Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window.

OBJECTIVES: To explore the performance of low-dose computed tomography (LDCT) with deep learning reconstruction (DLR) for the improvement of image quality and assessment of lung parenchyma. METHODS: Sixty patients underwent chest regular-dose CT (RDCT) followed by LDCT during the same examination. RDCT images were reconstructed with hybrid iterative reconstruction (HIR) and LDCT images were reconstructed with HIR and DLR, both using lung algorithm. Radiation exposure was recorded. Image noise, signal-to-noise ratio, and subjective image quality of normal and abnormal CT features were evaluated and compared using the Kruskal-Wallis test with Bonferroni correction. RESULTS: The effective radiation dose of LDCT was significantly lower than that of RDCT (0.29 ± 0.03 vs 2.05 ± 0.65 mSv, p < 0.001). The mean image noise ± standard deviation was 33.9 ± 4.7, 39.6 ± 4.3, and 31.1 ± 3.2 HU in RDCT, LDCT HIR-Strong, and LDCT DLR-Strong, respectively (p < 0.001). The overall image quality of LDCT DLR-Strong was significantly better than that of LDCT HIR-Strong (p < 0.001) and comparable to that of RDCT (p > 0.05). LDCT DLR-Strong was comparable to RDCT in evaluating solid nodules, increased attenuation, linear opacity, and airway lesions (all p > 0.05). The visualization of subsolid nodules and decreased attenuation was better with DLR than with HIR in LDCT but inferior to RDCT (all p < 0.05). CONCLUSION: LDCT DLR can effectively reduce image noise and improve image quality. LDCT DLR provides good performance for evaluating pulmonary lesions, except for subsolid nodules and decreased lung attenuation, compared to RDCT-HIR. CLINICAL RELEVANCE STATEMENT: The study prospectively evaluated the contribution of DLR applied to chest low-dose CT for image quality improvement and lung parenchyma assessment. DLR can be used to reduce radiation dose and keep image quality for several indications. KEY POINTS: • DLR enables LDCT maintaining image quality even with very low radiation doses. • Chest LDCT with DLR can be used to evaluate lung parenchymal lesions except for subsolid nodules and decreased lung attenuation. • Diagnosis of pulmonary emphysema or subsolid nodules may require higher radiation doses.

Accuracy of subtraction fractional flow reserve with computed tomography in identifying early revascularization in patients with coronary artery disease.

OBJECTIVES: The diagnostic performance of fractional flow reserve with computed tomography (FFR-CT) is affected by the presence of calcified plaque. Subtraction can remove the influence of calcification in coronary computed tomography angiography (CCTA) to increase confidence in the diagnosis of coronary artery stenosis. Our purpose is to investigate the accuracy of post-subtraction FFR-CT in predicting early revascularization. DESIGN: Based on CCTA data of 237 vessels from 79 patients with coronary artery disease, subtraction CCTA images were obtained at a local post-processing workstation, and the conventional and post-subtraction FFR-CT measurements and the difference in proximal and distal FFR-CT values of the narrowest segment of the vessel (ΔFFR-CT) were analyzed for their accuracy in predicting early coronary artery hemodynamic reconstruction. RESULTS: With FFR-CT ≤ 0.8 as the criterion, the accuracy of conventional and post-subtraction FFR-CT measurements in predicting early revascularization was 73.4% and 77.2% at the patient level, and 64.6% and 72.2% at the vessel level, respectively. The specificity of post-subtraction FFR-CT measurements was significantly higher than that of conventional FFR-CT at both the patient and vessel levels (P of 0.013 and 0.015, respectively). At the vessel level, the area under the curve of receiver operating characteristic was 0.712 and 0.797 for conventional and post-subtraction ΔFFR-CT, respectively, showing a difference (P = 0.047), with optimal cutoff values of 0.07 and 0.11, respectively. CONCLUSION: The post-subtraction FFR-CT measurements enhance the specificity in predicting early revascularization. The post-subtraction ΔFFR-CT value of the stenosis segment > 0.11 may be an important indicator for early revascularization.

Tunable two-step shape and dimensional changes with temperature of a PNIPAM/CNC hydrogel.

PNIPAM (poly(N-isopropylacrylamide)), a well-studied thermo-responsive polymer, undergoes conformational transition around 32 °C. On the other hand, cellulose nanocrystals (CNCs), as a promising and biocompatible material, has rarely been introduced to the PNIPAM-based fibrous hydrogel system. CNCs' impact on the temperature responsive behaviors of hydrogels, either in single layer or bilayer hydrogel systems, is yet to be investigated. In this work, stable well dispersed PNIPAM/CNC suspensions (with various CNC proportions) are prepared and electrospun into nanofiber membranes. The corresponding hydrogels are then obtained via UV-induced crosslinking. CNCs are found to exert a significant constraint effect on hydrogel swelling when it exceeded 5 wt% but a negligible effect on contraction. The difference between hydrogels with various CNC proportions regarding their temperature responsive behaviors is utilized to fabricate bilayer hydrogels. These bilayer samples are capable of generating 3D geometries when they come into contact with water for the first time via anisotropic swelling between the two layers and changing their dimension reversibly in the following swelling and contraction. In addition, these geometries are found to be highly tunable via the finely tuned thickness ratio between the two layers. This promising feature would significantly extend the application of these materials in tissue engineering where a controllable geometry of the culture substrate is of great importance.

Subtraction improves the accuracy of coronary CT angiography for detecting obstructive disease in severely calcified segments.

OBJECTIVE: To investigate the accuracy, diagnostic confidence, and interobserver agreement of subtraction coronary CT angiography (CCTA) versus invasive coronary angiography on 320-row CT in coronary segments with severe or non-severe calcification. MATERIALS/METHODS: Sixty-four patients (33 men, 66.6 ± 8.2 years) with suspected coronary artery disease (CAD) were prospectively enrolled from October 2019 to June 2020. The cross-sectional circumferential extent of calcification was used to classify calcified segments as non-severely ( < 180°) or severely calcified ( ≥ 180°). Three independent, blinded radiologists evaluated the severity of coronary stenosis. Interobserver agreement was evaluated using Fleiss' kappa (κ). A multiple-reader multiple-case receiver operating characteristic (ROC) method was conducted, and diagnostic accuracy was measured using the mean areas under the ROC curves (AUCs), with ≥ 50% stenosis as a cut-off. Diagnostic confidence, diagnostic accuracy, and interobserver agreement were compared between CCTA with or without subtraction information in severely and non-severely calcified segments. RESULTS: In cases with severe calcification (51 patients, 146 segments), CCTA with subtraction information achieved better diagnostic accuracy (per-patient AUC: 0.73 vs 0.57, p = 0.03; per-segment AUC: 0.85 vs 0.62, p = 0.01), diagnostic confidence (3.7 vs 2.6, p < 0.001), and interobserver agreement (κ: 0.59 vs 0.30). Diagnostic accuracy (per-patient AUC: 0.81 vs 0.93, p = 0.30; per-patient AUC: 0.79 vs 0.82, p = 0.54) was not increased in cases with non-severe calcification (13 patients, 190 segments). CONCLUSIONS: CCTA with subtraction information achieved better diagnostic accuracy in cases of severe calcification (circumferential extent ≥ 180°). However, for non-severe calcification (circumferential extent < 180°), the effect of calcium subtraction was unclear, as it did not improve diagnostic accuracy. KEY POINTS: • Subtraction coronary CT angiography achieves better diagnostic accuracy, higher diagnostic confidence, and increased interobserver agreement for severe calcification (circumferential extent ≥ 180°). • Calcium subtraction does not improve the diagnostic accuracy of coronary CT angiography for calcification with a circumferential extent of < 180°.

Tissue ultrasound imaging based on wavelet correlation analysis and pulse-inversion technique.

BACKGROUND: Pulse-inversion-based tissue harmonic imaging has been utilized for many years because it can effectively eliminate the harmonic leakage and produce low side-lobe. However, the pulse inversion method is sensitive to imaging object movements, which may result in motion artifacts. Spatial resolution and contrast were limited. OBJECTIVE: To improve ultrasound image quality by a new pulse-inversion-based tissue harmonic imaging technique. METHODS: Continuous wavelet transform is applied to investigate the correlation between mother wavelet and the received echoes from two opposite pulses. To get a better correlation, a novel mother wavelet named 'tissue wavelet' is designed based on the Khokhlov-Zabolotskaya- Kuznetsov (KZK) wave equation. Radio frequency data were obtained from open Ultrasonix SonixTouch imaging system. Experiments were carried on ultrasonic tissue phantom, human carotid artery and human liver. RESULTS: The average improvement of lateral spatial resolution is 49.52% compared to pulse-inversion-based tissue second-harmonic Imaging (PIHI). Contrast ratio (CR) and contrast-to-noise ratio (CNR) increased by 5.55 dB and 1.40 dB over PIHI. Tissue wavelet performs better than Mexh and Morl wavelet in lateral spatial resolution, CR, and CNR. CONCLUSION: The proposed technique effectively improves the imaging quality in lateral spatial resolution, CR, and CNR.

In-Domain GAN Inversion for Faithful Reconstruction and Editability.

Generative Adversarial Networks (GANs) have significantly advanced image synthesis through mapping randomly sampled latent codes to high-fidelity synthesized images. However, applying well-trained GANs to real image editing remains challenging. A common solution is to find an approximate latent code that can adequately recover the input image to edit, which is also known as GAN inversion. To invert a GAN model, prior works typically focus on reconstructing the target image at the pixel level, yet few studies are conducted on whether the inverted result can well support manipulation at the semantic level. This work fills in this gap by proposing in-domain GAN inversion, which consists of a domain-guided encoder and a domain-regularized optimizer, to regularize the inverted code in the native latent space of the pre-trained GAN model. In this way, we manage to sufficiently reuse the knowledge learned by GANs for image reconstruction, facilitating a wide range of editing applications without any retraining. We further make comprehensive analyses on the effects of the encoder structure, the starting inversion point, as well as the inversion parameter space, and observe the trade-off between the reconstruction quality and the editing property. Such a trade-off sheds light on how a GAN model represents an image with various semantics encoded in the learned latent distribution.

Spatial Steerability of GANs via Self-Supervision from Discriminator.

Generative models make huge progress to the photorealistic image synthesis in recent years. To enable human to steer the image generation process and customize the output, many works explore the interpretable dimensions of the latent space in GANs. Existing methods edit the attributes of the output image such as orientation or color scheme by varying the latent code along certain directions. However, these methods usually require additional human annotations for each pretrained model, and they mostly focus on editing global attributes. In this work, we propose a self-supervised approach to improve the spatial steerability of GANs without searching for steerable directions in the latent space or requiring extra annotations. Specifically, we design randomly sampled Gaussian heatmaps to be encoded into the intermediate layers of generative models as spatial inductive bias. Along with training the GAN model from scratch, these heatmaps are being aligned with the emerging attention of the GAN's discriminator in a self-supervised learning manner. During inference, users can interact with the spatial heatmaps in an intuitive manner, enabling them to edit the output image by adjusting the scene layout, moving, or removing objects. Moreover, we incorporate DragGAN into our framework, which facilitates fine-grained manipulation within a reasonable time and supports a coarse-to-fine editing process. Extensive experiments show that the proposed method not only enables spatial editing over human faces, animal faces, outdoor scenes, and complicated multi-object indoor scenes but also brings improvement in synthesis quality. Code, models, and demo video are available at https://genforce.github.io/SpatialGAN/.

A Facile Strategy for the Preparation of N-Doped TiO2 with Oxygen Vacancy via the Annealing Treatment with Urea.

Although titanium dioxide (TiO2) has a wide range of potential applications, the photocatalytic performance of TiO2 is limited by both its limited photoresponse range and fast recombination of the photogenerated charge carriers. In this work, the preparation of nitrogen (N)-doped TiO2 accompanied by the introduction of oxygen vacancy (Vo) has been achieved via a facile annealing treatment with urea as the N source. During the annealing treatment, the presence of urea not only realizes the N-doping of TiO2 but also creates Vo in N-doped TiO2 (N-TiO2), which is also suitable for commercial TiO2 (P25). Unexpectedly, the annealing treatment-induced decrease in the specific surface area of N-TiO2 is inhibited by the N-doping and, thus, more active sites are maintained. Therefore, both the N-doping and formation of Vo as well as the increased active sites contribute to the excellent photocatalytic performance of N-TiO2 under visible light irradiation. Our work offers a facile strategy for the preparation of N-TiO2 with Vo via the annealing treatment with urea.

Deep learning reconstruction improves the image quality of low-dose temporal bone CT with otitis media and mastoiditis patients.

Objective: To evaluate the image quality of low-dose temporal bone computed tomography (CT) in otitis media and mastoiditis patients by using deep learning reconstruction (DLR). Materials and methods: A total of ninety-seven temporal bones from 53 consecutive adult patients who had suspected otitis media and mastoiditis and underwent temporal bone CT were prospectively enrolled. All patients underwent high resolution CT protocol (group A) and an additional low-dose protocol (group B). In group A, high resolution data were reconstructed by filter back projection (FBP). In group B, low-dose data were reconstructed by DLR mild (B1), DLR standard (B2) and DLR strong (B3). The objective image quality was analyzed by measuring the CT value and image noise on the transverse image and calculating the signal-to-noise ratio (SNR) on incudomallear joint, retroauricular muscle, vestibule and subcutaneous fat. Subjective image quality was analyzed by using a five-point scale to evaluate nine anatomical structures of middle and inner ear. The number of temporal bone lesions which involved in five structures of middle ear were assessed in group A, B1, B2 and B3 images. Results: There were no significant differences in the CT values of the four reconstruction methods at four structures (all p > 0.05). The DLR group B1, B2 and B3 had significantly less image noise and a significantly higher SNR than group A at four structures (all p < 0.001). The group B1 had comparable subjective image quality as group A in nine structures (all p > 0.05), however, the group B3 had lower subjective image quality than group A in modiolus, spiral osseous lamina and stapes (all p < 0.001), the group B2 had lower subjective image quality than group A in modiolus and spiral osseous lamina (both p < 0.05). The number of temporal bone lesions which involved in five structures for group A, B1 and B2 images were no significant difference (all p > 0.05), however, the number of temporal bone lesions which involved in mastoid for group B3 images were significantly more than group A (p < 0.05). The radiation dose of high resolution CT protocol and low-dose protocol were 0.55 mSv and 0.11 mSv, respectively. Conclusion: Compared with high resolution CT protocol, in the low-dose protocol of temporal bone CT, DLR mild and standard could improve the objective image quality, maintain good subjective image quality and satisfy clinical diagnosis of otitis media and mastoiditis patients.

A proteomic study of the effect of UV-B on the regulatory mechanism of flavonoids metabolism in pea seedlings.

This study aimed to investigate the mechanism of response of pea seedlings to UV-B stress from a proteomic perspective. In this experiment, we measured the growth of pea seedlings in two groups affected by UV-B and unaffected by UV-B and conducted proteomic analysis. The results showed that the ascorbic acid content of UV-B-irradiated pea seedlings increased by 19.0%; the relative content of flavonoids increased by 112.4%; the length of edible parts decreased by 14.2%, and the elongation of roots increased by 11.4%. Proteomics studies showed a significant increase in the levels of CHI, F3'5'H, F3H, F3'H, C4H, and CHR, which are key enzymes for flavonoid synthesis. RT-qPCR indicated that the expression of the regulatory genes of these enzymes was significantly upregulated. This study provided a basis for further studies on the flavonoid response mechanism in pea seedlings during UV stress.

GH-Feat: Learning Versatile Generative Hierarchical Features From GANs.

Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and reproduce the real data distribution. Through that, a hierarchical visual feature with multi-level semantics spontaneously emerges. In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones. We first train an encoder by considering the pre-trained StyleGAN generator as a learned loss function. The visual features produced by our encoder, termed as Generative Hierarchical Features (GH-Feat), highly align with the layer-wise GAN representations, and hence describe the input image adequately from the reconstruction perspective. Extensive experiments support the versatile transferability of GH-Feat across a range of applications, such as image editing, image processing, image harmonization, face verification, landmark detection, layout prediction, image retrieval, etc. We further show that, through a proper spatial expansion, our developed GH-Feat can also facilitate fine-grained semantic segmentation using only a few annotations. Both qualitative and quantitative results demonstrate the appealing performance of GH-Feat. Code and models are available at https://genforce.github.io/ghfeat/.

ARU-GAN: U-shaped GAN based on Attention and Residual connection for super-resolution reconstruction.

Plane-wave ultrasound imaging technology offers high-speed imaging but lacks image quality. To improve the image spatial resolution, beam synthesis methods are used, which often compromise the temporal resolution. Herein, we propose ARU-GAN, a super-resolution reconstruction model based on residual connectivity and attention mechanisms, to address this issue. ARU-GAN comprises a Full-scale Skip-connection U-shaped Generator (FSUG) with an attention mechanism and a Residual Attention Patch Discriminator (RAPD). The former captures global and local features of the image by using full-scale skip-connections and attention mechanisms. The latter focuses on changes in the image at different scales to enhance its discriminative ability at the patch level. ARU-GAN was trained using a combined loss function on the Plane-Wave Imaging Challenge in Medical Ultrasound (PICMUS) 2016 dataset, which includes three types of targets: point targets, cyst targets, and in-vivo targets. Compared to Coherent Plane-Wave Compounding (CPWC), ARU-GAN achieved a reduction in Full Width at Half Maximum (FWHM) by 5.78%-20.30% on point targets, improved Contrast (CR) by 7.59-11.29 percentage points, and Contrast to Noise Ratio (CNR) by 30.58%-45.22% on cyst targets. On in-vivo target, ARU-GAN improved the Peak Signal-to-Noise Ratio (PSNR) by 11.94%, the Complex-Wavelet Structural Similarity Index Measurement (CW-SSIM) by 17.11%, and the Normalized Cross Correlation (NCC) by at least 2.17% compared to existing deep learning methods. In conclusion, ARU-GAN is a promising model for the super-resolution reconstruction of plane-wave medical ultrasound images. It provides a novel solution for improving image quality, which is essential for clinical practice.

Metal-organic rotaxane frameworks constructed from a cucurbit[8]uril-based ternary complex for the selective detection of antibiotics.

Herein we report two 2D layered metal-organic rotaxane frameworks (MORFs), WUST-1 and WUST-2, constituted by a ternary host-guest complex based on cucurbit[8]uril (CB[8]) and an (E)-1-methyl-4-[4-(pyridin-4-yl)styryl] pyridinium (G1) ligand, and different metal ions and auxiliary linkers. Both MORFs are stable in water and highly fluorescence emissive, and can selectively sense nitrofurazone with low detection limits.

Implicit Neural Representations With Structured Latent Codes for Human Body Modeling.

This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves remarkable view synthesis quality given dense input views. However, the representation learning will be ill-posed if the views are highly sparse. To solve this ill-posed problem, our key idea is to integrate observations over video frames. To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated. The deformable mesh also provides geometric guidance for the network to learn 3D representations more efficiently. Furthermore, we combine Neural Body with implicit surface models to improve the learned geometry. To evaluate our approach, we perform experiments on both synthetic and real-world data, which show that our approach outperforms prior works by a large margin on novel view synthesis and 3D reconstruction. We also demonstrate the capability of our approach to reconstruct a moving person from a monocular video on the People-Snapshot dataset.

Differences in microbial communities and potato growth in two soil types under organic cultivation.

Organic agriculture plays a positive role in promoting genetic diversity, including living organisms, plants, and cultivated crops in the soil. However, few comparative studies reported whether different soil types were suitable for organic cultivation. In this study, loam and clay-loam soils under continuous organic cultivation were analyzed. The results showed that there were no significant differences between two soil types in soil pH, bulk density, total porosity, moisture content and three soil phases. The capillary porosity and organic matter content of loam were significantly higher than those of clay-loam. Compared with clay-loam soil, the contents of total nitrogen, phosphorus, potassium, calcium, zinc and silicon in loam soil were also significantly higher. The microbial diversity was higher in loam and the dominant microbes differed between the two soils. Glycosyl transferases and carbohydrate esterases were enriched in loam, whereas glycoside hydrolases and carbohydrate-binding modules were enriched in clay loam. The potato yield in loam was significantly higher than that in clay loam. Among the tuber quality indicators, the protein content of potatoes in loam was higher than that in clay-loam, but the reducing sugar content was lower for loam than for clay-loam. In conclusion, compared with clay loam, loam was more suitable for organic cultivation of potatoes on account of the high contents of nitrogen, phosphorus, and potassium and the rich microbial community, thus promoting a high yield of tubers. This study provided a theoretical reference for the selection of soil type suitable for organic cultivation.

Insights into the Semiconductor SERS Activity: The Impact of the Defect-Induced Energy Band Offset and Electron Lifetime Change.

The significant boost in surface-enhanced Raman scattering (SERS) by the chemical enhancement of semiconducting oxides is a pivotal finding. It offers a prospective path toward high uniformity and low-cost SERS substrates. However, a detailed understanding of factors that influence the charge transfer process is still insufficient. Herein, we reveal the important role of defect-induced band offset and electron lifetime change in SERS evolution observed in a MoO3 oxide semiconductor. By modulating the density of oxygen vacancy defects using ultraviolet (UV) light irradiation, SERS is found to be improved with irradiation time in the first place, but such improvement later deteriorates for prolonged irradiation even if more defects are generated. Insights into the observed SERS evolution are provided by ultraviolet photoelectron spectroscopy and femtosecond time-resolved transient absorption spectroscopy measurements. Results reveal that (1) a suitable offset between the energy band of the substrate and the orbitals of molecules is facilitated by a certain defect density and (2) defect states with relatively long electron lifetime are essential to achieve optimal SERS performance.

Subtraction Improves the Accuracy of Coronary CT Angiography in Patients with Severe Calcifications in Identifying Moderate and Severe Stenosis: A Multicenter Study.

RATIONALE AND OBJECTIVES: To investigate the diagnostic accuracy of subtraction coronary computed tomographic angiography (CCTAsub) in identifying ≥ 50% and ≥ 70% coronary stenosis in patients with different degrees of calcification. MATERIALS AND METHODS: In this study, 180 patients with coronary calcified plaques who underwent both coronary CT angiography and invasive coronary angiography (ICA) were prospectively enrolled at five centers. Patients were divided into three groups according to the Agatston score: group A (low to moderate, < 400), group B (high, 400-999), and group C (very high, ≥ 1000). Diagnostic accuracies estimated by area under the receiver operating characteristic curve (AUC) were compared between conventional CCTA (CCTAcon) and CCTAsub, with ICA as a reference standard. RESULTS: There were 86 patients in group A, 44 in group B, and 50 in group C. In identifying ≥ 70% coronary stenosis, subtraction improved the diagnostic accuracies on a per-segment basis in group B (AUC: 0.80 vs 0.92, p = 0.001) and group C (AUC: 0.75 vs 0.84, p = 0.001) after subtraction. When identifying ≥ 50% coronary stenosis, the per-segment AUC of CCTAsub in group B and C were significantly higher than that in CCTAcon (group B: 0.81 vs 0.92, p < 0.001; group C: 0.77 vs 0.88, p < 0.001). However, no improvement was observed in group A. CONCLUSION: Subtraction achieved better diagnostic accuracy in patients with Agatston score ≥ 400, both in identifying ≥ 50% and ≥ 70% coronary stenosis, which was instructive for the application of subtraction in clinical practice.

Flexible 3D printed microwires and 3D microelectrodes for heart-on-a-chip engineering.

We developed a heart-on-a-chip platform that integrates highly flexible, vertical, 3D micropillar electrodes for electrophysiological recording and elastic microwires for the tissue's contractile force assessment. The high aspect ratio microelectrodes were 3D-printed into the device using a conductive polymer, poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS). A pair of flexible, quantum dots/thermoplastic elastomer nanocomposite microwires were 3D printed to anchor the tissue and enable continuous contractile force assessment. The 3D microelectrodes and flexible microwires enabled unobstructed human iPSC-based cardiac tissue formation and contraction, suspended above the device surface, under both spontaneous beating and upon pacing with a separate set of integrated carbon electrodes. Recording of extracellular field potentials using the PEDOT:PSS micropillars was demonstrated with and without epinephrine as a model drug, non-invasively, along within situmonitoring of tissue contractile properties and calcium transients. Uniquely, the platform provides integrated profiling of electrical and contractile tissue properties, which is critical for proper evaluation of complex, mechanically and electrically active tissues, such as the heart muscle under both physiological and pathological conditions.

Strain Analysis in Patients at High-Risk for COPD Using Four-Dimensional Dynamic-Ventilation CT.

Purpose: To quantitatively identify abnormal lung motion in chronic obstructive pulmonary disease (COPD) using strain analysis, and further clarify the potential differences of deformation in COPD with different severity of airflow limitation. Materials and Methods: Totally, 53 patients at high-risk for COPD were enrolled in this study. All CT examinations were performed on a 320-row MDCT scanner, and strain measurement based on dynamic-ventilation CT data was performed with a computational fluid dynamics analysis software (Micro Vec V3.6.2). The strain-related parameters derived from the whole expiration phase (PSmax-all, PSmean-all, Speedmax-all ), the first 2s of expiration phase (PSmax2s, PSmean2s, Speedmax2s ) were divided respectively by the changes in lung volume to adjust for the degree of expiration. Spearman rank correlation analysis was used to evaluate associations between the strain-related parameters and various spirometric parameters. Comparisons of the strain-related parameters between COPD and non-COPD patients, between GOLD I (mild airflow restriction) and GOLD II-IV (moderate to severe airflow restriction) were made using the Mann-Whitney U-test. Receiver-operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of the strain-related parameters for COPD. P <0.05 was considered statistically significant. Results: Strain-related parameters demonstrated positive correlations with spirometric parameters (ρ=0.275~0.687, P<0.05), suggesting that heterogeneity in lung motion was related to abnormal spirometric results. Strain-related parameters can quantitatively distinguish COPD from non-COPD patients with moderate diagnostic significance with the AUC values ranged from 0.821 to 0.894. Furthermore, parameters of the whole expiration phase (PSmax-all, Speedmax-all) demonstrated significant differences (P=0.005; P=0.04) between COPD patients with mild and moderate to severe airflow limitation. Conclusion: Strain-related parameters derived from dynamic-ventilation CT data covering the whole lung associated with lung function changes in COPD, reflecting the severity of airflow limitation in some degree, even though its utility in severe COPD patients remains to be investigated.