Browsing by Subject "Radiology, Nuclear Medicine & Medical Imaging"

Now showing 1 - 10 of 10
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    Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy.
    (Physics in medicine and biology, 2019-01-08) Lafata, Kyle J; Hong, Julian C; Geng, Ruiqi; Ackerson, Bradley G; Liu, Jian-Guo; Zhou, Zhennan; Torok, Jordan; Kelsey, Chris R; Yin, Fang-Fang
    The purpose of this work was to investigate the potential relationship between radiomic features extracted from pre-treatment x-ray CT images and clinical outcomes following stereotactic body radiation therapy (SBRT) for non-small-cell lung cancer (NSCLC). Seventy patients who received SBRT for stage-1 NSCLC were retrospectively identified. The tumor was contoured on pre-treatment free-breathing CT images, from which 43 quantitative radiomic features were extracted to collectively capture tumor morphology, intensity, fine-texture, and coarse-texture. Treatment failure was defined based on cancer recurrence, local cancer recurrence, and non-local cancer recurrence following SBRT. The univariate association between each radiomic feature and each clinical endpoint was analyzed using Welch's t-test, and p-values were corrected for multiple hypothesis testing. Multivariate associations were based on regularized logistic regression with a singular value decomposition to reduce the dimensionality of the radiomics data. Two features demonstrated a statistically significant association with local failure: Homogeneity2 (p  =  0.022) and Long-Run-High-Gray-Level-Emphasis (p  =  0.048). These results indicate that relatively dense tumors with a homogenous coarse texture might be linked to higher rates of local recurrence. Multivariable logistic regression models produced maximum [Formula: see text] values of [Formula: see text], and [Formula: see text], for the recurrence, local recurrence, and non-local recurrence endpoints, respectively. The CT-based radiomic features used in this study may be more associated with local failure than non-local failure following SBRT for stage I NSCLC. This finding is supported by both univariate and multivariate analyses.
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    High-speed widefield photoacoustic microscopy of small-animal hemodynamics.
    (Biomedical optics express, 2018-10) Lan, Bangxin; Liu, Wei; Wang, Ya-Chao; Shi, Junhui; Li, Yang; Xu, Song; Sheng, Huaxin; Zhou, Qifa; Zou, Jun; Hoffmann, Ulrike; Yang, Wei; Yao, Junjie
    Optical-resolution photoacoustic microscopy (OR-PAM) has become a popular tool in small-animal hemodynamic studies. However, previous OR-PAM techniques variously lacked a high imaging speed and/or a large field of view, impeding the study of highly dynamic physiologic and pathophysiologic processes over a large region of interest. Here we report a high-speed OR-PAM system with an ultra-wide field of view, enabled by an innovative water-immersible hexagon-mirror scanner. By driving the hexagon-mirror scanner with a high-precision DC motor, the new OR-PAM has achieved a cross-sectional frame rate of 900 Hz over a 12-mm scanning range, which is 3900 times faster than our previous motor-scanner-based system and 10 times faster than the MEMS-scanner-based system. Using this hexagon-scanner-based OR-PAM system, we have imaged epinephrine-induced vasoconstriction in the whole mouse ear and vascular reperfusion after ischemic stroke in the mouse cortex in vivo, with a high spatial resolution and high volumetric imaging speed. We expect that the hexagon-scanner-based OR-PAM system will become a powerful tool for small animal imaging where the hemodynamic responses over a large field of view are of interest.
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    Learned sensing: jointly optimized microscope hardware for accurate image classification.
    (Biomedical optics express, 2019-12) Muthumbi, Alex; Chaware, Amey; Kim, Kanghyun; Zhou, Kevin C; Konda, Pavan Chandra; Chen, Richard; Judkewitz, Benjamin; Erdmann, Andreas; Kappes, Barbara; Horstmeyer, Roarke
    Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to re-design the microscope's hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a "physical layer" to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.
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    Organ doses from CT localizer radiographs: Development, validation, and application of a Monte Carlo estimation technique
    (MEDICAL PHYSICS, 2019-11-01) Hoye, Jocelyn; Sharma, Shobhit; Zhang, Yakun; Fu, Wanyi; Ria, Francesco; Kapadia, Anuj; Segars, W Paul; Wilson, Joshua; Samei, Ehsan
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    Radiofrequency Ablation Duration per Tumor Volume May Correlate with Overall Survival in Solitary Hepatocellular Carcinoma Patients Treated with Radiofrequency Ablation Plus Lyso-Thermosensitive Liposomal Doxorubicin
    (JOURNAL OF VASCULAR AND INTERVENTIONAL RADIOLOGY, 2019-12-01) Celik, Haydar; Wakim, Paul; Pritchard, William F; Castro, Meryll; Leonard, Shelby; Karanian, John W; Dewhirst, Mark W; Lencioni, Riccardo; Wood, Bradford J
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    Single-Fraction Radiosurgery for 4 or More Brain Metastases.
    (International journal of radiation oncology, biology, physics, 2016-10) Limon, D; Kim, GJ; McSherry, F; Herndon, J; Fecci, PE; Adamson, J; Sampson, JH; Floyd, SR; Wang, Z; Vlahovic, G; Yin, FF; Kirkpatrick, JP
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    Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology.
    (Physics in medicine and biology, 2018-11-08) Lafata, Kyle; Cai, Jing; Wang, Chunhao; Hong, Julian; Kelsey, Chris R; Yin, Fang-Fang
    The purpose of this research was to study the sensitivity of Computed Tomography (CT) radiomic features to motion blurring and signal-to-noise ratios (SNR), and investigate its downstream effect regarding the classification of non-small cell lung cancer (NSCLC) histology. Forty-three radiomic features were considered and classified into one of four categories: Morphological, Intensity, Fine Texture, and Coarse Texture. First, a series of simulations were used to study feature-sensitivity to changes in spatial-temporal resolution. A dynamic digital phantom was used to generate images with different breathing amplitudes and SNR, from which features were extracted and characterized relative to initial simulation conditions. Stage I NSCLC patients were then retrospectively identified, from which three different acquisition-specific feature-spaces were generated based on free-breathing (FB), average-intensity-projection (AIP), and end-of-exhalation (EOE) CT images. These feature-spaces were derived to cover a wide range of spatial-temporal tradeoff. Normalized percent differences and concordance correlation coefficients (CCC) were used to assess the variability between the 3D and 4D acquisition techniques. Subsequently, three corresponding acquisition-specific logistic regression models were developed to classify lung tumor histology. Classification performance was compared between the different data-dependent models. Simulation results demonstrated strong linear dependences (p  >  0.95) between respiratory motion and morphological features, as well as between SNR and texture features. The feature Short Run Emphasis was found to be particularly stable to both motion blurring and changes in SNR. When comparing FB-to-EOE, 37% of features demonstrated high CCC agreement (CCC  >  0.8), compared to only 30% for FB-to-AIP. In classifying tumor histology, EoE images achieved an average AUC, Accuracy, Sensitivity, and Specificity of [Formula: see text], respectively. FB images achieved respective values of [Formula: see text], and AIP images achieved respective values of [Formula: see text]. Several radiomic features have been identified as being relatively robust to spatial-temporal variations based on both simulation data and patient data. In general, features that were sensitive to motion blurring were not necessarily the same features that were sensitive to changes in SNR. Our modeling results suggest that the EoE phase of a 4DCT acquisition may provide useful radiomic information, particularly for features that are highly sensitive to respiratory motion.
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    The Radiologist's Role in Tumor Staging Response
    (RADIOLOGY, 2016-08-01) Glastonbury, Christine M; Sullivan, Daniel C
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    Validation of algorithmic CT image quality metrics with preferences of radiologists
    (MEDICAL PHYSICS, 2019-11-01) Cheng, Yuan; Abadi, Ehsan; Smith, Taylor Brunton; Ria, Francesco; Meyer, Mathias; Marin, Daniele; Samei, Ehsan
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    Wide-field whole eye OCT system with demonstration of quantitative retinal curvature estimation
    (Biomedical Optics Express, 2019-01-01) McNabb, Ryan P; Polans, James; Keller, Brenton; Jackson-Atogi, Moseph; James, Charlene L; Vann, Robin R; Izatt, Joseph A; Kuo, Anthony N