but the relative concentration of phosphatidylinositol (PI) increases, reflecting the inhibition role of the phosphatidylinositol 3 kinase/protein kinase B (PI3K/Akt) pathway. Accordingly, two-photon images of neurons show a clear decrease in the number of axons, indicating a close correlation between phospholipid hydrolysis and axon damage, as well as cognitive impairment. Therefore, the decrease in phospholipid concentration and theincrease in the PI concentration might serve as a pair of indicators for characterizing CMB and its relationship with cognitive decline.

Development, validation, and application of a generic image-based noise addition method for simulating reduced dose computed tomography images.

Major efforts in computed tomography (CT) have been focused on reducing radiation dose to patients while maintaining adequate diagnostic quality. To that end, research tools have been developed to simulate reduced-dose images via either image-based or projection-based methods. The former is limited to fully capturing realistic texture, streak, and non-stationary characteristics of reduced dose, while the latter is impractical clinically. To develop and validate an image-based noise addition method that accounts for such attributes while being practical in clinical settings. A noise addition method was developed to add realistic noise in the image domain. The method first estimates the noise power spectrum (NPS) of CT images, which are also forward-projected to form synthetic projections. The projection data are supplemented with random white noise proportional to their attenuation values. The noise sinogram is then back-projected onto the image, filtered by the NPS, and scaled according to the desired dose reduction level. The tool was evaluated using both phantom images and patient data. The phantom images were acquired using a multi-sized image quality phantom (Mercury Phantom 3.0, Duke University), and a thorax anthropomorphic phantom (Lungman Phantom, Kyoto Kagaku) at different dose levels and reconstruction settings. The patient images consisted of two dose levels of various CT examinations and reconstruction settings. The simulated and real reduced-dose images were compared in terms of the noise magnitude and texture (i.e., NPS average frequency, NPS-fav). The utility of this methodology was also assessed for routine clinical use for CT protocol review. For the phantom images, the percent errors in the noise magnitude between the simulated images and the actual images of the Mercury Phantom and anthropomorphic phantom images were 3.34% and 3.50%, respectively. The difference in fav was 0.07mm-1 for the Mercury Phantom and 0.06mm-1 for the anthropomorphic phantom between the simulated and actual images. The average noise magnitude percent error between the simulated and actual patient images was 4.61% with noise texture judged to be visually comparable with some kernel dependencies. When implemented clinically, the tool proved practical to simplify the process of estimating radiation dose reduction for CT protocols, resulting in a 50% dose reduction of our multiple myeloma protocol. The method generated simulated CT images with realistic noise properties similar to images acquired at the same radiation exposure without needing access to raw projection data.

Characterisation and properties of Nanohybrid Dental Composite (NhDC) reinforced with zirconia and alumina

A trial nanohybrid dental composite (NHDC) was developed using silica extracted from biowaste rice. Aim: This study aims to test the effect of mixture of the silica and other filler which is zirconia and alumina, focusing on the characterization and their properties. Nano-silica obtained from rice husk was used as NHDC filler. Methods: Nanosilica white powder, derived from rice husk, was used. There were three groups: Group I, Zr (2%) and Al (3%); group II, Zr (3%) and Al (2%); and group III, Zr (3%). The distribution of filler particles and the chemical structure of filler particles were determined by FESEM images and FTIR test. Then, testing on Vickers hardness (VHN) and fracture strength (FS) of the samples was performed. Results: The distribution of filler particles showed a uniform distribution, and each peak element of the fillers was shown in the FTIR spectrum images. There was significantly increased on VHN and FS after addition of zirconia and alumina. Conclusion: This study concluded that zirconia and alumina strengthen the NHDC properties with addition of zirconia and alumina filler.

Quantitative MRI biomarker for classification of clinically significant prostate cancer: Calibration for reproducibility across echo times.

The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa). This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75ms and once at TE=90ms (TEmin1, TEmin2, and TE90, respectively). A linear regression model was determined to match the C-maps of TE90 to the reference C-maps of TEmin1 within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate. We compared RSIrs from calibrated TE90 (RSIrsTE90corr) and uncorrected TE90 (RSIrsTE90) to RSIrs from reference TEmin1 (RSIrsTEmin1) and repeated TEmin2 (RSIrsTEmin2). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC). Scaling factors for C1, C2, C3, and C4 were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non-csPCa cases, the 98th percentile of RSIrsTEmin2 and RSIrsTEmin1 differed by 0.27±0.86SI (mean±standard deviation), whereas RSIrsTE90 differed from RSIrsTEmin1 by 1.82±1.20SI. After calibration, this bias was reduced to -0.51±1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54±1.98SI between RSIrsTEmin2 and RSIrsTEmin1 and 2.28±2.06SI between RSIrsTE90 and RSIrsTEmin1. After calibration, the mean difference decreased to -1.03SI, a 55% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrsTEmin1 has a sensitivity of 66% and a specificity of 72%. The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 72% and 55% for non-csPCa and csPCa, respectively.

Deep CNN for Parkinson’s Disease Classification Using Line Spectral Frequency Images of Sustained Speech Phonation

It is proposed to use Deep Convolution Neural Network (DCNN) which is a good classifier of natural images to learn speech spectrum images of sustained phonation to detect Parkinson’s Disease (PD) as an alternative to the existing feature-based machine learning method. It is shown that the proposed method yields very high accuracy without the need for separate feature computation stage. The speech spectrum representations proposed are Short Time Fourier Transform (STFT) spectrum of size Nx256 and Line Spectral Frequency (LSF) spectrum of size Nx16. LSF reflects the speech production mechanism and it is a novel idea to use LSF spectrum in DCNN to detect PD speech. The spectrum images look like random patterns and the performance is improved when using an additional deeper hidden layer of tampering pattern in the last stage of a fully connected layer. Using a standard PD-sustained phonation dataset the training accuracies achieved are 98.50% and 92.50% for STFT and LSF method, respectively. The validation accuracies achieved are 84.38% for STFT and 100% for LSF. The STFT method results in a sensitivity of 97.05%, a specificity of 88.63%, a precision of 86.84%, an F1-score of 91.66, a false positive rate (FPR) of 11.36%, and a false alarm rate of 12.82%. The LSF method results in a sensitivity 97.05%, a specificity of 95.45%, a precision of 94.28%, an F1-score of 95.65, an FPR of 4.50%, and a false alarm rate of 5.71%. The LSF based method performs better and the performance comparison with the state-of-the-art methods brings out the merits of the LSF spectrum image-based DCNN learning in PD detection using sustained phonation.

Magnetic resonance imaging spectrum of intracranial meningiomas: An institutional review of common and uncommon imaging appearances

Magnetic resonance imaging spectrum of intracranial meningiomas: An institutional review of common and uncommon imaging appearances