Theoretical and Experimental Study of the Phase Optimization of Tapping Mode Atomic Force Microscope

Phase image in tapping mode atomic force microscope (TM-AFM) results from various dissipation in microcantilever system. The phases mainly reflected the tip-sample contact dissipations which allowed the nanoscale characteristics to be distinguished. In this research investigation, two factors affecting the phase and phase contrast were analyzed. It was concluded from the theoretical and experimental results that the phases and phase contrasts in the TM-AFM were related to the excitation frequencies and energy dissipation of the system. For a two-component blend, it was theoretically and experimentally proven that there was an optimal excitation frequency for maximizing the phase contrast. Therefore, selecting the optimal excitation frequency could potentially improve the phase contrast results. In addition, only the key dissipation between the tip and sample was found to accurately reflect the sample properties. Meanwhile, the background dissipation could potentially reduce the contrasts of the phase images and even mask or distort the effective information in the phase images. In order to address the aforementioned issues, a self-excited method was adopted in this study in order to eliminate the influencing effects of the background dissipation on the phases. Subsequently, the real phase information of the samples was successfully obtained. It was considered in this study that eliminating the background dissipation had effectively improved the phase contrast results and the real phase information of the samples was accurately reflected. These results are of great significance to optimize the phase of two-component samples and multi-component samples in atomic force microscope.

STUDY OF SIGNIFICANCE OF PHASE MASK IMAGE IN ACUTE STROKE PATIENTS

Background: Phase images contains information regarding local susceptibility changes between the tissues, which can help measure the iron and other content which changes the local field. Typically, this information is ignored before looking at console. Susceptibility weighted imaging (SWI) is a magnetic resonance (MR) technique detects an early hemorrhagic transformation within the infarct to provide insight into cerebral hemodynamics following the stroke. Objective: Significance of “phase mask imaging in differentiation of hemorrhage and calcifications” in acute stroke patients. Methods: An observational non-interventional study carried out on 100 patients with stroke and headache symptoms. MRI Brain Stroke Profile with FLAIR, DWI, ADC, SWAN, and Phase mask sequences, done on 3T GE MRI scanner. Results: All patients underwent MRI study with SWI sequence. Of 183 cases, 33%(n=60) patients had microbleeds, 5%(n=10) patients had granulomas, 32%(n=58) patients had arterial thrombus with infarct, 11%(n=20) patients had falx calcifications, 11%(n=20) patients had intraparenchymal haemorrhage, and 8%(n=15) patients had infarcts with haemorrhagic transformation. The sensitivity of phase imaging in the detection of calcification was 90%. Conclusion: Phase mask imaging plays an important role to detect intracranial calcifications and chronic microbleeds. Phase mask imaging acts as a supplement tool in acute stroke patients, which guides further management.

Intracranial Lesion Detection and Artifact Characterization: Comparative Study of Susceptibility and T2*-Weighted Imaging in Dogs and Cats

Susceptibility-weighted imaging (SWI), an MRI sequence for the detection of hemorrhage, allows differentiation of paramagnetic and diamagnetic substances based on tissue magnetic susceptibility differences. The three aims of this retrospective study included a comparison of the number of areas of signal void (ASV) between SWI and T2*-weighted imaging (T2*WI), differentiation of hemorrhage and calcification, and investigation of image deterioration by artifacts. Two hundred twelve brain MRIs, 160 dogs and 52 cats, were included. The sequences were randomized and evaluated for presence/absence and numbers of ASV and extent of artifacts causing image deterioration by a single, blinded observer. In cases with a CT scan differentiation of paramagnetic (hemorrhagic) and diamagnetic (calcification) lesions was made, SWI was performed to test correct assignment using the Hounsfield Units. Non-parametric tests were performed to compare both sequences regarding detection of ASV and the effect of artifacts on image quality. The presence of ASV was found in 37 SWI sequences and 34 T2*WI sequences with a significant increase in ASV only in dogs >5 and ≤ 15 kg in SWI. The remaining weight categories showed no significance. CT examination was available in 11 cases in which 81 ASV were found. With the use of phase images, 77 were classified as paramagnetic and none as diamagnetic. A classification was not possible in four cases. At the level of the frontal sinus, significantly more severe artifacts occurred in cats and dogs (dogs, p < 0.001; cats, p = 0.001) in SWI. The frontal sinus artifact was significantly less severe in brachycephalic than non-brachycephalic dogs in both sequences (SWI, p < 0.001; T2*WI, p < 0.001). In conclusion, with the advantages of better detection of ASV in SWI compared with T2*WI and the opportunity to differentiate between paramagnetic and diamagnetic origin in most cases, SWI is generally recommended for dogs. Frontal sinus conformation appears to be a limiting factor in image interpretation.

Mathematical Description and Laboratory Study of Electrophysical Methods of Localization of Geodeformational Changes during the Control of the Railway Roadbed

Currently, the load on railway tracks is increasing due to the increase in freight traffic. Accordingly, more and more serious requirements are being imposed on the reliability of the roadbed, which means that studies of methods for monitoring the integrity of the railway roadbed are relevant. The article provides a mathematical substantiation of the possibility of using seismoelectric and phasemetric methods of geoelectric control of the roadbed of railway tracks in order to identify defects and deformations at an early stage of their occurrence. The methods of laboratory modeling of the natural–technical system “railway track” are considered in order to assess the prospects of using the presented methods. The results of laboratory studies are presented, which have shown their high efficiency in registering a weak useful electrical signal caused by seismoacoustic effects against the background of high-level external industrial and natural interference. In the course of laboratory modeling, it was found that on the amplitude spectra of the output electrical signals of the investigated geological medium in the presence of an elastic harmonic action with a frequency of 70 Hz, the frequency of a harmonic electrical signal with a frequency of 40 Hz is observed. In laboratory modeling, phase images were obtained for the receiving line when simulating the process of sinking the soil base of the railway bed, confirming the presence of a transient process that causes a shift in the initial phase of the signal Δφ = 40° by ~45° (Δφ’ = 85°), which allows detection of the initial stage of failure formation.

Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network

The conventional reconstruction method of off-axis digital holographic microscopy (DHM) relies on computational processing that involves spatial filtering of the sample spectrum and tilt compensation between the interfering waves to accurately reconstruct the phase of a biological sample. Additional computational procedures such as numerical focusing may be needed to reconstruct free-of-distortion quantitative phase images based on the optical configuration of the DHM system. Regardless of the implementation, any DHM computational processing leads to long processing times, hampering the use of DHM for video-rate renderings of dynamic biological processes. In this study, we report on a conditional generative adversarial network (cGAN) for robust and fast quantitative phase imaging in DHM. The reconstructed phase images provided by the GAN model present stable background levels, enhancing the visualization of the specimens for different experimental conditions in which the conventional approach often fails. The proposed learning-based method was trained and validated using human red blood cells recorded on an off-axis Mach–Zehnder DHM system. After proper training, the proposed GAN yields a computationally efficient method, reconstructing DHM images seven times faster than conventional computational approaches.

Comparison of liver scintigraphy and the liver-spleen contrast in Gd-EOB-DTPA-enhanced MRI on liver function tests

The liver-spleen contrast (LSC) using hepatobiliary-phase images could replace the receptor index (LHL15) in liver scintigraphy; however, few comparative studies exist. This study aimed to verify the convertibility from LSC into LHL15. In 136 patients, the LSC, not at 20 min, but at 60 min after injecting gadolinium-ethoxybenzyl-diethylenetriaminepentaacetic acid was compared with the LHL15, albumin–bilirubin (ALBI) score, and the related laboratory parameters. The LHL15 was also compared with their biochemical tests. The correlation coefficients of LSC with LHL15, ALBI score, total bilirubin, and albumin were 0.740, –0.624, –0.606, and 0.523 (P < 0.00001), respectively. The correlation coefficients of LHL15 with ALBI score, total bilirubin, and albumin were –0.647, –0.553, and 0.569 (P < 0.00001), respectively. The linear regression equation on the estimated LHL15 (eLHL15) from LSC was eLHL15 = 0.460 · LSC + 0.727 (P < 0.00001) and the coefficient of determination was 0.548. Regarding a contingency table using imaging-based clinical stage classification, the degree of agreement between eLHL15 and LHL15 was 65.4%, and Cramer's V was 0.568 (P < 0.00001). Therefore, although the LSC may be influenced by high total bilirubin, the eLHL15 can replace the LSC as an index to evaluate liver function.

A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network

Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.

Classification for Breast Ultrasound Using Convolutional Neural Network with Multiple Time-Domain Feature Maps

Ultrasound (US) imaging is widely utilized as a diagnostic screening method, and deep learning has recently drawn attention for the analysis of US images for the pathological status of tissues. While low image quality and poor reproducibility are the common obstacles in US analysis, the small size of the dataset is a new limitation for deep learning due to lack of generalization. In this work, a convolutional neural network (CNN) using multiple feature maps, such as entropy and phase images, as well as a B-mode image, was proposed to classify breast US images. Although B-mode images contain both anatomical and textual information, traditional CNNs experience difficulties in abstracting features automatically, especially with small datasets. For the proposed CNN framework, two distinct feature maps were obtained from a B-mode image and utilized as new inputs for training the CNN. These feature maps can also be made from the evaluation data and applied to the CNN separately for the final classification decision. The experimental results with 780 breast US images in three categories of benign, malignant, and normal, showed that the proposed CNN framework using multiple feature maps exhibited better performances than the traditional CNN with B-mode only for most deep network models.