Bloodletting Acupuncture at Jing-Well Points on Hand Induced Autophagy to Alleviate Brain Injury in Acute Altitude Hypoxic Rats by Activating PINK1/Parkin Pathway.

OBJECTIVE: To explore the protective effect of bloodletting acupuncture at twelve Jing-well points on hand (BAJP) on acute hypobaric hypoxia (AHH)-induced brain injury in rats and its possible mechanisms. METHODS: Seventy-five Sprague Dawley rats were divided into 5 groups by a random number table (n=15), including control, model, BAJP, BAJP+3-methyladenine (3-MA), and bloodletting acupuncture at non-acupoint (BANA, tail tip blooding) groups. After 7-day pre-treatment, AHH models were established using hypobaric oxygen chambers. The levels of S100B, glial fibrillary acidic protein (GFAP), superoxide dismutase (SOD), and malondialdehyde (MDA) in serum were measured by enzyme-linked immunosorbent assay. Hematoxylin-eosin staining and the terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling method were used to assess hippocampal histopathology and apoptosis. Transmission electron microscopy assay was used to observe mitochondrial damage and autophagosomes in hippocampal tissues. Flow cytometry was used to detect mitochondrial membrane potential (MMP). The mitochondrial respiratory chain complexes I, III and IV activities and ATPase in hippocampal tissue were evaluated, respectively. Western blot analysis was used to detect the protein expressions of Beclin1, autophagy protein 5 (ATG5), microtubule-associated protein 1 light chain 3 beta (LC3B), phosphatase and tensin homolog induced kinase 1 (PINK1), and Parkin in hippocampal tissues. The mRNA expressions of Beclin1, ATG5 and LC3-II were analyzed by quantitative real-time polymerase chain reaction. RESULTS: BAJP treatment reduced hippocampal tissue injury and inhibited hippocampal cell apoptosis in AHH rats. BAJP reduced oxidative stress by decreasing S100B, GFAP and MDA levels and increasing SOD level in the serum of AHH rats (P<0.05 or P<0.01). Then, BAJP increased MMP, the mitochondrial respiratory chain complexes I, III and IV activities, and the mitochondrial ATPase activity in AHH rats (all P<0.01). BAJP improved mitochondrial swelling and increased the autophagosome number in hippocampal tissue of AHH rats. Moreover, BAJP treatment increased the protein and mRNA expressions of Beclin1 and ATG5 and LC3-II/LC3-I ratio in AHH rats (all P<0.01) and activated the PINK1/Parkin pathway (P<0.01). Finally, 3-MA attenuated the therapeutic effect of BAJP on AHH rats (P<0.05 or P<0.01). CONCLUSION: BAJP was an effective treatment for AHH-induced brain injury, and the mechanism might be through reducing hippocampal tissue injury via increasing the PINK1/Parkin pathway and enhancement of mitochondrial autophagy.

Contrast-Enhanced Spectral Mammography-Based Prediction of Non-Sentinel Lymph Node Metastasis and Axillary Tumor Burden in Patients With Breast Cancer.

Purpose: To establish and evaluate non-invasive models for estimating the risk of non-sentinel lymph node (NSLN) metastasis and axillary tumor burden among breast cancer patients with 1-2 positive sentinel lymph nodes (SLNs). Materials and Methods: Breast cancer patients with 1-2 positive SLNs who underwent axillary lymph node dissection (ALND) and contrast-enhanced spectral mammography (CESM) examination were enrolled between 2018 and 2021. CESM-based radiomics and deep learning features of tumors were extracted. The correlation analysis, least absolute shrinkage and selection operator (LASSO), and analysis of variance (ANOVA) were used for further feature selection. Models based on the selected features and clinical risk factors were constructed with multivariate logistic regression. Finally, two radiomics nomograms were proposed for predicting NSLN metastasis and the probability of high axillary tumor burden. Results: A total of 182 patients [53.13 years ± 10.03 (standard deviation)] were included. For predicting the NSLN metastasis status, the radiomics nomogram built by 5 selected radiomics features and 3 clinical risk factors including the number of positive SLNs, ratio of positive SLNs, and lymphovascular invasion (LVI), achieved the area under the receiver operating characteristic curve (AUC) of 0.85 [95% confidence interval (CI): 0.71-0.99] in the testing set and 0.82 (95% CI: 0.67-0.97) in the temporal validation cohort. For predicting the high axillary tumor burden, the AUC values of the developed radiomics nomogram are 0.82 (95% CI: 0.66-0.97) in the testing set and 0.77 (95% CI: 0.62-0.93) in the temporal validation cohort. Discussion: CESM images contain useful information for predicting NSLN metastasis and axillary tumor burden of breast cancer patients. Radiomics can inspire the potential of CESM images to identify lymph node metastasis and improve predictive performance.

Deep Learning of Morphologic Correlations To Accurately Classify CD4+ and CD8+ T Cells by Diffraction Imaging Flow Cytometry.

The two major subtypes of human T cells, CD4+ and CD8+, play important roles in adaptive immune response by their diverse functions. To understand the structure-function relation at the single cell level, we isolated 2483 CD4+ and 2450 CD8+ T cells from fresh human splenocytes by immunofluorescent sorting and investigated their morphologic relations to the surface CD markers by acquisition and analysis of cross-polarized diffraction image (p-DI) pairs. A deep neural network of DINet-R has been built to extract 2560 features across multiple pixel scales of a p-DI pair per imaged cell. We have developed a novel algorithm to form a matrix of Pearson correlation coefficients by these features for selection of a support cell set with strong morphologic correlation in each subtype. The p-DI pairs of support cells exhibit significant pattern differences between the two subtypes defined by CD markers. To explore the relation between p-DI features and CD markers, we divided each subtype into two groups of A and B using the two support cell sets. The A groups comprise 90.2% of the imaged T cells and classification of them by DINet-R yields an accuracy of 97.3 ± 0.40% between the two subtypes. Analysis of depolarization ratios further reveals the significant differences in molecular polarizability between the two subtypes. These results prove the existence of a strong structure-function relation for the two major T cell subtypes and demonstrate the potential of diffraction imaging flow cytometry for accurate and label-free classification of T cell subtypes.

Identification of WHO II/III Gliomas by 16 Prognostic-related Gene Signatures using Machine Learning Methods.

BACKGROUND: It is found that the prognosis of gliomas of the same grade has large differences among World Health Organization (WHO) grade II and III in clinical observation. Therefore, a better understanding of the genetics and molecular mechanisms underlying WHO grade II and III gliomas is required, with the aim of developing a classification scheme at the molecular level rather than the conventional pathological morphology level. METHODS: We performed survival analysis combined with machine learning methods of Least Absolute Shrinkage and Selection Operator using expression datasets downloaded from the Chinese Glioma Genome Atlas as well as The Cancer Genome Atlas. Risk scores were calculated by the product of expression level of overall survival-related genes and their multivariate Cox proportional hazards regression coefficients. WHO grade II and III gliomas were categorized into the low-risk subgroup, medium-risk subgroup, and high-risk subgroup. We used the 16 prognostic-related genes as input features to build a classification model based on prognosis using a fully connected neural network. Gene function annotations were also performed. RESULTS: The 16 genes (AKNAD1, C7orf13, CDK20, CHRFAM7A, CHRNA1, EFNB1, GAS1, HIST2H2BE, KCNK3, KLHL4, LRRK2, NXPH3, PIGZ, SAMD5, ERINC2, and SIX6) related to the glioma prognosis were screened. The 16 selected genes were associated with the development of gliomas and carcinogenesis. The accuracy of an external validation data set of the fully connected neural network model from the two cohorts reached 95.5%. Our method has good potential capability in classifying WHO grade II and III gliomas into low-risk, medium-risk, and high-risk subgroups. The subgroups showed significant (P<0.01) differences in overall survival. CONCLUSION: This resulted in the identification of 16 genes that were related to the prognosis of gliomas. Here we developed a computational method to discriminate WHO grade II and III gliomas into three subgroups with distinct prognoses. The gene expressionbased method provides a reliable alternative to determine the prognosis of gliomas.

Research Progress of Gliomas in Machine Learning.

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.

L-Arginine Supplementation Improves Vascular Endothelial Dysfunction Induced by High-Fat Diet in Rats Exposed to Hypoxia.

INTRODUCTION: Our previous study showed that high-fat diet inhibited the increase in nitric oxide and endothelial nitric oxide synthase expression in the aortic endothelium of rats exposed to hypoxia, and hypoxia plus a high-fat diet led to earlier and more severe vascular endothelial dysfunction (VED) than hypoxia alone. The purpose of the present study was to investigate the effects of L-arginine on high-fat diet-induced VED of rats in hypoxia. METHODS: Forty male Sprague-Dawley rats were randomly divided into 4 groups and treated with hypoxia (H group), hypoxia plus high-fat diet (H+HFD group), hypoxia plus L-arginine (H+L-Arg group), and hypoxia plus high-fat diet and L-arginine (H+HFD+L-Arg group) for 1 wk. Hypoxia was simulated in a hypobaric chamber with an altitude of 5000 m. Aortic morphology and endothelium-dependent vasorelaxation were used to assess VED. RESULTS: High-fat diet impaired vascular remodeling and reduced endothelium-dependent vasodilator response to acetylcholine in rats exposed to hypoxia, secondary to dysregulation of the nitric oxide pathway. L-arginine supplementation significantly increased plasma nitrates and nitrites and endothelial nitric oxide synthase mRNA levels and improved ultrastructural changes in aortic endothelium and endothelium-dependent vasodilator response. CONCLUSIONS: L-arginine prevents aortic ultrastructural changes and reverses VED induced by high-fat diet in rats exposed to hypoxia, which may have implications for VED induced by high-fat diet in high altitude dwellers.

A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients.

BACKGROUND: Accurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the GTV. We synthesize anatomical and functional MRI for accurate and semi-automatic segmentation of GTVs and improvement of clinical efficiency. METHODS: Four MR image sets including T1-weighted contrast-enhanced (T1C), T2-weighted (T2), apparent diffusion constant (ADC) and fractional anisotropy (FA) images of 5 glioma patients were acquired and registered. A new potential field segmentation (PFS) method was proposed based on the concept of potential field in physics. For T1C, T2 and ADC images, global potential field segmentation (global-PFS) was used on user defined region of interest (ROI) for rough segmentation and then morphologically processed for accurate delineation of the GTV. For FA images, white matter (WM) was removed using local potential field segmentation (local-PFS), and then tumor extent was delineated with region growing and morphological methods. The individual segmentations of multi-parametric images were ensembled into a fused segmentation, considered as final GTV. GTVs were compared with manually delineated ground truth and evaluated with segmentation quality measure (Q), Dice's similarity coefficient (DSC) and Sensitivity and Specificity. RESULTS: Experimental study with the five patients' data and new method showed that, the mean values of Q, DSC, Sensitivity and Specificity were 0.80 (±0.07), 0.88 (±0.04), 0.92 (±0.01) and 0.88 (±0.05) respectively. The global-PFS used on ROIs of T1C, T2 and ADC images can avoid interferences from skull and other non-tumor areas. Similarity to local-PFS on FA images, it can also reduce the time complexity as compared with the global-PFS on whole image sets. CONCLUSIONS: Efficient and semi-automatic segmentation of the GTV can be achieved with the new method. Combination of anatomical and functional MR images has the potential to provide new methods and ideas for target definition in radiotherapy.

Feasibility study of stain-free classification of cell apoptosis based on diffraction imaging flow cytometry and supervised machine learning techniques.

This study was to explore the feasibility of prediction and classification of cells in different stages of apoptosis with a stain-free method based on diffraction images and supervised machine learning. Apoptosis was induced in human chronic myelogenous leukemia K562 cells by cis-platinum (DDP). A newly developed technique of polarization diffraction imaging flow cytometry (p-DIFC) was performed to acquire diffraction images of the cells in three different statuses (viable, early apoptotic and late apoptotic/necrotic) after cell separation through fluorescence activated cell sorting with Annexin V-PE and SYTOX® Green double staining. The texture features of the diffraction images were extracted with in-house software based on the Gray-level co-occurrence matrix algorithm to generate datasets for cell classification with supervised machine learning method. Therefore, this new method has been verified in hydrogen peroxide induced apoptosis model of HL-60. Results show that accuracy of higher than 90% was achieved respectively in independent test datasets from each cell type based on logistic regression with ridge estimators, which indicated that p-DIFC system has a great potential in predicting and classifying cells in different stages of apoptosis.

A novel automatic quantification method for high-content screening analysis of DNA double strand-break response.

High-content screening is commonly used in studies of the DNA damage response. The double-strand break (DSB) is one of the most harmful types of DNA damage lesions. The conventional method used to quantify DSBs is γH2AX foci counting, which requires manual adjustment and preset parameters and is usually regarded as imprecise, time-consuming, poorly reproducible, and inaccurate. Therefore, a robust automatic alternative method is highly desired. In this manuscript, we present a new method for quantifying DSBs which involves automatic image cropping, automatic foci-segmentation and fluorescent intensity measurement. Furthermore, an additional function was added for standardizing the measurement of DSB response inhibition based on co-localization analysis. We tested the method with a well-known inhibitor of DSB response. The new method requires only one preset parameter, which effectively minimizes operator-dependent variations. Compared with conventional methods, the new method detected a higher percentage difference of foci formation between different cells, which can improve measurement accuracy. The effects of the inhibitor on DSB response were successfully quantified with the new method (p = 0.000). The advantages of this method in terms of reliability, automation and simplicity show its potential in quantitative fluorescence imaging studies and high-content screening for compounds and factors involved in DSB response.

Comparison of contourlet transform and gray level co-occurrence matrix for analyzing cell-scattered patterns.

Distribution of scattered image patterns hinges on morphological and optical characteristics of cells. This paper applied a numerical method to simulate scattered images of real cell morphologies, which were reconstructed from confocal image stacks dyed by fluorescent stains. Two approaches, contourlet transform (CT) and gray level co-occurrence matrix (GLCM), were then used to analyze the simulated scattered images. The results showed that features extracted using GLCM contained more information than those extracted using CT. Higher classification accuracy could be achieved with a single GLCM parameter than CT and GLCM could achieve higher accuracy with fewer parameters than CT when using multiple parameters. Meanwhile, GLCM requires less computational cost. Thus, GLCM is more suitable and efficient than CT for the analysis of cell-scattered images.

Original Research: Label-free detection for radiation-induced apoptosis in glioblastoma cells.

Current flow cytometry (FCM) requires fluorescent dyes labeling cells which make the procedure costly and time consuming. This manuscript reports a feasibility study of detecting the cell apoptosis with a label-free method in glioblastoma cells. A human glioma cell line M059K was exposed to 8 Gy dose of radiation, which enables the cells to undergo radiation-induced apoptosis. The rates of apoptosis were studied at different time points post-irradiation with two different methods: FCM in combination with Annexin V-FITC/PI staining and a newly developed technique named polarization diffraction imaging flow cytometry. Totally 1000 diffraction images were acquired for each sample and the gray level co-occurrence matrix (GLCM) algorithm was used in morphological characterization of the apoptotic cells. Among the feature parameters extracted from each image pair, we found that the two GLCM parameters of angular second moment (ASM) and sum entropy (SumEnt) exhibit high sensitivities and consistencies as the apoptotic rates (Pa) measured with FCM method. In addition, no significant difference exists between Pa and ASM_S, Pa and SumEnt_S, respectively (P > 0.05). These results demonstrated that the new label-free method can detect cell apoptosis effectively. Cells can be directly used in the subsequent biochemical experiments as the structure and function of cells and biomolecules are well-preserved with this new method.

A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images.

It has been shown that the intratumor heterogeneity can be characterized with quantitative analysis of the [18]F-FDG PET image data. The existing models employ multiple parameters for feature extraction which makes it difficult to implement in clinical settings for the quantitative characterization. This article reports an easy-to-use and differential SUV based model for quantitative assessment of the intratumor heterogeneity from 3D [18]F-FDG PET image data. An H index is defined to assess tumor heterogeneity by summing voxel-wise distribution of differential SUV from the [18]F-FDG PET image data. The summation is weighted by the distance of SUV difference among neighboring voxels from the center of the tumor and can thus yield increased values for tumors with peripheral sub-regions of high SUV that often serves as an indicator of augmented malignancy. Furthermore, the sign of H index is used to differentiate the rate of change for volume averaged SUV from its center to periphery. The new model with the H index has been compared with a widely-used model of gray level co-occurrence matrix (GLCM) for image texture characterization with phantoms of different configurations and the [18]F-FDG PET image data of 6 lung cancer patients to evaluate its effectiveness and feasibility for clinical uses. The comparison of the H index and GLCM parameters with the phantoms demonstrate that the H index can characterize the SUV heterogeneity in all of 6 2D phantoms while only 1 GLCM parameter can do for 1 and fail to differentiate for other 2D phantoms. For the 8 3D phantoms, the H index can clearly differentiate all of them while the 4 GLCM parameters provide complicated patterns in the characterization. Feasibility study with the PET image data from 6 lung cancer patients show that the H index provides an effective single-parameter metric to characterize tumor heterogeneity in terms of the local SUV variation, and it has higher correlation with tumor volume change after radiotherapy (R(2) = 0.83) than the 4 GLCM parameters (R(2) = 0.63, 0.73, 0.59 and 0.75 for Energy, Contrast, Local Homogeneity and Entropy respectively). The new model of the H index has the capacity to characterize the intratumor heterogeneity feature from 3D [18]F-FDG PET image data. As a single parameter with an intuitive definition, the H index offers potential for clinical applications.

Spectrophotometric determination of turbid optical parameters without using an integrating sphere.

Spectrophotometric quantification of turbidity by multiple optical parameters has wide-ranging applications in material analysis and life sciences. A robust system design needs to combine hardware for precise measurement of light signals with software to accurately model measurement configuration and rapidly solve a sequence of challenging inverse problems. We have developed and validated a design approach and performed system validation based on radiative transfer theory for determination of absorption coefficient, scattering coefficient, and anisotropy factor without using an integrating sphere. Accurate and rapid determination of parameters and spectra is achieved for microsphere suspension samples by combining photodiode-based measurement of four signals with the Monte Carlo simulation and perturbation-based inverse calculations. The three parameters of microsphere suspension samples have been determined from the measured signals as functions of wavelength from 400 to 800 nm and agree with calculated results based on the Mie theory. It has been shown that the inverse problems in the cases of microsphere suspension samples are well posed with convex cost functions to yield unique solutions, and it takes about 1 min to obtain the three parameters per wavelength.

Realistic optical cell modeling and diffraction imaging simulation for study of optical and morphological parameters of nucleus.

Coherent light scattering presents complex spatial patterns that depend on morphological and molecular features of biological cells. We present a numerical approach to establish realistic optical cell models for generating virtual cells and accurate simulation of diffraction images that are comparable to measured data of prostate cells. With a contourlet transform algorithm, it has been shown that the simulated images and extracted parameters can be used to distinguish virtual cells of different nuclear volumes and refractive indices against the orientation variation. These results demonstrate significance of the new approach for development of rapid cell assay methods through diffraction imaging.

Comparison study of distinguishing cancerous and normal prostate epithelial cells by confocal and polarization diffraction imaging.

Accurate classification of malignant cells from benign ones can significantly enhance cancer diagnosis and prognosis by detection of circulating tumor cells (CTCs). We have investigated two approaches of quantitative morphology and polarization diffraction imaging on two prostate cell types to evaluate their feasibility as single-cell assay methods toward CTC detection after cell enrichment. The two cell types have been measured by a confocal imaging method to obtain their three-dimensional morphology parameters and by a polarization diffraction imaging flow cytometry (p-DIFC) method to obtain image texture parameters. The support vector machine algorithm was applied to examine the accuracy of cell classification with the morphology and diffraction image parameters. Despite larger mean values of cell and nuclear sizes of the cancerous prostate cells than the normal ones, it has been shown that the morphologic parameters cannot serve as effective classifiers. In contrast, accurate classification of the two prostate cell types can be achieved with high classification accuracies on measured data acquired separately in three measurements. These results provide strong evidence that the p-DIFC method has the potential to yield morphology-related "fingerprints" for accurate and label-free classification of the two prostate cell types.

Acquisition of cross-polarized diffraction images and study of blurring effect by one time-delay-integration camera.

Blurred diffraction images acquired from flowing particles affect the measurement of fringe patterns and subsequent analysis. An imaging unit with one time-delay-integration (TDI) camera has been developed to acquire two cross-polarized diffraction images. It was shown that selected elements of Mueller matrix of single scatters can be imaged with pixel matching precision in this configuration. With the TDI camera, the effect of blurring on imaging of scattered light propagating along the side directions was found to be much more significant for biological cells than microspheres. Despite blurring, classification of MCF-7 and K562 cells is feasible since the effect has similar influence on extracted image parameters. Furthermore, image blurring can be useful for analysis of the correlations among texture parameters for characterization of diffraction images from single cells. The results demonstrate that with one TDI camera the polarization diffraction imaging flow cytometry can be significantly improved and angular distribution of selected Mueller matrix elements can be accurately measured for rapid and morphology-based assay of particles and cells without fluorescent labeling.

Automatic lung tumor segmentation on PET/CT images using fuzzy Markov random field model.

The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice's similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

A quantitative method for measurement of HL-60 cell apoptosis based on diffraction imaging flow cytometry technique.

A quantitative method for measurement of apoptosis in HL-60 cells based on polarization diffraction imaging flow cytometry technique is presented in this paper. Through comparative study with existing methods and the analysis of diffraction images by a gray level co-occurrence matrix algorithm (GLCM), we found 4 GLCM parameters of contrast (CON), cluster shade (CLS), correlation (COR) and dissimilarity (DIS) exhibit high sensitivities as the apoptotic rates. It was further demonstrated that the CLS parameter correlates significantly (R(2) = 0.899) with the degree of nuclear fragmentation and other three parameters showed a very good correlations (R(2) ranges from 0.69 to 0.90). These results demonstrated that the new method has the capability for rapid and accurate extraction of morphological features to quantify cellular apoptosis without the need for cell staining.

Automatic quantitative analysis of morphology of apoptotic HL-60 cells.

Morphological identification is a widespread procedure to assess the presence of apoptosis by visual inspection of the morphological characteristics or the fluorescence images. The procedure is lengthy and results are observer dependent. A quantitative automatic analysis is objective and would greatly help the routine work. We developed an image processing and segmentation method which combined the Otsu thresholding and morphological operators for apoptosis study. An automatic determination method of apoptotic stages of HL-60 cells with fluorescence images was developed. Comparison was made between normal cells, early apoptotic cells and late apoptotic cells about their geometric parameters which were defined to describe the features of cell morphology. The results demonstrated that the parameters we chose are very representative of the morphological characteristics of apoptotic cells. Significant differences exist between the cells in different stages, and automatic quantification of the differences can be achieved.

Analysis of diffraction imaging in non-conjugate configurations.

Diffraction imaging of scattered light allows extraction of information on scatterer's morphology. We present a method for accurate simulation of diffraction imaging of single particles by combining rigorous light scattering model with ray-tracing software. The new method has been validated by comparison to measured images of single microspheres. Dependence of fringe patterns on translation of an objective based imager to off-focus positions has been analyzed to clearly understand diffraction imaging with multiple optical elements. The calculated and measured results establish unambiguously that diffraction imaging should be pursued in non-conjugate configurations to ensure accurate sampling of coherent light distribution from the scatterer.