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.

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.

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.

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.

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.

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.

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).

Robust regression-based estimation of isocenter offset with subpixel precision in tomographic image reconstruction.

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.

[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.

Single-Irradiation Simultaneous Dual-Modal Bioimaging Using Nanostructure Scintillators as Single Contrast Agent.

The rising demand for clinical diagnosis tools has led to extensive research on multimodal bioimaging systems. Unlike single-modal detection, multimodal imaging not only can provide both function and structure information but also can address the issue of sensitivity, depth, and cost. Despite enormous efforts, conventional step-by-step procedures for obtaining multimodal imaging pose a significant constraint on their practical applications. In this work, X-rays as highly penetrating radiation is proposed as a single-irradiation resource, while lanthanide-based nanostructure scintillators are employed as the single contrast agent to attenuate and convert X-rays, achieving computer tomography (CT) and optical dual-modal imaging at the same time. In other words, CT and optical dual-modal imaging are simultaneously produced via single radiation combined with single contrast agent. The function and structure information of targeted tumors in a mouse model can be clearly provided with large penetration and high sensitivity, indicating that this strategy is a simple but promising route for multimodal imaging of molecular disease and preclinical applications.

A novel calibration method incorporating nonlinear optimization and ball-bearing markers for cone-beam CT with a parameterized trajectory.

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.

Interior tomography in microscopic CT with image reconstruction constrained by full field of view scan at low spatial resolution.

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.

Enhancing the anti-multiple myeloma efficiency in a cancer stem cell xenograft model by conjugating the ABCG2 antibody with microbubbles for a targeted delivery of ultrasound mediated epirubicin.

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.

Magnetic Nanoliposomes as in Situ Microbubble Bombers for Multimodality Image-Guided Cancer Theranostics.

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 (H2S) 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 H2S bubbles. This dynamic process can be monitored by magnetic resonance and ultrasound dual modal imaging. Importantly, the intratumoral generated H2S 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 H2S 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.

PEGylated long-circulating liposomes deliver homoharringtonine to suppress multiple myeloma cancer stem cells.

The goal of this investigation was to evaluate the inhibiting effect of high proportion polyethyleneglycol of long-circulating homoharringtonine liposomes on RPMI8226 multiple myeloma cancer stem cells. The CD138-CD34- multiple myeloma cancer stem cells isolated from RPMI8226 cell line using magnetic activated cell sorting system were, respectively, incubated with the optimized formulation of polyethyleneglycol of long-circulating homoharringtonine liposomes and the homoharringtonine in vitro, and the multiple myeloma cancer stem cell proliferation, colony formation, and cell cycle were analyzed. The inhibition of the multiple myeloma CD138-CD34- cancer stem cell growth was investigated in non-obese-diabetic/severe-combined-immunodeficiency mice that were implanted with multiple myeloma RPMI 8226 cancer stem cells and treated with the LCL-HHT-H-PEG. The results showed that the polyethyleneglycol of long-circulating homoharringtonine liposomes significantly inhibited MM cancer stem cell proliferation, colony formation, and induced cancer stem cell apoptosis in vitro as well as MM cancer stem cell growth in non-obese-diabetic/severe-combined-immunodeficiency mice compared with the homoharringtonine. In addition, the mouse bone mineral density and the red blood cell count were significantly increased in polyethyleneglycol of long-circulating homoharringtonine liposomes group. In conclusion, the data demonstrated that the developed polyethyleneglycol of long-circulating homoharringtonine liposomes formulation may serve as an efficient therapeutic drug for suppressing CD138-CD34- multiple myeloma cancer stem cell growth by inducing cancer stem cell apoptosis in non-obese-diabetic/severe-combined-immunodeficiency mouse model. Impact statement Multiple myeloma (MM) remains largely incurable until now. One of the 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 wanted to extend our previous investigation22 that whether we developed the LCL-HHT-H-PEG formulation have an inhibitory effect on MM CD138-CD34-CSCs in MM CSC engrafted NOD/SCID mouse model. Our data from the present study have demonstrated the therapeutic effect of LCL-HHT-H-PEG on MM-bearing mouse model. The study represents the first attempt to demonstrate that the LCL-HHT-H-PEG formulation is available for treatment MM patients in clinic. Therefore, this finding is important and deserves publication in Experimental Biology and Medicine.

A fast beam hardening correction method incorporated in a filtered back-projection based MAP algorithm.

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.