A novel design of ultrafast micro-CT system based on carbon nanotube: A feasibility study in phantom

Fully Vacuum-Sealed Diode-Structure Addressable ZnO Nanowire Cold Cathode Flat-Panel X-ray Source: Fabrication and Imaging Application

A fully vacuum-sealed addressable flat-panel X-ray source based on ZnO nanowire field emitter arrays (FEAs) was fabricated. The device has a diode structure composed of cathode panel and anode panel. ZnO nanowire cold cathodes were prepared on strip electrodes on a cathode panel and Mo thin film strips were prepared on an anode panel acting as the target. Localized X-ray emission was realized by cross-addressing of cathode and anode electrodes. A radiation dose rate of 10.8 μGy/s was recorded at the anode voltage of 32 kV. The X-ray imaging of objects using different addressing scheme was obtained and the imaging results were analyzed. The results demonstrated the feasibility of achieving addressable flat-panel X-ray source using diode-structure for advanced X-ray imaging.

Self-supervised CT super-resolution with hybrid model

Software-based methods can improve CT spatial resolution without changing the hardware of the scanner or increasing the radiation dose to the object. In this work, we aim to develop a deep learning (DL) based CT super-resolution (SR) method that can reconstruct low-resolution (LR) sinograms into high-resolution (HR) CT images. We mathematically analyzed imaging processes in the CT SR imaging problem and synergistically integrated the SR model in the sinogram domain and the deblur model in the image domain into a hybrid model (SADIR). SADIR incorporates the CT domain knowledge and is unrolled into a DL network (SADIR-Net). The SADIR-Net is a self-supervised network, which can be trained and tested with a single sinogram. SADIR-Net was evaluated through SR CT imaging of a Catphan700 physical phantom and a real porcine phantom, and its performance was compared to the other state-of-the-art (SotA) DL-based CT SR methods. On both phantoms, SADIR-Net obtains the highest information fidelity criterion (IFC), structure similarity index (SSIM), and lowest root-mean-square-error (RMSE). As to the modulation transfer function (MTF), SADIR-Net also obtains the best result and improves the MTF50% by 69.2% and MTF10% by 69.5% compared with FBP. Alternatively, the spatial resolutions at MTF50% and MTF10% from SADIR-Net can reach 91.3% and 89.3% of the counterparts reconstructed from the HR sinogram with FBP. The results show that SADIR-Net can provide performance comparable to the other SotA methods for CT SR reconstruction, especially in the case of extremely limited training data or even no data at all. Thus, the SADIR method could find use in improving CT resolution without changing the hardware of the scanner or increasing the radiation dose to the object.

Modularized data-driven reconstruction framework for nonideal focal spot effect elimination in computed tomography

PURPOSE

High-performance computed tomography (CT) plays a vital role in clinical decision-making. However, the performance of CT imaging is adversely affected by the nonideal focal spot size of the x-ray source or degraded by an enlarged focal spot size due to aging. In this work, we aim to develop a deep learning-based strategy to mitigate the problem so that high spatial resolution CT images can be obtained even in the case of a nonideal x-ray source.

METHODS

To reconstruct high-quality CT images from blurred sinograms via joint image and sinogram learning, a cross-domain hybrid model is formulated via deep learning into a modularized data-driven reconstruction (MDR) framework. The proposed MDR framework comprises several blocks, and all the blocks share the same network architecture and network parameters. In essence, each block utilizes two sub-models to generate an estimated blur kernel and a high-quality CT image simultaneously. In this way, our framework generates not only a final high-quality CT image but also a series of intermediate images with gradually improved anatomical details, enhancing the visual perception for clinicians through the dynamic process. We used simulated training datasets to train our model in an end-to-end manner and tested our model on both simulated and realistic experimental datasets.

RESULTS

On the simulated testing datasets, our approach increases the information fidelity criterion (IFC) by up to 34.2%, the universal quality index (UQI) by up to 20.3%, the signal-to-noise (SNR) by up to 6.7%, and reduces the root mean square error (RMSE) by up to 10.5% as compared with FBP. Compared with the iterative deconvolution method (NSM), MDR increases IFC by up to 24.7%, UQI by up to 16.7%, SNR by up to 6.0%, and reduces RMSE by up to 9.4%. In the modulation transfer function (MTF) experiment, our method improves the MTF_50% by 34.5% and MTF_10% by 18.7% as compared with FBP, Similarly remarkably, our method improves MTF_50% by 14.3% and MTF_10% by 0.9% as compared with NSM. Also, our method shows better imaging results in the edge of bony structures and other tiny structures in the experiments using phantom consisting of ham and a bottle of peanuts.

CONCLUSIONS

A modularized data-driven CT reconstruction framework is established to mitigate the blurring effect caused by a nonideal x-ray source with relatively large focal spot. The proposed method enables us to obtain high-resolution images with less ideal x-ray source.

Systematic imaging in medicine: a comprehensive review

Systematic imaging can be broadly defined as the systematic identification and characterization of biological processes at multiple scales and levels. In contrast to "classical" diagnostic imaging, systematic imaging emphasizes on detecting the overall abnormalities including molecular, functional, and structural alterations occurring during disease course in a systematic manner, rather than just one aspect in a partial manner. Concomitant efforts including improvement of imaging instruments, development of novel imaging agents, and advancement of artificial intelligence are warranted for achievement of systematic imaging. It is undeniable that scientists and radiologists will play a predominant role in directing this burgeoning field. This article introduces several recent developments in imaging modalities and nanoparticles-based imaging agents, and discusses how systematic imaging can be achieved. In the near future, systematic imaging which combines multiple imaging modalities with multimodal imaging agents will pave a new avenue for comprehensive characterization of diseases, successful achievement of image-guided therapy, precise evaluation of therapeutic effects, and rapid development of novel pharmaceuticals, with the final goal of improving human health-related outcomes.

Real-time control of respiratory motion: Beyond radiation therapy

Motion management in radiation oncology is an important aspect of modern treatment planning and delivery. Special attention has been paid to control respiratory motion in recent years. However, other medical procedures related to both diagnosis and treatment are likely to benefit from the explicit control of breathing motion. Quantitative imaging - including increasingly important tools in radiology and nuclear medicine - is among the fields where a rapid development of motion control is most likely, due to the need for quantification accuracy. Emerging treatment modalities like focussed-ultrasound tumor ablation are also likely to benefit from a significant evolution of motion control in the near future. In the present article an overview of available respiratory motion systems along with ongoing research in this area is provided. Furthermore, an attempt is made to envision some of the most expected developments in this field in the near future.

Investigation on the resolution of a micro cone beam CT scanner scintillating detector using Monte Carlo methods

The impact of several physical quantities on the spatial resolution of an X-ray scintillating pixel detector for a micro cone beam CT (µCBCT) is investigated and discussed. The XtremeCT from SCANCO Medical AG was simulated using the EGSnrc/EGS++ Monte Carlo (MC) framework and extensively benchmarked in a previous work. The resolution of the detector was determined by simulating a titanium knife-edge to obtain the edge spread function (ESF) and the modulation transfer function (MTF). Propagation of the scintillation light through the scintillator and its coupling into the fiber optics system was taken into account. The contribution of particles scattered in the main scanner components to the detector signal is very low and does not affect the spatial resolution of the detector. The resolution obtained from the energy deposition in the scintillator without any blurring due to the propagation of the scintillation light into the fiber optics array was 31 µm. By assuming isotropic light propagation in the scintillator, the resolution degraded to 360 µm. A simple light propagation model taking into account the impact of the scintillator's columnar microstructures was developed and compared with the MANTIS Monte Carlo simulation package. By reducing the width of the model's light propagation kernel by a factor of 2 compared to the isotropic case, the detector resolution can be improved to 83 µm, which corresponds well to the measured resolution of 86 µm. The resolution of the detector is limited mainly by the propagation of the scintillation light through the scintillator layer. It offers the greatest potential to improve the resolution of the µCBCT imaging system.

A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images

Background

Quality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers.

Methods

In total, 192, 88, 76 and 55 brain images are acquired using T₂^*, T₁, T₂ and contrast-enhanced T₁ (T₁C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed.

Results

To the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, p_w ≥ 0.11; paired-sample t test, p_p ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, p_icc ≥ 0.74). Furthermore, Pearson correlation coefficient (r_p) suggests that SNR_wm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T₂^* (r_p ≥ 0.78), BRISQUE and NIQE in T₁ (r_p ≥ 0.77), BLIINDS-II in T₂ (r_p ≥ 0.68) and BRISQUE and NIQE in T₁C (r_p ≥ 0.62) weighted MR images, while SNR_csf correlates strongly with BLIINDS-II in T₂^* (r_p ≥ 0.63) and in T₂ (r_p ≥ 0.64) weighted MR images.

Conclusions

The consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.