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X-ray computed tomography (CT) imaging technology has become an indispensable diagnostic tool in clinical examination. However, it poses a risk of ionizing radiation, making the reduction of radiation dose one of the current research hotspots in CT imaging. Sparse-view imaging, as one of the main methods for reducing radiation dose, has made significant progress in recent years. In particular, sparse-view reconstruction methods based on deep learning have shown promising results. Nevertheless, efficiently recovering image details under ultra-sparse conditions remains a challenge. To address this challenge, this paper proposes a high-frequency enhanced and attention-guided learning Network (HEAL). HEAL includes three optimization strategies to achieve detail enhancement: Firstly, we introduce a dual-domain progressive enhancement module, which leverages fidelity constraints within each domain and consistency constraints across domains to effectively narrow the solution space. Secondly, we incorporate both channel and spatial attention mechanisms to improve the network's feature-scaling process. Finally, we propose a high-frequency component enhancement regularization term that integrates residual learning with direction-weighted total variation, utilizing directional cues to effectively distinguish between noise and textures. The HEAL network is trained, validated and tested under different ultra-sparse configurations of 60 views and 30 views, demonstrating its advantages in reconstruction accuracy and detail enhancement. [ABSTRACT FROM AUTHOR]

The interior problem, a persistent ill-posed challenge in CT imaging, gives rise to truncation artifacts capable of distorting CT values, thereby significantly impacting clinical diagnoses. Traditional methods have long struggled to effectively solve this issue until the advent of supervised models built on deep neural networks. However, supervised models are constrained by the need for paired data, limiting their practical application. Therefore, we propose a simple and efficient unsupervised method based on the Cycle-GAN framework. Introducing an implicit disentanglement strategy, we aim to separate truncation artifacts from content information. The separated artifact features serve as complementary constraints and the source of generating simulated paired data to enhance the training of the sub-network dedicated to removing truncation artifacts. Additionally, we incorporate polar transformation and an innovative constraint tailored specifically for truncation artifact features, further contributing to the effectiveness of our approach. Experiments conducted on multiple datasets demonstrate that our unsupervised network outperforms the traditional Cycle-GAN model significantly. When compared to state-of-the-art supervised models trained on paired datasets, our model achieves comparable visual results and closely aligns with quantitative evaluation metrics. [ABSTRACT FROM AUTHOR]

Background: Improving imaging speed has always been the focus of research in CT technology, which is related to the radiation dose and imaging quality of moving organs, including heart and blood vessels. However, it is difficult to achieve further improvement by increasing the rotation speed of the gantry due to its structural strength limitation. Differing from the conventional CTs, the static CT employs dozens of ray sources to acquire projection data from different angular ranges, and each source only needs to be rotated in a small range to finish a full 360° scan, thus greatly increasing the scanning speed. Purpose: As sources of static CT need to be evenly distributed over 360°, the sources and detectors have to be arranged on two parallel rings independently. Such a geometry can be considered as a special case of CT systems with a significantly large cone angle, that is, a part of the detector is missing in the vicinity of the mid‐plane. Due to restriction of upper and lower bounds of the cone angle of the static CT, there are uneven projection data varying in each portion of the reconstruction volume, the conventional analytical or iterative reconstruction methods may introduce artifacts in the reconstructed outcomes. Methods: Following the weighting approach extended FDK (xFDK) by Grimmer et al., we propose an improved bilateral xFDK algorithm (bixFDK), which focuses on the reconstruction of the expanded volume. With the same philosophy as xFDK in terms of weighting function design, bixFDK takes the longitudinal offset of the detector with respect to the source into consideration, making our method applicable to a wide range of CT geometries, especially for the static CT. Based on the proposed bixFDK, a new iterative scheme bixFDK‐IR is also constructed to extend the applications to a wide range of scan protocols such as sparse‐view scan. Results: The proposed method has been validated with the simulated phantom data and the actual clinical data of the static CT, and demonstrates that it can ensure good image quality and enlarge the reconstruction volume in z‐direction of the static CT. Conclusions: The bixFDK algorithm is an ideal reconstruction approach for static CT geometry, and the iterative scheme of bixFDK‐IR is applicable to a wide range of CT geometries and scan protocols, thus providing a wide range of application scenarios. [ABSTRACT FROM AUTHOR]

Objective. Metal artifact reduction (MAR) has been a key issue in CT imaging. Recently, MAR methods based on deep learning have achieved promising results. However, when deploying deep learning-based MAR in real-world clinical scenarios, two prominent challenges arise. One limitation is the lack of paired training data in real applications, which limits the practicality of supervised methods. Another limitation is that image-domain methods suitable for more application scenarios are inadequate in performance while end-to-end approaches with better performance are only applicable to fan-beam CT due to large memory consumption. Approach. We propose a novel image-domain MAR method based on the generative adversarial network with variable constraints (MARGANVAC) to improve MAR performance. The proposed variable constraint is a kind of time-varying cost function that can relax the fidelity constraint at the beginning and gradually strengthen the fidelity constraint as the training progresses. To better deploy our image-domain supervised method into practical scenarios, we develop a transfer method to mimic the real metal artifacts by first extracting the real metal traces and then adding them to artifact-free images to generate paired training data. Main results. The effectiveness of the proposed method is validated in simulated fan-beam experiments and real cone-beam experiments. All quantitative and qualitative results demonstrate that the proposed method achieves superior performance compared with the competing methods. Significance. The MARGANVAC model proposed in this paper is an image-domain model that can be conveniently applied to various scenarios such as fan beam and cone beam CT. At the same time, its performance is on par with the cutting-edge dual-domain MAR approaches. In addition, the metal artifact transfer method proposed in this paper can easily generate paired data with real artifact features, which can be better used for model training in real scenarios. [ABSTRACT FROM AUTHOR]

Background: Many dedicated cone‐beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibration model. Purpose: A high‐precision phantom and a highly robust calibration model are interdependent and mutually supportive, and they are both important for calibration accuracy, especially for the high‐resolution CBCT. Therefore, we propose a calibration scheme that considers both accurate phantom measurement and robust geometric calibration. Methods: Our proposed scheme consists of two parts: (1) introducing a measurement model to acquire the accurate intrinsic geometric relationship of the fiducials in the phantom; (2) developing a highly noise‐robust nonconvex model‐based calibration method. The measurement model in the first part is achieved by extending our previous high‐precision geometric calibration model suitable for CBCT with circular trajectories. In the second part, a novel iterative method with optimization constraints based on a back‐projection model is developed to solve the geometric parameters of each view. Results: The simulations and real experiments show that the measurement errors of the fiducial ball bearings (BBs) are within the subpixel level. With the help of the geometric relationship of the BBs obtained by our measurement method, the classic calibration method can achieve good calibration based on far fewer BBs. All metrics obtained in simulated experiments as well as in real experiments on Micro CT systems with resolutions of 9 and 4.5 μm show that the proposed calibration method has higher calibration accuracy than the competing classic method. It is particularly worth noting that although our measurement model proves to be very accurate, the classic calibration method based on this measurement model can only achieve good calibration results when the resolution of the measurement system is close to that of the system to be calibrated, but our calibration scheme enables high‐accuracy calibration even when the resolution of the system to be calibrated is twice that of the measurement system. Conclusions: The proposed combined geometrical calibration scheme does not rely on a phantom with an intricate pattern of fiducials, so it is applicable in Micro CT with high resolution. The two parts of the scheme, the "measurement model" and the "calibration model," prove to be of high accuracy. The combination of these two models can effectively improve the calibration accuracy, especially in some extreme cases. [ABSTRACT FROM AUTHOR]

Poor chip solder joints can severely affect the quality of the finished printed circuit boards (PCBs). Due to the diversity of solder joint defects and the scarcity of anomaly data, it is a challenging task to automatically and accurately detect all types of solder joint defects in the production process in real time. To address this issue, we propose a flexible framework based on contrastive self-supervised learning (CSSL). In this framework, we first design several special data augmentation approaches to generate abundant synthetic, not good (sNG) data from the normal solder joint data. Then, we develop a data filter network to distill the highest quality data from sNG data. Based on the proposed CSSL framework, a high-accuracy classifier can be obtained even when the available training data are very limited. Ablation experiments verify that the proposed method can effectively improve the ability of the classifier to learn normal solder joint (OK) features. Through comparative experiments, the classifier trained with the help of the proposed method can achieve an accuracy of 99.14% on the test set, which is better than other competitive methods. In addition, its reasoning time is less than 6 ms per chip image, which is in favor of the real-time defect detection of chip solder joints. [ABSTRACT FROM AUTHOR]

In this study, the authors report the design and fabrication of a small mixed-integrated balun for magnetic resonance imaging (MRI). The device was designed by using the positive anti-symmetric coupling method, which applies the lump surface-mount technology capacitors as well as mirror-symmetric coupling strips that were etched on the top and bottom layers of a printed circuit board. The capacitors reduced the length of the coupling strips and compensated for imbalances in the phase and gain due to errors in the fabrication process. The structure and equivalent even–odd circuit model of the device was modeled and examined using commercial software to optimize the design parameters. Following this, the device was fabricated and its performance was assessed through measurements using a network analyzer. The results showed that imbalances in the gain and phase were lower than 0.1 dB and 1°, respectively, and the insertion loss and the input voltage standing-wave ratio (VSWR) were lower than 0.4 dB and −25 dB, respectively. More importantly, the device was small, with dimensions of 50 × 60 × 1.5 mm. This makes it suitable for MRI applications involving highly integrated miniaturized systems. The proposed device was integrated into a 3.0 T radio-frequency power amplifier (RFPA) and reduced the dimensions of its power modules by 20% compared with the traditional balun. Finally, the RFPA module was used in an 3.0T MRI system for imaging experiments, and the results showed that the balun can help obtain high-quality scanning images. [ABSTRACT FROM AUTHOR]

Limited-angle CT is a challenging problem in real applications. Incomplete projection data will lead to severe artifacts and distortions in reconstruction images. To tackle this problem, we propose a novel reconstruction framework termed Deep Iterative Optimization-based Residual-learning (DIOR) for limited-angle CT. Instead of directly deploying the regularization term on image space, the DIOR combines iterative optimization and deep learning based on the residual domain, significantly improving the convergence property and generalization ability. Specifically, the asymmetric convolutional modules are adopted to strengthen the feature extraction capacity in smooth regions for deep priors. Besides, in our DIOR method, the information contained in low-frequency and high-frequency components is also evaluated by perceptual loss to improve the performance in tissue preservation. Both simulated and clinical datasets are performed to validate the performance of DIOR. Compared with existing competitive algorithms, quantitative and qualitative results show that the proposed method brings a promising improvement in artifact removal, detail restoration and edge preservation. [ABSTRACT FROM AUTHOR]

Micro-CT has important applications in biomedical research due to its ability to perform high-precision 3D imaging of micro-architecture in a non-invasive way. Because of the limited power of the radiation source, it is difficult to obtain a high signal-to-noise image under the requirement of temporal resolution. Therefore, low-dose CT image denoising has attracted considerable attention to improve the image quality of micro-CT while maintaining time resolution. In this paper, an end-to-end asymmetric perceptual convolutional network (APCNet) is proposed to enhance the network's ability to capture and retain image details by improving the convolutional layer and introducing an edge detection layer. Compared with the previously proposed denoising models such as DnCNN, CNN-VGG, and RED-CNN, experiments proved that our proposed method has achieved better results in both numerical indicators and visual perception. [ABSTRACT FROM AUTHOR]

Purpose: The misalignment correction in cone beam computed tomography (CBCT), which is usually carried out in an offline manner, is a difficult and tedious process. It becomes even more challenging in microscopic CBCT due to the much higher requirements on spatial resolution. In practice, however, an offline approach for misalignment correction may not be readily implementable, especially in the situations where either time is of the essence or the process needs to be carried out repetitively. Thus, an online self‐calibration (i.e., data sustained misalignment correction without the involvement of specific alignment phantom) would be more practical. In this work, we investigate the data sustained misalignment correction in microscopic CBCT via optimization under the Grangeat Epipolar Consistence Condition and evaluate its performance via phantom and specimen studies. Methods: With the cost function defined according to the Grangeat Epipolar Consistency Condition (G‐ECC) and by minimizing the cost function using the simplex‐simulated annealing algorithm (SIMPSA), we evaluate and verify the G‐ECC optimization‐based online self‐calibration method's performance. Performance is measured in sensitivity, robustness, and accuracy using the projection data of phantoms generated by computer simulation and botanical specimens acquired by a prototype microscopic CBCT. Results: The online data sustained misalignment correction in microscopic CBCT via G‐ECC optimization works very well in sensitivity and robustness, in addition to its accuracy of 0.27%, 0.48%, and 0.34% relative errors, respectively, in obtaining the three geometric parameters that are the most critical to image reconstruction in CBCT. Quantitatively, the performance in meeting the requirements on spatial resolution is comparable to, if not better than, that of the offline misalignment correction method, in which a specific alignment phantom has to be used. Conclusions: The G‐ECC optimization‐based online self‐calibration approach provides a practical solution (as long as no latitudinal (lateral) data truncation occurs) for misalignment correction in microscopic CBCT, an application that demands high accuracy in geometric alignment for biological (cellular) imaging at super high spatial resolutions in the order of micrometers (2.1 µm). [ABSTRACT FROM AUTHOR]

In multi-lens microscopic cone-beam CT system, different resolutions can be achieved by tuning different objective lens with different magnifications, following the scan field of view (FOV) changed. By this multi-FOV/resolution acquisition strategy, many applications like truncation problem can be solved successfully. However, in order to achieve high-resolution image, the microscopic cone-beam CT system should comply with its geometrical restrictions accordingly, which are not only for that of conventional cone-beam system but also for that of multiple objective lens. Hence, compared with the existed cone-beam system calibration, a specific united calibration method is proposed in this paper. A set of geometrical parameters is presented, which can fully describe whole geometry of conventional cone-beam system as well as multi-lens. By taking use of projective data of circular trajectory of one single ball phantom, a cost function with the described arguments based on the projective lines of the phantom is built up. After solving the cost function, the calibration parameters can be determined optimally. The simulated and experimental results demonstrate that the proposed method shows significant improvements in robustness, precision and implementation. [ABSTRACT FROM AUTHOR]

Purpose: Cone‐beam (CB) CT is a powerful noninvasive imaging modality, and is widely used in many applications. Accurate geometric parameters are essential for high‐quality image reconstruction. Usually, a CBCT system with higher spatial resolution, particularly on the order of microns or nanometers, will be more sensitive to the parametric accuracy. Here, we propose a novel calibration method combining a simple phantom containing ball bearing markers and an advanced optimization procedure. This method can be applied to CBCT with reproducible geometry and frame‐to‐frame invariant geometric parameters. Methods: Our proposed simplex‐simulated annealing procedure minimizes the cost function that associates the geometrical parameters with the degree to which the back projections of the ball bearings in projections from various viewing angles converge, and the global minimum of the cost function corresponds to the actual geometric parameters. Specifically, six geometric parameters can be directly obtained by minimizing the cost function, and the last parameter, the distance from source to rotation axis (SRD), can be obtained using prior knowledge of the phantom — the spacing between the two ball bearings. Results: Numerical simulation was performed to validate that the proposed method with various noise levels. With the proposed method, the mean errors and standard deviations can be reduced to ∼10% and less than 1/3 of a competing benchmark method in the case of strong Gaussian noise (sigma = 200% of the pixel size) and large tilt angle (tilt angle = −4∘). The calibration experiments with micro‐CT and high‐resolution CT scanners demonstrate that the proposed method recovers imaging parameters accurately, leading to superior image quality. Conclusion: The proposed method can obtain accurate geometric parameters of a CBCT system with a circular trajectory. While in the case of micro‐CT the proposed method has a performance comparable to the competing method, for high‐resolution CT, which is more sensitive to geometric calibration, the proposed method demonstrates higher calibration accuracy and more robustness than the benchmark algorithm. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks possess tremendous potential in biomedical areas for their particular structure. In this study, the authors explored Fe2+-adsorbed nanoscaled zeolitic imidazolate framework-8 (ZIF-8) for in vivo multimodal imaging of cancerous cells for early diagnosis of target cancers. The observations demonstrate that adding Fe2+ into the suspension of ZIF-8 can neutralize the alkalinity and lower toxicity, while the Fe2+-adsorbed ZIF-8 can readily transform to fluorescence ZnO and super paramagnetic Fe3O4 under the synergistic reaction of ROS, GSH, and acids. It is evident that the formation of the nanoclusters ZnO and Fe3O4 only occurred in cancerous cells and does not take place in normal cells, which can be attributed to the different ROS levels and specific micro-environment in tumor and normal cells. This raises the possibility for the Fe2+-adsorbed zeolitic imidazolate frameworks to act as promising agents for the in vivo multimodal imaging of cancers in their early stage. [ABSTRACT FROM AUTHOR]

Objectives: Present a clinical application of virtual laryngoscopy (VL) in the assessment of laryngeal tumor and its extent. Study Design: CT data from two subjects are acquired for this preliminary study. One subject is a healthy volunteer and the other is a patient with laryngeal tumor. The laryngeal framework and upper airway are reconstructed using CT data, which allows for computer-aided internal and external anatomical views and interactive fly-through. Methods: These CT data are reconstructed into 0.5 mm slice images, resulting in a total of 200-300 image slices. An advanced commercial visualization software (AMIRA) is used for 3D image segmentation, reconstruction and surface rendering of laryngeal anatomical structures. Results: The 3D laryngeal framework and upper airway are reconstructed for both the tumor patient and the healthy subject. The conventional views of the reconstructed vocal folds are compared with those obtained from fiber-optic laryngoscope. Additionally, unique views of the vocal folds obtained from retrograde visualization and fly-through are presented, which are not possible to obtain using conventional endoscope imaging. The segmented anatomical model and the tumor from the patient's CT images were displayed individually to show the distribution of the tumor and its extent as well as spatial and contextual relationships to the larynx and airway anatomical structures. Conclusions: This study demonstrated the potential application of VL as a noninvasive clinical diagnostic tool for the assessment of laryngeal tumor and its extent. Our preliminary results demonstrated that the VL may provide valuable insights for the diagnosis and treatment planning for laryngeal and airway tumors. The noninvasive VL may complement the invasive laryngoscopic examinations for the staging of tumors and follow-ups on surgical interventions. [ABSTRACT FROM AUTHOR]

Background: As a dual-modality contrast agent, magnetic microbubbles (MMBs) can not only improve contrast of ultrasound (US) image, but can also serve as a contrast agent of magnetic resonance image (MRI). With the help of MMBs, a new registration method between US image and MRI is presented.Methods: In this method, MMBs were used in both ultrasound and magnetic resonance imaging process to enhance the most important information of interest. In order to reduce the influence of the speckle noise to registration, semi-automatic segmentations of US image and MRI were carried out by using active contour model. After that, a robust optical flow model between US image segmentation (floating image) and MRI segmentation (reference image) was built, and the vector flow field was estimated by using the Coarse-to-fine Gaussian pyramid and graduated non-convexity (GNC) schemes.Results: Qualitative and quantitative analyses of multiple group comparison experiments showed that registration results using all methods tested in this paper without MMBs were unsatisfactory. On the contrary, the proposed method combined with MMBs led to the best registration results.Conclusion: The proposed algorithm combined with MMBs contends with larger deformation and performs well not only for local deformation but also for global deformation. The comparison experiments also demonstrated that ultrasound-MRI registration using the above-mentioned method might be a promising method for obtaining more accurate image information. [ABSTRACT FROM AUTHOR]

Background Although multiple myeloma (MM) treatment has improved in the last decade, it remains largely incurable. One of main reasons is that there are cancer stem cells (CSCs) in MM, which are responsible for MM’s drug resistance and relapse. In this study, we used the targeting microbubbles (MBs) conjugated with anti-ABCG2 monoclonal antibody (mAb) for ultrasound mediated epirubicin (EPI) delivery to evaluate the therapeutic effectiveness of the novel agent in MM CSC xenograft model. Methods MM CSCs, marked by CD138 − CD34 − cell phenotypes were isolated from human MM RPMI8226 cell line using immune magnetic activated cell sorting system, and inoculated into nonobese diabetic/severe combined immunodeficient mice by subcutaneous or intravenous injection. After the mice developed MM, they were intravenous injection treated with EPI, EPI-MBs + mAb, and EPI-MBs + mAb with ultrasound exposure, respectively. Results All treated mice showed inhibited tumor sizes or bone lesions, decreased renal damages and anemia, and increased MM bearing mice’ survival. In particular, the EPI-MBs + mAb plus ultrasound exhibited significantly enhanced therapeutic MM effectiveness by inducing apoptosis compared with other biologic agents. Conclusion The data provide evidence that EPI-MBs + mAb with ultrasound exposure might be available for treatment MM patients in clinic. [ABSTRACT FROM AUTHOR]

Carotid atherosclerotic plaques are a major complication associated with type II diabetes, and carotid ultrasound is commonly used for diagnosing carotid vascular disease. In primary hospitals, less experienced ultrasound physicians often struggle to consistently capture standard carotid images and identify plaques. To address this issue, we propose a novel approach, the long-short memory-based detection network (LSMD), for carotid artery detection in ultrasound video streams, facilitating the identification and localization of critical anatomical structures and plaques. This approach models short- and long-distance spatiotemporal features through Short-term Temporal Aggregation (STA) and Long-term Temporal Aggregation (LTA) modules, effectively expanding the temporal receptive field with minimal delay and enhancing the detection efficiency of carotid anatomy and plaques. Specifically, we introduce memory buffers with a dynamic updating strategy to ensure extensive temporal receptive field coverage while minimizing memory and computation costs. The proposed model was trained on 80 carotid ultrasound videos and evaluated on 50, with all videos annotated by physicians for carotid anatomies and plaques. The trained LSMD was evaluated for performance on the validation and test sets using the single-frame image-based Single Shot Multi-box Detector (SSD) algorithm as a baseline. The results show that the precision, recall, Average Precision at IoU = 0.50 (AP 50 ), and mean Average Precision (mAP) are 6.83%, 12.29%, 11.23%, and 13.21% higher than the baseline (p < 0.001) respectively, while the model's inference latency reaches 6.97ms on a desktop-level GPU (NVIDIA RTX 3090Ti) and 29.69ms on an edge computing device (Jetson Orin Nano). These findings demonstrate that LSMD can accurately localize carotid anatomy and plaques with real-time inference, indicating its potential for enhancing diagnostic accuracy in clinical practice.

Background: Radix Bupleuri , a kind of Chinese herbal medicine with great clinical use, is often confused with its adulterants, and it is difficult to identify it without certain knowledge. The existing identification methods have their own drawbacks, so a new method is needed to realize the identification of Radix Bupleuri and its adulterants. Methods: We used Micro Computed Tomography (Micro-CT) to perform tomography scans on Radix Bupleuri and its adulterants, performed data screening and data correction on the obtained DICOM images, and then applied 3D reconstruction, data augmentation, and ResNext deep learning model for the classification study. Results: The DICOM images after data screening, data correction, and 3D reconstruction can observe the differences in the microstructure of Radix Bupleuri and its adulterants, thus enabling effective classification and analysis. Meanwhile, the accuracy of classification using the ResNext model reached 75%. Conclusion: The results of this study showed that Micro-CT technology is feasible for the authentication of Radix Bupleuri . The pre-processed and 3D reconstructed tomographic images clearly show the microstructure and the difference between Radix Bupleuri and its adulterants without damaging the internal structure of the samples. This study concludes that Micro-CT technology provides important technical support for the reliable identification of Radix Bupleuri and its adulterants, which is expected to play an important role in the quality control and clinical application of herbs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Chen, Chen, Zhuang, Luo, Liu and Liu.)

Tomographic image reconstruction requires precise geometric measurements and calibration for the scanning system to yield optimal images. The isocenter offset is a very important geometric parameter that directly governs the spatial resolution of reconstructed images. Due to system imperfections such as mechanical misalignment, an accurate isocenter offset is difficult to achieve. Common calibration procedures used during isocenter offset tuning, such as pin scan, are not able to reach precision of subpixel level and are also inevitably hampered by system imperfections. We propose a purely data-driven method based on Fourier shift theorem to indirectly, yet precisely, estimate the isocenter offset at the subpixel level. The solution is obtained by applying a generalized M-estimator, a robust regression algorithm, to an arbitrary sinogram of axial scanning geometry. Numerical experiments are conducted on both simulated phantom data and actual data using a tungsten wire. Simulation results reveal that the proposed method achieves great accuracy on estimating and tuning the isocenter offset, which, in turn, significantly improves the quality of final images, particularly in spatial resolution., (© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Deep brain stimulation (DBS) surgery is an important treatment for patients with Parkinson's disease in the middle and late stages. The accuracy of the implantation of electrode at the location of the nuclei directly determines the therapeutic effect of the operation. At present, there is no single imaging method that can obtain images with electrodes, nuclei and their positional relationship. In addition, the subthalamic nucleus is small in size and the boundary is not obvious, so it cannot be directly segmented. In this paper, a complete end-to-end DBS effect evaluation pipeline was constructed using magnetic resonance (MR) data of T1, T2 and SWI weighted by DBS surgery. Firstly, the images of preoperative and postoperative patients are registered and normalized to the same coordinate space. Secondly, the patient map is obtained by non-rigid registration of brain map and preoperative data, as well as the preoperative nuclear cluster prediction position. Then, a three-dimensional (3D) image of the positional relationship between the electrode and the nucleus is obtained by using the electrode path in the postoperative image and the result of the nuclear segmentation. The 3D image is helpful for the evaluation of the postoperative effect of DBS and provides effective information for postoperative program control. After analysis, the algorithm can achieve a good registration between the patient's DBS surgical image and the brain map. The error between the algorithm and the expert evaluation of the physical coordinates of the center of the thalamus is (1.590 ± 1.063) mm. The problem of postoperative evaluation of the placement of DBS surgical electrodes is solved.

In high resolution (microscopic) CT applications, the scan field of view should cover the entire specimen or sample to allow complete data acquisition and image reconstruction. However, truncation may occur in projection data and results in artifacts in reconstructed images. In this study, we propose a low resolution image constrained reconstruction algorithm (LRICR) for interior tomography in microscopic CT at high resolution. In general, the multi-resolution acquisition based methods can be employed to solve the data truncation problem if the project data acquired at low resolution are utilized to fill up the truncated projection data acquired at high resolution. However, most existing methods place quite strict restrictions on the data acquisition geometry, which greatly limits their utility in practice. In the proposed LRICR algorithm, full and partial data acquisition (scan) at low and high resolutions, respectively, are carried out. Using the image reconstructed from sparse projection data acquired at low resolution as the prior, a microscopic image at high resolution is reconstructed from the truncated projection data acquired at high resolution. Two synthesized digital phantoms, a raw bamboo culm and a specimen of mouse femur, were utilized to evaluate and verify performance of the proposed LRICR algorithm. Compared with the conventional TV minimization based algorithm and the multi-resolution scout-reconstruction algorithm, the proposed LRICR algorithm shows significant improvement in reduction of the artifacts caused by data truncation, providing a practical solution for high quality and reliable interior tomography in microscopic CT applications. The proposed LRICR algorithm outperforms the multi-resolution scout-reconstruction method and the TV minimization based reconstruction for interior tomography in microscopic CT.

The beam hardening effect can induce strong artifacts in CT images, which result in severely deteriorated image quality with incorrect intensities (CT numbers). This paper develops an effective and efficient beam hardening correction algorithm incorporated in a filtered back-projection based maximum a posteriori (BHC-FMAP). In the proposed algorithm, the beam hardening effect is modeled and incorporated into the forward-projection of the MAP to suppress beam hardening induced artifacts, and the image update process is performed by Feldkamp-Davis-Kress method based back-projection to speed up the convergence. The proposed BHC-FMAP approach does not require information about the beam spectrum or the material properties, or any additional segmentation operation. The proposed method was qualitatively and quantitatively evaluated using both phantom and animal projection data. The experimental results demonstrate that the BHC-FMAP method can efficiently provide a good correction of beam hardening induced artefacts.

Nanosized drug delivery systems have offered promising approaches for cancer theranostics. However, few are effective to simultaneously maximize tumor-specific uptake, imaging, and therapy in a single nanoplatform. Here, we report a simple yet stimuli-responsive anethole dithiolethione (ADT)-loaded magnetic nanoliposome (AML) delivery system, which consists of ADT, hydrogen sulfide (H 2 S) pro-drug, doped in the lipid bilayer, and superparamagnetic nanoparticles encapsulated inside. HepG2 cells could be effectively bombed after 6 h co-incubation with AMLs. For in vivo applications, after preferentially targeting the tumor tissue when spatiotemporally navigated by an external magnetic field, the nanoscaled AMLs can intratumorally convert to microsized H 2 S bubbles. This dynamic process can be monitored by magnetic resonance and ultrasound dual modal imaging. Importantly, the intratumoral generated H 2 S bubbles imaged by real-time ultrasound imaging first can bomb to ablate the tumor tissue when exposed to higher acoustic intensity; then as gasotransmitters, intratumoral generated high-concentration H 2 S molecules can diffuse into the inner tumor regions to further have a synergetic antitumor effect. After 7-day follow-up observation, AMLs with magnetic field treatments have indicated extremely significantly higher inhibitions of tumor growth. Therefore, such elaborately designed intratumoral conversion of nanostructures to microstructures has exhibited an improved anticancer efficacy, which may be promising for multimodal image-guided accurate cancer therapy.

Hematological malignancy originated from B-cell line, multiple myeloma (MM), is a kind of plasma cells in bone marrow hyperplasia and cause of osteoclast-mediated skeletal destruction disease. MiR-34a plays an important epigenetic regulating role in malignant tumors and presents a therapeutic potential. In this study, we investigated the effects of overexpression of miR-34a in MM cancer stem cells (CSCs) on tumor growth and bone lesions. Here we showed that miR-34a overexpression inhibited cell proliferation, colony formation, and increased CSC apoptosis in vitro . The apparent epigenetic modulation induced by miR-34a overexpression was found no only in MM RPMI8226 cells but also in CSC xenograft MM. Both bioinformatics prediction and dual-luciferase reporter assay showed that transforming growth interaction factor 2 (TGIF2) was sufficient to confer miR-34a regulation. The results of qRT-PCR and Western blot assays demonstrated that the expression of TGIF2 was significant decreased in tumor tissues from NOD/SCID mice injected with miR-34a-MM CSCs. We conclude that miR-34a overexpression in MM CSCs significantly suppressed the tumorigenicity and lytic bone lesions in mouse model by inducing apoptosis and inhibiting TGIF2 expression.

Considering the survival rate of small animals and the continuity of the experiments,high-dose X-ray shooting process is not suitable for the small animals in computed tomography(CT)experiments.But the low-dose process results with images might be polluted by noises which are not conducive for the experiments.In order to solve this problem,we in this paper introduce a global dictionary learning based denoising method to apply the promotion of the low dose CT image.We at first adopted the K-means singular value decomposition(K-SVD)algorithm to train a global dictionary based on the high dose CT image.Then,the noise image could be decomposed into sparse component which was free from noise through the orthogonal matching pursuit(OMP)algorithm.Finally,the noisefree image could be achieved by reconstructing the image only with its sparse components.The experiments results showed that the method we proposed here could decrease the noise efficiently and remain the details,and it would help promote the low dose image quality and increase the survival rate of the small animals.

Multiple myeloma (MM) remains an incurable disease in most patients. Homoharringtonine (HHT) is a natural alkaloid produced by various Cephalotaxus species, and is approved by the United States of America Food and Drug Administration to treat patients with acute and chronic myeloid lymphoma. The aim of this study was to develop the high proportion polyethyleneglycol (PEG) of long-circulating HHT liposomes (LCL-HHT-H-PEG) and investigate its therapeutic applicability in vitro and in vivo against RPMI8226 MM. The optimized formulation of LCL-HHT-H-PEG showed a higher association with cytotoxicity against MM RPMI8226 cells than those of low proportion PEG of long-circulating HHT liposomes, liposome-encapsulated-HHT, micelle-HHT, and HHT in vitro. Therapeutic experiments in severe combined immunodeficient mice implanted with MM RPMI8226 cells by the subcutaeous route showed the significant inhibition of tumor growth in LCL-HHT-H-PEG group compared with the HHT group, and other control groups. The analysis of flow cytometry and transmission electron microscopy indicated that LCL-HHT-H-PEG exerted the cytotoxicity against MM by inducing the MM apoptosis in vitro and in vivo. This study suggests that our developed LCL-HHT-H-PEG may be regarded as a promising nano-device to deliver anti-MM drug HHT for treatment of MM patients.

Computer-aided detection (CAD) systems provide useful tools and an advantageous process to physicians aiming to detect lung nodules. This paper develops a method composed of four processes for lung nodule detection. The first step employs image acquisition and pre-processing techniques to isolate the lungs from the rest of the body. The second stage involves the segmentation process using a 2D algorithm to affect every layer of a scan eliminating non-informative structures inside the lungs, and a 3D blob algorithm associated with a connectivity algorithm to select possible nodule shape candidates. The combinations of these algorithms efficiently eliminate the high rates of false positives. The third process extracts eight minimal representative characteristics of the possible candidates. The final step utilizes a support vector machine for classifying the possible candidates into nodules and non-nodules depending on their features. As the objective is to find nodules bigger than 4mm, the proposed approach demonstrated quite encouraging results. Among 65 computer tomography (CT) scans, 94.23% of sensitivity and 84.75% in specificity were obtained. The accuracy of these two results was 89.19% taking into consideration that 45 scans were used for testing and 20 for training. The rate of false positives was 0.2 per scan.

Sizes of nanoscale contrast agents play an important role in targeting specific organs and distribution in organisms. lodinated oil nanoemulsions with uniform size distribution and containing indocyanine green (ICG) fluorescent dye (25 nm, 60 nm, 100 nm) were synthesized by stirring, combined with ultrasonic emulsification technique. Rats were intravenously injected with the iodinated oil nanoemulsions with different sizes, used as contrast agents, and investigated with enhanced computed tomography (CT) and fluorescence imaging. Through experiments, the distribution and metabolism of the contrast agents in rat's bodies were studied, and their influence on enhanced CT imaging of different organs was compared. The results demonstrated that target accumulating organs for the iodinated oil nanoemulsions were liver and spleen, with obvious dosage-dependence. Large sized nanoemulsion preferred to accumulate into spleen, and liver, and the phagocytosis was getting weaker with the decrease of the nanoemulsion size. The CT imaging of the inferior vena cava was rapidly enhanced and reached the highest point after administration of the nanoemulsion. The nanoemulsion gradually gathered and metabolized in the spleen and liver, resulting in rapidly decreased CT imaging, with weak rebound, of the inferior vena cava.

Multiple myeloma (MM) remains to be an incurable disease. The purpose of this study was to evaluate the effect of ABCG2 monoclonal antibody (McAb) combined with paclitaxel (PTX) conjugated with Fe3O4 nanoparticles (NPs) on MM progressed from cancer stem cells (CSCs) in non-obese-diabetic/severe-combined-immunodeficiency (NOD/SCID) mouse model. Mice were injected with MM CSCs as marked by CD138-CD34- phenotypes through tail veins. The developed MM mice were examined by micro-computer tomography scanning, ultrasonography and enzyme-linked immunosorbent analysis. These mice were then intravenously treated with different combinations of NPs, PTX, McAb, PTX-NPs and melphalan/prednisone once a week for four weeks. The injected mice developed characteristic MM-associated syndromes, including lytic bone lesions, renal damages and proteinuria. All the treated mice showed decrease in bone lesions, renal damages and anemia but increase in apoptosis compared with the mice treated with NPs only. In particular, the treatment with ABCG2 McAb plus PTX-NPs induced the strongest therapeutic response and had an efficacy even better than that of melphalan/prednisone, a conventional regimen for MM patients. These data suggest that PTX-NPs with ABCG2 McAb can be developed into potential treatment regimens for patients with relapsed/refractory MM.

Background: The high-resolution X-ray imaging system employing synchrotron radiation source, thin scintillator, optical lens and advanced CCD camera can achieve a resolution in the range of tens of nanometers to sub-micrometer. Based on this advantage, it can effectively image tissues, cells and many other small samples, especially the calcification in the vascular or in the glomerulus. In general, the thickness of the scintillator should be several micrometers or even within nanometers because it has a big relationship with the resolution. However, it is difficult to make the scintillator so thin, and additionally thin scintillator may greatly reduce the efficiency of collecting photons., Methods: In this paper, we propose an approach to extend the depth of focus (DOF) to solve these problems. We develop equation sets by deducing the relationship between the high-resolution image generated by the scintillator and the degraded blur image due to defect of focus first, and then we adopt projection onto convex sets (POCS) and total variation algorithm to get the solution of the equation sets and to recover the blur image., Results: By using a 20 μm thick unmatching scintillator to replace the 1 μm thick matching one, we simulated a high-resolution X-ray imaging system and got a degraded blur image. Based on the algorithm proposed, we recovered the blur image and the result in the experiment showed that the proposed algorithm has good performance on the recovery of image blur caused by unmatching thickness of scintillator., Conclusions: The method proposed is testified to be able to efficiently recover the degraded image due to defect of focus. But, the quality of the recovery image especially of the low contrast image depends on the noise level of the degraded blur image, so there is room for improving and the corresponding denoising algorithm is worthy for further study and discussion.

Cone beam computer tomography (CBCT) has advantages of high precision, low radiation and high image quality. It has been developing quickly since it was applied clinically. In order to control X-ray TUBE HEAD effectively in Dental CBCT, X-ray TUBE HEAD Control System was designed and realized in this study. This control system is the core of CBTC system, which includes the communication between CBCT system and computer, the control of X-ray tube head by CBCT system main control board and the synchronization between main control board and the flat panel detector. Control circuit of the control system and corresponding operating software were designed with PIC16F877A as the core. This control system has been put into use in current CBCT system successfully.

Background: Conventional fiberoptic laryngoscope may cause discomfort to the patient and in some cases it can lead to side effects that include perforation, infection and hemorrhage. Virtual laryngoscopy (VL) can overcome this problem and further it may lower the risk of operation failures. Very few virtual endoscope (VE) based investigations of the larynx have been described in the literature., Material/methods: CT data sets from a healthy subject were used for the VL studies. An algorithm of preprocessing and region-growing for 3-D image segmentation is developed. An octree based approach is applied in our VL system which facilitates a rapid construction of iso-surfaces. Some locating techniques are used for fast rendering and navigation (fly-through)., Results: Our VL visualization system provides for real time and efficient 'fly-through' navigation. The virtual camera can be arranged so that it moves along the airway in either direction. Snap shots were taken during fly-throughs. The system can automatically adjust the direction of the virtual camera and prevent collisions of the camera and the wall of the airway., Conclusions: A virtual laryngoscope (VL) system using OpenGL (Open Graphics Library) platform for interactive rendering and 3D visualization of the laryngeal framework and upper airway is established. OpenGL is supported on major operating systems and works with every major windowing system. The VL system runs on regular PC workstations and was successfully tested and evaluated using CT data from a normal subject.

In this paper, we present a real-time approach for effective and automatic manipulation and navigation in a virtual laryngeal environment. Efficient algorithms are developed for 1), automatic segmentation, and 2) volume data organization and 3), surface rendering. Finally a virtual laryngoscope is built for the visualization of laryngeal systems and this visualization system is tested using CT volume data acquired from two normal subjects.