PIDNET: Polar Transformation Based Implicit Disentanglement Network for Truncation Artifacts.

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.

A Novel Contrastive Self-Supervised Learning Framework for Solving Data Imbalance in Solder Joint Defect Detection.

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.

[Design and implementation of postoperative evaluation pipeline of deep brain stimulation by multimodality imaging].

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.

Biosynthesized Gold Nanoclusters and Iron Complexes as Scaffolds for Multimodal Cancer Bioimaging.

Cancer treatment has a far greater chance of success if the neoplasm is diagnosed before the onset of metastasis to vital organs. Hence, cancer early diagnosis is extremely important and remains a major challenge in modern therapeutics. In this contribution, facile and new method for rapid multimodal tumor bioimaging is reported by using biosynthesized iron complexes and gold nanoclusters via simple introduction of AuCl4- and Fe2+ ions. The observations demonstrate that the biosynthesized Au nanoclusters may act as fluorescent and computed tomography probes for cancer bioimaging while the iron complexes behave as effective contrast agent for magnetic resonance imaging. The biosynthesized iron complexes and gold nanoclusters are found biocompatible in vitro (MTT (3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide) assay) and in vivo for all the vital organs of circulatory and excretory system. These observations raise the possibility that the biosynthesized probes may find applications in future clinical diagnosis for deep seated early neoplasms by multimodal imaging.

Size-Dependent Biodistribution of lodinated Oil Nanoemulsions Observed by Dual-Modal Imaging in Rats.

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.

[A Denoising Method for Low-dose Small-animal Computed Tomography Image Based on Globe Dictionary Learning].

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.

A method of extending the depth of focus of the high-resolution X-ray imaging system employing optical lens and scintillator: a phantom study.

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.

Magnetic microbubble-mediated ultrasound-MRI registration based on robust optical flow model.

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.

Bilateral extended FDK: An improved weighting method for static CT imaging.

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.

Feasibility study on the introduction of Micro-CT technology for the identification of Radix Bupleuri and its adulterants.

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.

[Design and realization of X-ray TUBE HEAD control system in the CBCT system].

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.

A nonconvex model-based combined geometric calibration scheme for micro cone-beam CT with irregular trajectories.

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.

TIME-Net: Transformer-Integrated Multi-Encoder Network for limited-angle artifact removal in dual-energy CBCT.

Dual-energy cone-beam computed tomography (DE-CBCT) is a promising imaging technique with foreseeable clinical applications. DE-CBCT images acquired with two different spectra can provide material-specific information. Meanwhile, the anatomical consistency and energy-domain correlation result in significant information redundancy, which could be exploited to improve image quality. In this context, this paper develops the Transformer-Integrated Multi-Encoder Network (TIME-Net) for DE-CBCT to remove the limited-angle artifacts. TIME-Net comprises three encoders (image encoder, prior encoder, and transformer encoder), two decoders (low- and high-energy decoders), and one feature fusion module. Three encoders extract various features for image restoration. The feature fusion module compresses these features into more compact shared features and feeds them to the decoders. Two decoders perform differential learning for DE-CBCT images. By design, TIME-Net could obtain high-quality DE-CBCT images using two complementary quarter-scans, holding great potential to reduce radiation dose and shorten the acquisition time. Qualitative and quantitative analyses based on simulated data and real rat data have demonstrated the promising performance of TIME-Net in artifact removal, subtle structure restoration, and reconstruction accuracy preservation. Two clinical applications, virtual non-contrast (VNC) imaging and iodine quantification, have proved the potential utility of the DE-CBCT images provided by TIME-Net.

MARGANVAC: metal artifact reduction method based on generative adversarial network with variable constraints.

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.

Analysis and fabrication of a small-scale radio-frequency balun for magnetic resonance imaging amplifier.

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.

DIOR: Deep Iterative Optimization-Based Residual-Learning for Limited-Angle CT Reconstruction.

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.

Micro-CT image denoising with an asymmetric perceptual convolutional network.

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.

Data sustained misalignment correction in microscopic cone beam CT via optimization under the Grangeat Epipolar consistency condition.

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).

Implementation of a virtual laryngoscope system using efficient reconstruction algorithms.

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.