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A correction to the article "Detecting Small Lung Tumors in Mouse Models by Refractive-Index Microradiology" that was published in a 2011 issue is presented.

Cholestatic liver disease is caused by disorders of bile synthesis, secretion, and excretion. Over the long term, progressive liver cell damage from the disease evolves into liver fibrosis and cirrhosis, ultimately leading to liver failure and even cancer. Notably, cholestatic liver disease has a complex pathogenesis that remains relatively unclear. In this study, we generated two mouse models of cholestatic liver disease using a 0.1% 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) diet and α-naphthyl isothiocyanate (ANIT) gavage. Quantitative proteomics using liquid chromatography-tandem mass spectrometry showed that arachidonic acid metabolism was a common pathway in both models. Additionally, serum arachidonic acid concentrations were lower in both models than in the control group. Arachidonic acid supplementation in the diet of DDC model mice significantly reduced the levels of serum markers of cholestasis (alanine aminotransferase, aspartate transaminase, alkaline phosphatase, total bile acid, and total bilirubin) and decreased the degree of bile duct hyperplasia and cholestasis. To elucidate the mechanisms by which arachidonic acid improved bile stasis, we analyzed gene expression after arachidonic acid administration and found that Oatp1 was upregulated in the liver tissue of cholestatic mice. Arachidonic acid also increased Oatp1 expression in AML12 cells, which promoted bile acid uptake. Conclusively, our research showed that arachidonic acid mitigates cholestatic liver disease by upregulating Oatp1, promoting bile acid uptake by hepatocytes and participating in intestinal-hepatic circulation. Overall, these results suggest that supplementing foods with arachidonic acid in the daily diet may be an effective treatment strategy for cholestatic liver disease.

A bisalicylhydrazone based fluorescence probe, bisalicyladehyde benzoylhydrazone (BS-BH), has been designed to detect Al 3+ . It exhibited high sensitivity and selectivity towards Al 3+ in methanol-water media in physiological condition. Large stokes shifts (∼122 nm) and over ∼1000-fold enhanced fluorescence intensity were observed, which was ascribed to the formation of the two relatively independent rigid extended π conjugated systems bridged by biphenyl group when binding with Al 3+ . A 1:2 binding ratio between BS-BH and Al 3+ was shown by Job's plot. Based on the fluorescence titration data, the detection limit was down to 3.50 nM and the association constant was evaluated to be 1.12 × 10 9 M -2 . The plausible fluorescence sensing mechanism of suppressed ESIPT, inhibited PET, activated CHEF and restricted C = N isomerization was confirmed by a variety of spectral experiments and DFT / TD-DFT calculations. The reversibility of recognition of Al 3+ for probe BS-BH was validated by adding Na 2 -EDTA. In addition, the MTT assay showed the good biocompatibility of BS-BH and BS-BH could be used for imaging Al 3+ in living cells., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Synchrotron radiation (SR) is an excellent light source for micro-CT (micro-computed tomography) applications due to its monochromaticity and high brightness, which are crucial for achieving high-resolution imaging. However, when scanning larger objects, the limited field of view (FOV) of SR will lead to data truncation, limiting its utilization efficiency. To address this limitation, this paper proposed a method to integrate conventional X-ray CT data to supplement the truncated SR data for joint reconstruction to improve imaging. We first employ a polynomial transformation to match the image gray levels from the two distinct light sources and then resample these to form joint data. Subsequently, the method derives noise images from the noise characteristics of the projection data to construct image weight constraint that accurately reflects different data quality from two sources. The flexibility of the image weight constraint also allows for its combination with various denoisers to further enhance the reconstruction quality. Experimental results demonstrate that the proposed method can leverage the strengths of both imaging modalities to facilitate larger scale and high-resolution imaging.

Take Home Message: The review summarizes the applications of CT and AI algorithms for prognostic models in IPF and procedures of model construction. It reveals the current limitations and prospects of AI-aid models, and helps clinicians to recognize the AI algorithms and apply them to more clinical work.

Purpose: The strong metal artifacts produced by the electrode needle cause poor image quality, thus preventing physicians from observing the surgical situation during the puncture process. To address this issue, we propose a metal artifact reduction and visualization framework for CT-guided ablation therapy of liver tumors., Methods: Our framework contains a metal artifact reduction model and an ablation therapy visualization model. A two-stage generative adversarial network is proposed to reduce the metal artifacts of intraoperative CT images and avoid image blurring. To visualize the puncture process, the axis and tip of the needle are localized, and then the needle is rebuilt in 3D space intraoperatively., Results: Experiments show that our proposed metal artifact reduction method achieves higher SSIM (0.891) and PSNR (26.920) values than the state-of-the-art methods. The accuracy of ablation needle reconstruction is 2.76 mm average in needle tip localization and 1.64° average in needle axis localization., Conclusion: We propose a novel metal artifact reduction and an ablation therapy visualization framework for CT-guided ablation therapy of liver cancer. The experiment results indicate that our approach can reduce metal artifacts and improve image quality. Furthermore, our proposed method demonstrates the potential for displaying the relative position of the tumor and the needle intraoperatively., (© 2023. CARS.)

Polyethylene oxide (PEO) based polymer electrolytes show promise in expanding the practical applications of lithium (Li) batteries. However, their applications in Li batteries are usually restricted owing to the lack of mechanical strength, poor oxidative stability, and relatively large thickness. Herein, a nanofibrous polyimide (PI) framework enhanced plasticized-PEO solid electrolyte is prepared to realize good mechanical and electrochemical performances. Following the configuration with the PI matrix, this "polymer in polymer" composite electrolyte with a thickness of 17.5 µm exhibits enhanced mechanical strength (13.9 MPa) and outstanding thermal stability. Additionally, it preserves the high ionic conductivity (2.25 × 10 -4  S cm -1 , 25 °C). The Li||Li symmetrical battery with the modified electrolyte could achieve a steady Li plating/stripping of more than 500 h, and the critical current density reaches up to 0.6 mA cm -2 at ambient temperature. The LiFePO 4 batteries delivery favorable capacity of 132.2 mAh g -1 with capacity retentions of 96.4% and 85.9% after 500 and 1000 cycles at 1 C, respectively. Acceptable cycling performance also could be achieved in LiNi 0.5 Co 0. 2 Mn 0. 3 O 2 solid batteries via an inorganic-rich artificial cathode electrolyte interphase., (© 2023 Wiley-VCH GmbH.)

This exploratory study compared vowel space area (VSA) in face-to-face situations and video conference situations using the software Zoom. Twenty native German participants read word lists recorded before and after spontaneous conversation. The overall VSA in Zoom was reduced significantly by 11.9%, with a more reduced VSA before and less reduction after the spontaneous conversation. Of nine peripheral vowels in German, /aː iː yː/ showed a significantly reduced Euclidean distance to the centroid of the vowel space. The observed hypoarticulation is discussed in light of the experimental setup, situational differences, and less involvement in Zoom than in face-to-face situations., (© 2023 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).)

Accurate understanding of permafrost dynamics is critical for evaluating and mitigating impacts that may arise as permafrost degrades in the future; however, existing projections have large uncertainties. Studies of how permafrost responded historically during Earth's past warm periods are helpful in exploring potential future permafrost behavior and to evaluate the uncertainty of future permafrost change projections. Here, we combine a surface frost index model with outputs from the second phase of the Pliocene Model Intercomparison Project to simulate the near-surface (~3 to 4 m depth) permafrost state in the Northern Hemisphere during the mid-Pliocene warm period (mPWP, ~3.264 to 3.025 Ma). This period shares similarities with the projected future climate. Constrained by proxy-based surface air temperature records, our simulations demonstrate that near-surface permafrost was highly spatially restricted during the mPWP and was 93 ± 3% smaller than the preindustrial extent. Near-surface permafrost was present only in the eastern Siberian uplands, Canadian high Arctic Archipelago, and northernmost Greenland. The simulations are similar to near-surface permafrost changes projected for the end of this century under the SSP5-8.5 scenario and provide a perspective on the potential permafrost behavior that may be expected in a warmer world.

With the continuous increase in sewage-sludge production worldwide, the pyrolytic disposal of sludge has received great attention. To build knowledge on the kinetics of pyrolysis, first, sludge was regulated using appropriate amounts of cationic polyacrylamide (CPAM) and sawdust to study their enhancing effect on dehydration. Due to the effects of the charge neutralization and skeleton hydrophobicity, a certain dose of CPAM and sawdust reduced the sludge's moisture content from 80.3% to 65.7%. Next, the pyrolysis characteristics of the dehydrated sludge regulated by CPAM and sawdust were investigated at a heating rate of 10~40 °C/min by using TGA method. The addition of sawdust enhanced the release of volatile substances and reduced the apparent activation energy of the sample. The maximum weight-loss rate decreased with the heating rate, and the DTG curves moved in the direction of high temperature. A model-free method, namely the Starink method, was adopted to calculate the apparent activation energies, which ranged from 135.3 kJ/mol to 174.8 kJ/mol. Combined with the master-plots method, the most appropriate mechanism function ultimately obtained was the nucleation-and-growth model.

Novel multimode thermal therapy by freezing before radio-frequency heating has achieved a desirable therapeutic effect in liver cancer. Compared with surgical resection, ablation treatment has a relatively high risk of tumor recurrence. To monitor tumor progression after ablation, we developed a novel survival analysis framework for survival prediction and efficacy assessment. We extracted preoperative and postoperative MRI radiomics features and vision transformer-based deep learning features. We also combined the immune features extracted from peripheral blood immune responses using flow cytometry and routine blood tests before and after treatment. We selected features using random survival forest and improved the deep Cox mixture (DCM) for survival analysis. To properly accommodate multitype input features, we proposed a self-adapted fully connected layer for locally and globally representing features. We evaluated the method using our clinical dataset. Of note, the immune features rank the highest feature importance and contribute significantly to the prediction accuracy. The results showed a promising C td -index of 0.885 ±0.040 and an integrated Brier score of 0.041 ±0.014, which outperformed state-of-the-art method combinations of survival prediction. For each patient, individual survival probability was accurately predicted over time, which provided clinicians with trustable prognosis suggestions.

Objective: The multimode ablation of liver cancer, which uses radio-frequency heating after a pre-freezing process to treat the tumor, has shown significantly improved therapeutic effects and enhanced anti-tumor immune response. Unlike open surgery, the ablated lesions remain in the body after treatment, so it is critical to assess the immediate outcome and to monitor disease status over time. Here we propose a novel tumor progression prediction method for simultaneous postoperative evaluation and prognosis analysis., Methods: We propose to leverage the intraoperative therapeutic information extracted from thermal dose distribution. For tumors with specific sensitivity reflected in medical images, different thermal doses implicitly indicate the degree of instant damage and long-term inhibition excited under specific ablation energy. We further propose a survival analysis framework for the multimode ablation treatment. It extracts carefully designed features from clinical, preoperative, intraoperative, and postoperative data, then uses random survival forest for feature selection and deep neural networks for survival prediction., Results: We evaluated the proposed methods using clinical data. The results show that our method outperforms the state-of-the-art survival analysis methods with a C-index of 0.855±0.090. The thermal dose information contributes significantly to the prediction accuracy by taking up 21.7% of the overall feature importance., Conclusion: The proposed methods have been demonstrated to be a powerful tool in tumor progression prediction of multimode ablation therapy., Significance: This kind of data-driven prognosis analysis may benefit personalized medicine and simplify the follow-up process.

Polycyclic aromatic hydrocarbons (PAHs) with boron-nitrogen (BN) moieties have attracted tremendous interest due to their intriguing electronic and optoelectronic properties. However, most of the BN-fused π-systems reported to date are difficult to modify and exhibit traditional aggregation-caused quenching (ACQ) characteristics. This phenomenon greatly limits their scope of application. Thus, continuing efforts to seek novel, structurally distinct and functionally diverse structures are highly desirable. Herein, we proposed a one-stone-two-birds strategy including simultaneous exploration of reactivity and tuning of the optical and electronic properties for BN-containing π-skeletons through flexible regioselective functionalization engineering. In this way, three novel functionalized BN luminogens (DPA-BN-BFT, MeO-DPA-BN-BFT and DMA-DPA-BN-BFT) with similar structures were obtained. Intriguingly, DPA-BN-BFT, MeO-DPA-BN-BFT and DMA-DPA-BN-BFT exhibit completely different emission behaviors. Fluorogens DPA-BN-BFT and MeO-DPA-BN-BFT exhibit a typical ACQ effect; in sharp contrast, DMA-DPA-BN-BFT possesses a prominent aggregation induced emission (AIE) effect. To the best of our knowledge, this is the first report to integrate ACQ and AIE properties into one BN aromatic backbone with subtle modified structures. Comprehensive analysis of the crystal structure and theoretical calculations reveal that relatively large twisting angles, multiple intermolecular interactions and tight crystal packing modes endow DMA-DPA-BN-BFT with strong AIE behavior. More importantly, cell imaging demonstrated that luminescent materials DPA-BN-BFT and DMA-DPA-BN-BFT can highly selectively and sensitively detect lipid droplets (LDs) in living MCF-7 cells. Overall, this work provides a new viewpoint of the rational design and synthesis of advanced BN-polycyclic aromatics with AIE features and triggers the discovery of new functions and properties of azaborine chemistry., Competing Interests: The authors declare no competing financial interest., (This journal is © The Royal Society of Chemistry.)

Objective. Subtype classification plays a guiding role in the clinical diagnosis and treatment of non-small-cell lung cancer (NSCLC). However, due to the gigapixel of whole slide images (WSIs) and the absence of definitive morphological features, most automatic subtype classification methods for NSCLC require manually delineating the regions of interest (ROIs) on WSIs. Approach. In this paper, a weakly supervised framework is proposed for accurate subtype classification while freeing pathologists from pixel-level annotation. With respect to the characteristics of histopathological images, we design a two-stage structure with ROI localization and subtype classification. We first develop a method called multi-resolution expectation-maximization convolutional neural network (MR-EM-CNN) to locate ROIs for subsequent subtype classification. The EM algorithm is introduced to select the discriminative image patches for training a patch-wise network, with only WSI-wise labels available. A multi-resolution mechanism is designed for fine localization, similar to the coarse-to-fine process of manual pathological analysis. In the second stage, we build a novel hierarchical attention multi-scale network (HMS) for subtype classification. HMS can capture multi-scale features flexibly driven by the attention module and implement hierarchical features interaction. Results. Experimental results on the 1002-patient Cancer Genome Atlas dataset achieved an AUC of 0.9602 in the ROI localization and an AUC of 0.9671 for subtype classification. Significance. The proposed method shows superiority compared with other algorithms in the subtype classification of NSCLC. The proposed framework can also be extended to other classification tasks with WSIs., (© 2021 Institute of Physics and Engineering in Medicine.)

For the CT iterative reconstruction, choosing the parameters of different regularization terms has been a difficult problem. Transforming the reconstruction problem into constrained optimization can solve this problem, but determining the constraint range and accurately solving it remains a challenge. This paper proposes a CT reconstruction method based on constrained data fidelity term, which estimates the distribution of the constraint function by Taylor expansion to determine the constraint range. We respectively use Douglas-Rachford splitting (DRS) and Projection-based primal-dual algorithm (PPD) to split the reconstruction problem and solve the data fidelity subproblem. This method can accurately estimate the constrained range of data fidelity terms to ensure reconstruction accuracy and use different regularization terms for reconstruction without parameter adjustment. Three regularization terms are used for reconstruction experiments, and simulation results show that the proposed method can converge stably, and its reconstruction quality is better than the filtered back-projection.

Competing Interests: Conflict of interest The authors declare that they have no conflict of interest.

Thermal ablation is a minimally invasive procedure for treating small or unresectable tumors. Although CT is widely used for guiding ablation procedures, yet the contrast of tumors against normal soft tissues is often poor in CT scans, aggravating the accurate thermal ablation. In this paper, we propose a fast MR-CT image registration method to overlay pre-procedural MR (pMR) and pre-procedural CT (pCT) images onto an intra-procedural CT (iCT) image to guide the thermal ablation of liver tumors. At the pre-procedural stage, the Cycle-GAN model with mutual information constraint is employed to generate the synthesized CT (sCT) image from the input pMR. Then, pMR-pCT image registration is carried out via traditional mono-modal sCT-pCT image registration. At the intra-procedural stage, the region of the probe and its artifacts are automatically localized and inpainted in the iCT image. Then, an unsupervised registration network (UR-Net) is used to efficiently align the pCT with the inpainted iCT (inpCT) image. The final transform from pMR to iCT is obtained by concatenating the two estimated transforms, i.e., (i) from pMR image space to pCT image space (via sCT) and (ii) from pCT image space to iCT image space (via inpCT). The proposed method has been evaluated over a real clinical dataset and compared with state-of-the-art methods. Experimental results confirm that the proposed method achieves high registration accuracy with fast computation speed., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Competing Interests: Conflict of interest The authors declare that they have no conflict of interest.

Classification of normal lung tissue, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by pathological images is significant for clinical diagnosis and treatment. Due to the large scale of pathological images and the absence of definitive morphological features between LUAD and LUSC, it is time-consuming, laborious and challenging for pathologists to analyze the microscopic histopathology slides by visual observation. In this paper, a pixel-level annotation-free framework was proposed to classify normal tissue, LUAD and LUSC slides. This framework can be divided into two stages: tumor classification and localization, and subtype classification. In the first stage, EM-CNN was utilized to distinguish tumor slides from normal tissue slides and locate the discriminative regions for subsequent analysis with only image-level labels provided. In the second stage, a multi-scale network was proposed to improve the accuracy of subtype classification. This method achieved an AUC of 0.9978 for tumor classification and an AUC of 0.9684 for subtype classification, showing its superiority in lung pathological image classification compared with other methods.

The potential clinical application of grating-based phase contrast computed tomography (GPCCT) requires moderate scanning time to reduce the radiation dose, which are not met by traditional GPCCT phase stepping (PS) method. Previous studies have proposed the interlaced scanning method to reduce the scanning time. However, due to the projection number demanded by the analysis reconstruction algorithm, the projection and scanning time cannot be further reduced. In this paper, we proposed an iterative algorithm based on the interlaced PS scanning for GPCCT, which was capable in reducing the motion artifacts during reconstruction as the same as the inner focus (IF) method we raised before. Furthermore, the iterative procedure is expected to introduce some machine learning method and allows a lower radiation dose while maintaining the image quality. Our proposed method mainly consists of three steps: 1) Interlaced data acquisition, 2) Phase retrieval, 3) Inner focus iterative reconstruction. Through changing the virtual rotation center and merging high resolution regions, images without severe boundary blurring can be reconstructed with fast scan speed. The experiment result indicates that our method can reconstruct GPCCT data with interlaced PS scanning.

This study proposed a control strategy of the ablation margin using the temperature of the probe, without causing additional damage. Compared with other methods, the proposed strategy is real time and impedance-ignored, thus has a better performance in practice. A theoretical model was established to obtain the temperature distribution during the treatment. Several functions were obtained by fitting the results of the simulation model, with which a preset central temperature curve corresponding to a desired ablation zone was determined to regulate the temperature of the control point. Considering the various impedances in practice, a voltage adjustment method according to the error between the preset central temperature and the practical central temperature was proposed to minimize the effect of impedances. At last, the strategy was verified with phantom experiments. The results show that all the temperatures of the control points reached to 50°C at a specific time and kept for a while, which demonstrated the strategy had a good performance within the error range allowed.

The adrenal glands are important endocrine glands in humans. They are in complex environments with thin vessels around them. It's meaningful to get the accurate dissection before surgery. However, images used in hospitals are now unable to help doctors with many surgeries, which are produced by digital subtraction angiography. In this study, we used a 3D U-Net model to segment the adrenal tumor vessels in 3D computed tomography angiography slices. The model was evaluated by dice similarity coefficient (DSC) and mean intersection over union (MIoU) with the manually labeled ground truth. The DSC in this model is 94.69% and the MIoU is 90.22%.

In this paper, we address the challenge of predicting occupants' different thermal states (cold-uncomfortable, comfortable and hot-uncomfortable) with high accuracy and high flexibility. At present, most solutions are based on traditional average models or traditional personalized models, which generally fail to guarantee accuracy and flexibility at the same time. To address this issue, we introduce a rapid establishment method of a personalized thermal comfort model by using environmental and physiological parameters as inputs. When a model is being built for a new occupant based on pre-collected data of other occupants, the weights of the training data will be changed personally by quantifying the thermal sensation similarities between the target occupant and the occupants in the data set. In order to validate the method, 14 healthy subjects were recruited for experiments, during which four environmental and physiological parameters (air temperature, skin temperature, skin humidity, skin conductance) and their gradients were recorded. The model is based on LightGBM classifier and achieves an average weighted F1 score of 0.893 with a small amount of personal data. The results clarify the effectiveness of this method and also shows the possibility of applying this method to thermal environment control with wearable sensing technology.

Grating-based phase-contrast is a hot topic in recent years owing to its excellent imaging contrast capability on soft tissues. Although it is compatible with conventional X-ray tubes and applicable in many fields, long scanning time, and high radiation dose obstruct its wider use in clinical and medical fields, especially for computed tomography applications. In this study, we solve this challenge by reducing the projection views and compensating the loss of reconstruction quality through dual-dictionary learning algorithm. The algorithm is implemented in two steps. First, estimated high-quality absorption images are obtained from the first dual-quality dictionary learning, which uses the correspondence between high-quality images and low-quality ones reconstructed from highly under-sampled data. Then, the second absorption-phase dual-modality dictionary learning is adopted to yield both estimated phase and absorption images, resulting in complementary information for both modality images. Afterwards the absorption and phase images are gradually improved in iterative reconstructions. By using SSIM RMSE measurements and visual assessment for enlarged regions of interest, our proposed method can improve the resolution of these two modality images and recover smaller structures, as compared to conventional methods.

Numerical simulations provide effective way to acquire detailed temperature field during radiofrequency ablation (RF). Based on the patient's real electrical resistance and RF power, we have designed a theoretical model suitable for clinical treatment planning. The human body is assumed to be two cylinders with the inner cylinder simulating the liver with real liver electrical conductivity, while the electrical conductivity of the out cylinder adjusted to match the real resistance recorded when treated with RFA. The orthogonal-array method has been applied to analyze the impact of the main geometric parameters. Results show that a limited range of model parameters with the same resistance and power condition results in similar prediction of ablation range. In addition, RF heating experiments have been performed in the liver of a live pig to validate this model. The simulated temperature fits well with the real temperature. The comparison of the results predicted using the proposed model and previous models finds that the previous uniform-electrical-conductivity model would significantly underestimates or overestimates the ablation range based on the magnitude of the electrical resistance recorded.

Proper training of convolutional neural networks (CNNs) requires annotated training datasets oflarge size, which are not currently available in CT colonography (CTC). In this paper, we propose a well-designed framework to address the challenging problem of data shortage in the training of 3D CNN for the detection of polyp candidates, which is the first and crucial part of the computer-aided diagnosis (CAD) of CTC. Our scheme relies on the following two aspects to reduce overfitting: 1) mass data augmentation, and 2) a flat 3D residual fully convolutional network (FCN). In the first aspect, we utilize extensive rotation, translation, and scaling with continuous value to provide numerous data samples. In the second aspect, we adapt the well-known V-Net to a flat residual FCN to resolve the problem of detection other than segmentation. Our proposed framework does not rely on accurate colon segmentation nor any electrical cleansing of tagged fluid, and experimental results show that it can still achieve high sensitivity with much fewer false positives. Code has been made available at: http://github.com/chenyzstju/ctc_screening_cnn.

Osteoporosis is a bone disease with a variety of causes, leading to bone pain and fragility to fracture. Major treatment methods include nutrition therapy, exercise therapy, drug therapy and surgical treatment, among which exercise therapy, such as swimming, is the most effective. To investigate the optimal swimming therapy regime for postmenopausal women, the effects of eight weeks of different intensity swimming exercises were studied in rat models. After the swimming program, lumbar vertebrae were dissected from all the rats and scanned by synchrotron radiation computed tomography (SRCT). Histomorphometry analysis and finite-element analysis were carried out on the trabecular structure of the L4 lumbar based on the acquired SRCT slices. Histomorphometry analysis showed that swimming can alleviate the decrease in bone strength induced by estrogen deficiency, and moderate-intensity swimming was found to have the most significant effect.

Repeated CT scans are known to increase the risk of cancer; thus, it is paradoxical to use multiple follow-up CT scans to monitor the development of a lung nodule and conduct early treatment of the nodule. In the case of a solitary lung nodule, regional scanning and region of interest (ROI) reconstruction are likely to restore the internal area at the nodule. A limited-range few-view CT is proposed in this paper for lung nodule follow-ups with extremely reduced X-radiation. For a planned scanning of an ROI, where a solitary lung nodule is positioned, a limited-range few-view CT can be employed, and thus, less tissue is exposed to X-radiation per view. An ROI reconstruction method is also proposed that makes full use of the former standard lung scan. The experimental results show that the nodule size and shape are preserved. In the case of a 40-mm ROI, the number of exposed X-rays can be reduced by 99.6% for a circular scan and 99.9% for a 3-D scan.

To investigate the changes of different periods of primary osteoporosis, we made quantitative analysis of osteoporosis using synchrotron radiation computed tomography (SRCT), together with histomorphometry analysis and finite element analysis (FEA). Tibias, femurs and lumbar vertebras were dissected from sham-ovariectomy rats and ovariectomized rats suffering from osteoporosis at certain time points. The samples were scanned by SRCT and then FEA was applied based on reconstructed slices. Histomorphometry analysis showed that the structure of some trabecular in osteoporosis degraded as the bone volume decreased, for femurs, the bone volume fraction (BV/TV) decreased from 69% to 43%. That led to the increase of the thickness of trabecular separation (from 45.05μm to 97.09μm) and the reduction of the number of trabecular (from 7.99 mm(-1) to 5.97mm(-1)). Simulation of various mechanical tests indicated that, with the exacerbation of osteoporosis, the bones' ability of resistance to compression, bending and torsion gradually became weaker. The compression stiffness decreased from 1770.96 Fμm(-1) to 697.41 Fμm(-1), and it matched the histomorphometry analysis. This study suggested that the combination of both analysis could quantitatively analyze the bone strength in good accuracy.

A novel system is proposed to control the liquid nitrogen cooling and radio frequency heating of tissue to achieve effective thermal ablation in the treatment using fuzzy logic controller and fuzzy logic PID type controller separately. Results of ex-vivo pig liver experiments demonstrate that this system is useful and could p control the desired treatment procedure.

Microvessel networks were imaged by means of synchrotron radiation CT, A sliced loading and double threshold method was utilized for extracting vessels from large quantities of data (2 GB-5 GB). Preliminary analysis on the development of tumor microvessel networks was performed by statistically analyzing volume rendering and two qualitative factors, micro vessel density (MVD) and fractal dimension(FD).

Refractive-index (phase-contrast) radiology was able to detect lung tumors less than 1 mm in live mice. Significant micromorphology differences were observed in the microradiographs between normal, inflamed, and lung cancer tissues. This was made possible by the high phase contrast and by the fast image taking that reduces the motion blur. The detection of cancer and inflammation areas by phase contrast microradiology and microtomography was validated by bioluminescence and histopathological analysis. The smallest tumor detected is less than 1 mm(3) with accuracy better than 1 × 10(-3) mm(3). This level of performance is currently suitable for animal studies, while further developments are required for clinical application.

Thermal treatment has become an alternative modality for cancer treatment. Low temperature freezing and high temperature heating kill tumor cells effectively through direct and indirect injuries by biochemical and physical stresses. Hyperthermia at a mildly elevated temperature has also been reported to induce biochemical alternations to kill tumor cells and to stimulate immunological response to prevent metastasis. The comprehensive multi-scale biological responses to different thermal history experienced demand an accurate temperature control of the thermal system used for such a treatment. A thermal system was built in our lab utilizing RF heating and liquid nitrogen cooling through a needle probe. In practice, difficulties involved in temperature measurement for in vivo monitoring and control of thermal input through two-phase LN2 flow inside the probe compromise the treatment outcome. To ensure an accurate temperature control, a new model was developed to study the dynamic freezing capacity of the cryo-probe by accounting for the probe shape and dimensions. The model was validated by experiments and used to predict the freezing processes under different conditions. Numerical simulation results showed that combined with RF heating, the system could be used to perform different treatment protocols with an accurate temperature control.

A new probe for tumor treatment is designed and simulated in this study. This probe combines the cryosurgery and hyperthermia which is suitable for the treatment of subcutaneous tumors. Simulations of the cooling and heating processes demonstrate that the probes are capable of treating the tumor effectively. And the numerical results indicate that the lengths of the probe, the diameters of the inner tube and the pressures of liquid nitrogen influence the probes' cooling ability. The temperature responses at the tumor base induced by different probes are similar, though the great differences appear on the treatment interface of the probes, thus the temperature gradient within the tumor. Based on the simulation results, the heating effect of the probe is shown to be effective in damaging the tumor while protecting normal tissue in the surrounding. Animal experiments will be carried out using this type of probe to treat tumor in the near future.

Objective: To establish an animal model of osteonecrosis of the femoral head (ONFH) like human., Methods: Ten healthy adult three-leg Beagle male dogs weighing (16.0 +/- 1.6) kg were conducted as the animal model of ONFH according to the schedule of cryosurgery designed in advance in which liquid nitrogen, pressurized to 0.5 MPa, was poured into the femoral head for 16.5 minutes. After rewarmed to O degrees C for 10 minutes, the liquid nitrogen was reported into the femoral head for another 16.5 minutes. At the end of the follow-up, the results were reviewed by pathologic check. One dog was conducted as control group., Results: The first boundary temperature of (-27.9 +/- 4.3) degrees C was higher than the second boundary temperature (-31.3 +/- 4.7) degrees C by -3.4 degrees C, and there was significant difference (P < 0.01). The diameter of the femoral head of (17.7 +/- 1.1) mm was linearly (y = 2.6 - 2.409 x) correlated to boundary temperature by Pearson analysis, and the R rate was 0.977 (P < 0.05). Four dogs in experimental group progressed to collapse of the femoral head like human in the 6th month after operation. The rate of the femoral head collapse rose to 44.4%. In the control group, osteonecrosis was never found., Conclusion: Cryosurgery for osteonecrosis of the femoral head in the three-leg canine model may become a method to establish an animal model of ONFH like human.

Objective: To establish a animal model of osteonecrosis of femoral head in canine like human., Methods: The thermal field of canine's femoral head was three-dimensionally analyzed with fluent 6.2 software so that the best cryosurgery patent could be designed to maximize the osteonecrosis and minimize extra surgery trauma with the cryosurgery system invented by Shanghai Jiaotong University. Liquid nitrogen was pressurized to 0.5 MPa, poured into femoral head for 6.5 minutes, rewarming to 2 degrees C for 5 minutes and then repoured into it again for another 6.5 minutes. Ten three-foot canines were conducted as the animal models of osteonecrosis of femoral head according to the method above. At the end of follow-up, the results were reviewed by radiologic and pathologic check. Two dogs were conducted as control group., Results: In the experimental group, one of the ten canines was testified to occur osteonecrosis of femoral head after one week pathologically, cell death and vessel breakage of cavitas medullaris in the femoral head was obvious under microscope; in other nine canines being still under follow-up, five with three-month follow-up at least progressed to the collapse of femoral head like human (Ficat III). In control group, no osteonecrosis was found., Conclusion: Cryosurgery for osteonecrosis of the femoral head in three-foot canine model may become a method to establish the animal model of osteonecrosis of femoral head like human.

In the present study the feasibility of applying synchrotron radiation to the morphological study of early-stage lung cancer has been investigated. Lewis lung cancer was implanted and grown in a nude mouse for different periods, and imaged using phase-contrast synchrotron X-rays. Morphological differences were clearly shown between the normal lung and cancerous tissues at this early stage. Irregular and tortuous angiogenesis were found in the periphery region of the developing lung cancer. Results from this study indicate that synchrotron X-rays can be used for imaging cancer development and progression with minimal invasion.

In the present study, the feasibility of applying synchrotron radiation for the detection of lung cancers was investigated. Lung cancer tissues grown in mice over different periods were observed with phase-contrast synchrotron X-ray chest imaging. Irregular and tortuous vessels appearing in early cancer angiogenesis were found in the periphery region of the implanted cancer. Structure difference was clearly shown between normal tissue and early stage solid cancer in micrometers. Results from this study indicate that synchrotron X-ray may open broad perspectives for imaging cancer development and progression noninvasively.

A new thermal system for tumor treatment was designed to enhance the curative effects while minimizing invasion to the surrounding healthy tissue. This system includes LN2cryosurgery and RF ablation, which can be used separately or simultaneously. A heat transfer model describing the thermal field during treatments was established. The SAR distribution was assumed to be resulted from an infinite linear source. Numerical simulation of the temperature distributions inside the tissue was performed using Fluent 6.1. Parameters such as the RF power, nitrogen flow rate, and the starting time of each treatment were adjusted to control the treatment region. The simulation results demonstrated that the RF power is the key factor to control the heating and thawing process, while the protecting nitrogen flow can adjust the heating rate slightly.