Phase Image Data Research Articles

Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology.

Quantitative susceptibility mapping (QSM) is a unique technique for providing quantitative information on tissue magnetic susceptibility using phase image data. QSM can provide valuable information regarding physiological and pathological processes such as iron deposition, hemorrhage, calcification, and myelin. QSM has been considered for use as an imaging biomarker to investigate physiological status and pathological changes. Although various studies have investigated the clinical applications of QSM, particularly regarding the use of QSM in clinical practice, have not been examined well. This review provides on an overview of the basics of QSM and its clinical applications in neuroradiology. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Live Cancer Cell Classification Based on Quantitative Phase Spatial Fluctuations and Deep Learning With a Small Training Set

We present an analysis method that can automatically classify live cancer cells from cell lines based on a small data set of quantitative phase imaging data without cell staining. The method includes spatial image analysis to extract the cell phase spatial fluctuation map, derived from the quantitative phase map of the cell measured without cell labeling, thus without prior knowledge on the biomarker. The spatial fluctuations are indicative of the cell stiffness, where cancer cells change their stiffness as cancer progresses. In this paper, the quantitative phase spatial fluctuations are used as the basis for a deep-learning classifier for evaluating the cell metastatic potential. The spatial fluctuation analysis performed on the quantitative phase profiles before inputting them to the neural network was proven to increase the classification results in comparison to inputting the quantitative phase profiles directly, as done so far. We classified between primary and metastatic cancer cells and obtained 92.5% accuracy, in spite of using a small training set, demonstrating the method potential for objective automatic clinical diagnosis of cancer cells in vitro.

Prediction of histologic grade and type of small (< 4 cm) papillary renal cell carcinomas using texture and neural network analysis: a feasibility study.

To predict the histologic grade and type of small papillary renal cell carcinomas (pRCCs) using texture analysis and machine learning algorithms. This was a retrospective HIPAA-compliant study. 24 noncontrast (NC), 22 corticomedullary (CM) phase, and 24 nephrographic (NG) phase CTs of small (< 4 cm) surgically resected pRCCs were identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade and type 1 or 2. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (20 gray-level co-occurrences and 11 gray-level run-lengths) features were calculated for each tumor in each phase. Feature values in low- versus high-grade and type 1 versus 2 pRCCs were compared. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of histologic grade and type of pRCCs in each phase. Histogram, texture, and combined histogram and texture feature sets were used to train and test three classification algorithms (support vector machine (SVM), random forest, and histogram-based gradient boosting decision tree (HGBDT)) with stratified shuffle splits and threefold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of histologic grade and type of pRCCs. Individual histogram and texture features did not have statistically significant differences between low- and high-grade or type 1 and type 2 pRCCs across all phases. Individual features had low predictive power for tumor grade or type in all phases (AUC < 0.70). HGBDT was highly accurate at predicting pRCC histologic grade and type using histogram, texture or combined histogram and texture feature data from the CM phase (AUCs = 0.97-1.0). All algorithms had highest AUCs using CM phase feature data sets; AUCs decreased using feature sets from NC or NG phases. The histologic grade and type of small pRCCs can be predicted with classification algorithms using CM histogram and texture features, which outperform NC and NG phase image data. The accurate prediction of pRCC histologic grade and type may be able to further guide management of patients with small (< 4 cm) pRCCs being considered for active surveillance.

Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data

Understanding the mechanisms by which neurons create or suppress connections to enable communication in brain-derived neuronal cultures can inform how learning, cognition and creative behavior emerge. While prior studies have shown that neuronal cultures possess self-organizing criticality properties, we further demonstrate that in vitro brain-derived neuronal cultures exhibit a self-optimization phenomenon. More precisely, we analyze the multiscale neural growth data obtained from label-free quantitative microscopic imaging experiments and reconstruct the in vitro neuronal culture networks (microscale) and neuronal culture cluster networks (mesoscale). We investigate the structure and evolution of neuronal culture networks and neuronal culture cluster networks by estimating the importance of each network node and their information flow. By analyzing the degree-, closeness-, and betweenness-centrality, the node-to-node degree distribution (informing on neuronal interconnection phenomena), the clustering coefficient/transitivity (assessing the “small-world” properties), and the multifractal spectrum, we demonstrate that murine neurons exhibit self-optimizing behavior over time with topological characteristics distinct from existing complex network models. The time-evolving interconnection among murine neurons optimizes the network information flow, network robustness, and self-organization degree. These findings have complex implications for modeling neuronal cultures and potentially on how to design biological inspired artificial intelligence.

Texture analysis and machine learning algorithms accurately predict histologic grade in small (&lt; 4 cm) clear cell renal cell carcinomas: a pilot study

To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms. Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (< 4cm) surgically resected ccRCCs were retrospectively identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (gray-level co-occurrences (GLC) and gray-level run-lengths (GLRL)) features were calculated for each tumor in each phase. T testing compared feature values in low- and high-grade ccRCCs, with a (Benjamini-Hochberg) false discovery rate of 10%. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of low- and high-grade ccRCCs in each phase. Histogram, texture, and combined histogram and texture data sets were used to train and test four algorithms (k-nearest neighbor (KNN), support vector machine (SVM), random forests, and decision tree) with tenfold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of low- and high-grade ccRCCs. Zero, 23, and 0 features in the NC, CM, and NG phases had statistically significant differences between low and high-grade ccRCCs. CM histogram skewness and GLRL short run emphasis had the highest AUCs (0.82) in predicting histologic grade. All four algorithms had the highest AUCs (0.97) predicting histologic grade using CM histogram features. The algorithms' AUCs decreased using histogram or texture features from NC or NG phases. The histologic grade of small ccRCCs can be accurately predicted with machine learning algorithms using CM histogram features, which outperform NC and NG phase image data.

Tissue spatial correlation as cancer marker.

.We propose an intrinsic cancer marker in fixed tissue biopsy slides, which is based on the local spatial autocorrelation length obtained from quantitative phase images. The spatial autocorrelation length in a small region of the tissue phase image is sensitive to the nanoscale cellular morphological alterations and can hence inform on carcinogenesis. Therefore, this metric can potentially be used as an intrinsic cancer marker in histopathology. Typically, these correlation length maps are calculated by computing two-dimensional Fourier transforms over image subregions—requiring long computational times. We propose a more time-efficient method of computing the correlation map and demonstrate its value for diagnosis of benign and malignant breast tissues. Our methodology is based on highly sensitive quantitative phase imaging data obtained by spatial light interference microscopy.

Positive chemical exchange contrast in MRI using Refocused Acquisition of Chemical Exchange Transferred Excitations (RACETE)

PurposeA new chemical exchange MRI method is proposed which allows for direct detection of exchanging solute protons with concurrent water background suppression. MethodsThe proposed method, RACETE (Refocused Acquisition of Chemical Exchange Transferred Excitations), is based on a stimulated-echo-technique, where the first two excitation pulses are replaced by a train of N solute-selective excitation-transfer modules. This excitation cycle is then followed by a stimulated echo acquisition via selective refocusing of exchanged solute protons now present in the solvent pool. ResultsThe obtained magnitude and phase phantom images demonstrate that with only one RACETE-imaging experiment two different chemical exchange active substances with mMol-concentrations can be detected and distinguished simultaneously. ConclusionThe proposed RACETE-approach allows for true positive chemical exchange contrast imaging with the proven ability to exploit magnitude as well as phase image data.

Evaluation of Texture Analysis Parameter for Response Prediction in Patients with Hepatocellular Carcinoma Undergoing Drug-eluting Bead Transarterial Chemoembolization (DEB-TACE) Using Biphasic Contrast-enhanced CT Image Data: Correlation with Liver Perfusion CT

This study aimed to evaluate the potential role of computed tomography texture analysis (CTTA) of arterial and portal-venous enhancement phase image data for prediction and accurate assessment of response of hepatocellular carcinoma undergoing drug-eluting bead transarterial chemoembolization (TACE) by comparison to liver perfusion CT (PCT). Twenty-eight patients (27 male; mean age 67.2 ± 10.4) with 56 hepatocellular carcinoma-typical liver lesions were included. Arterial and portal-venous phase CT data obtained before and after TACE with a mean time of 39.93 ± 62.21 days between examinations were analyzed. TACE was performed within 48 hours after first contrast-enhanced CT. CTTA software was a prototype. CTTA analysis was performed blinded (for results) by two observers separately. Combined results of modified Response Evaluation Criteria In Solid Tumors (mRECIST) and PCT of the liver were used as the standard of reference. Time to progression was additionally assessed for all patients. CTTA parameters included heterogeneity, intensity, average, deviation, skewness, and entropy of co-occurrence. Each parameter was compared to those of PCT (blood flow [BF], blood volume, arterial liver perfusion [ALP], portal-venous perfusion, and hepatic perfusion index) measured before and after TACE. mRECIST + PCT yielded 28.6% complete response (CR), 42.8% partial response, and 28.6% stable disease. Significant correlations were registered in the arterial phase in CR between changes in mean heterogeneity and BF (P = .004, r = -0.815), blood volume (P = .002, r = -0.851), and ALP (P = .002, r = -0.851), respectively. In the partial response group, changes in mean heterogeneity correlated with changes in ALP (P = .003) and to a lesser degree with hepatic perfusion index (P = .027) in the arterial phase. In the stable disease group, BF correlated with entropy of nonuniformity (P = .010). In the portal-venous phase, no statistically significant correlations were registered in all groups. Receiver operating characteristic analysis of CTTA parameters yielded predictive cutoff values for CR in the arterial contrast-enhanced CT phase for uniformity of skewness (sensitivity: 90.0%; specificity: 45.8%), and in the portal-venous phase for uniformity of heterogeneity (sensitivity: 92.3%; specificity: 81.8%). Significant correlations exist between CTTA parameters and those derived from PCT both in the pre- and the post-TACE settings, and some of them have predictive value for TACE midterm outcome.

MRI image enhancement using Biot--Savart law at 3 tesla

Coil sensitivity is considered as the most valuable data in the parallel reconstruction of magnetic resonance imaging (MRI). In this study, a novel coil sensitivity map extraction method is introduced for spatially fixed phased array coils. The proposed technique uses the Biot--Savart law with coil internal shape information and low-resolution phase image data to form sensitivity maps. The performance of this method is tested in a parallel image reconstruction task, using the sensitivity-encoding (SENSE) technique. Under the quasi-static assumption and using the duality principle, we computed the sensitivity maps of a phased-array head coil and reconstructed full FOV images. The experiments show that the resulting image quality is higher in terms of sharpness, artifact level, homogeneity, etc. than existing methods that use sensitivity maps calculated only from low-resolution image data. Moreover, several simulations were conducted using an electromagnetic simulation software tool to theoretically prove the success of the proposed technique. Our simulation results indicate that the success of the method depends heavily on the size of the coil elements. A new method for calculation of sensitivity maps is briefly introduced, which increases image quality and homogeneity while reducing the artifacts.

Quantitative self-calibrating lock-in carrierographic lifetime imaging of silicon wafers

Quantitative self-calibrating lock-in carrierography (LIC) imaging of crystalline silicon wafers is introduced using an InGaAs camera and a spread super-bandgap illumination laser beam. Images at several modulation frequencies and a simplified model based on photocarrier radiometric theory are used to construct the effective carrier lifetime image from the phase-frequency dependence. The phase image data at several frequencies and at selected locations on a wafer were compared to frequency scans obtained with a single-element InGaAs detector, and good agreement was found. The quantitative LIC lifetime imaging capability demonstrated in this work is self-calibrating and eliminates the requirement for calibration in conventional photoluminescence imaging.

Detection of tab wire soldering defects on silicon solar cells using terahertz time-domain spectroscopy

Terahertz time-domain spectroscopy was applied to measure the reflectivity spectra of a silicon solar cell with tab wire soldering defects. It was demonstrated that THz phase imaging data allows a reliable estimation of height differences of bulging tab wires within 22% as tested for 0.63 and 1.07 mm loop peaks. Such measurements can be implemented for automated defect correction in future solar module production lines.

Measurement of nanomovements of an object from the optical phase image

We examine a method for measuring small movements of an object, based on calculation of the displacement of the image obtained using an optical interferometer. We estimate the effect of phase noise in reconstruction of the coherent structure of the field on the accuracy in measuring the movements. We have made practical estimates of the displacements of the real object from phase image data. We compare the estimates with the results of direct measurements of the displacements. We show that it is possible to detect movements of less than 10 nm using a commercial microscope.

Investigation of MR signal modulation due to magnetic fields from neuronal currents in the adult human optic nerve and visual cortex

Neuronal currents produce weak transient magnetic fields, and the hypothesis being investigated here is that the components of these parallel to the B0 field can potentially modulate the MR signal, thus providing a means of direct detection of nerve impulses. A theory for the phase and amplitude changes of the MR signal over time due to an external magnetic field has been developed to predict this modulation. Experimentally, a fast gradient-echo EPI sequence (TR = 158 ms, TE = 32.4 ms) was employed in an attempt to directly detect these neuronal currents in the adult human optic nerve and visual cortex using a 280-mm quadrature head coil at 1.5 T. A symmetrical intravoxel field distribution, which can be plausibly hypothesized for the axonal fields in the optic nerve and visual cortex, would result in phase cancellation within a voxel, and hence, only amplitude changes would be expected. On the other hand, an asymmetrical intravoxel field distribution would produce both phase and amplitude changes. The in vivo magnitude image data sets show a significant nerve firing detection rate of 56%, with zero detection using the phase image data sets. The percentage magnitude signal changes relative to the fully relaxed equilibrium signal fall within a predicted RMS field range of 1.2-2.1 nT in the optic nerve and 0.4-0.6 nT in the visual cortex, according to the hypothesis that the axonal fields create a symmetrical Lorentzian field distribution within the voxel.