The technique of Localizer Radiography (LR) can realize patient oriented automatic exposure control based on attenuation information. Rotating Projection based Localizer Radiography (RPLR), as a dynamic tube positioned scanning, aims to improve the whole clinical workflow. However, topogram (topo) reconstruction in RPLR is affected by sparse sampling. This paper proposed a deep learning model which contains transformers (power in modeling long-term relationship) and CNNs (high texture modeling capacity) to implement projection context restoration for topo reconstruction. With a coarse topo prior generated by the transformers based on sparse sampling data, high-fidelity topo texture can be rendered with CNNs, which reveals great potential for topo reconstruction in RPLR.
We would propose a Deep Learning based Model Observer (DLMO) to assess performance of computed tomography (CT) images generated by applying tin-filter based spectral shaping technique. The DLMO was constructed based on a simplified VGG neural network trained from scratch. The training and test image datasets were obtained by scanning an anthropomorphic phantom with high-fidelity pulmonary structure at four dose levels with and without tin-filter, respectively. Spherical urethane foams were attached at variant positions of pulmonary tree to mimic ground glass nodule (GGN). These low dose CT scan images were assessed by the trained DLMO for lung nodule detection. The result demonstrated that spectral shaping by tin-filter can provide additional benefits on detection accuracy for certain ultra-low dose level scan (~0.2mGy), but faces challenges for extremely low dose level (~0.05mGy) due to significant noise. For normal dose range (~0.5 to 1mGy), both images from scan with and scan without tin-filter can achieve comparable detection accuracy on mimic GGN objects. A human observer (HO) study performed by 8 experienced CT image quality engineers on the same dataset as a signal-known-exactly (SKE) nodule detection task also indicated similar results.
Subjective reading is still the majority way in current medical image diagnostics, and the visualization effect of images is one important factor which may affect the reading performance or diagnostic quality. In computed tomography (CT), CT numbers are converted into greyscale images by the display window settings. Therefore, settings of display window width and window level significantly influence the image visibility, and the object detectability could be enhanced with appropriate display window settings. In this study, we propose a new idea that the window settings can be automatically adjusted based on greyscale-based contrast-to-noise ratio, which takes into account the effect of the window settings on image quality. With optimized window settings, the greyscale-based image contrast is enhanced and reading performance is improved.
A 3D printed heart chamber phantom was developed to work combined with other commercially available phantom kit. Based on CT image combined with traditional phantom and anatomic structures, the 3D model was generated and input for printing with selected materials. The 3D printed phantom could realize multi-dimensional motion and deformation similarity, improved HU behavior as contrast enhanced tissue mimic, biological closed anthropomorphic structure including cardiac chambers and coronary arteries with contrast agents, as well as inserts for anatomic or functional abnormalities simulation. With those properties the proposed 3D printed phantom could potentially be used for either CCTA imaging performance or CCTA scan strategy verifications combined with scanner and patient properties.
Attenuation information from Localizer Radiograph (LR) is the basis for Automatic Exposure Control. However, the total achievable dose optimization could be significantly affected by either LR based attenuation calculation or patient positioning. To avoid those aspects, we proposed an integrated procedure for more robust and accurate attenuation calculation as well as possible automatic patient centering combined with the Rotating Projection base Localizer Radiograph (RPLR). A 3D attenuation map with more accurate attenuation information and automatic patient centering can be realized with one pre-view scan, so that a complete automatic workflow with always optimized dose modulation can be enabled.
X-ray scatter is a major limit for good CT image quality. Apart from using hardware approach (e.g. anti-scatter grid), computational algorithms based on Monte-Carlo simulation or convolution kernels have been proven to be valid for compensating scatter effect. However, computational algorithms always have to take care about the balance between complexity and efficiency, so the performance has some limitation when scatter contribution is large. In this paper we proposed a deep learning based approach by adopting a convolutional neuro-network (CNN) to predict the scatter distribution on projection domain. The performance of the CNN-based model is validated in both projection domain as well as reconstructed image domain. The result shows that the scatter correction algorithm with learning approach is able to compensate the artifact from scatter radiations under various complicated scenarios, resulting in equivalent or even better image quality than commercially used kernel-based scatter correction algorithm.
In a modern CT system, the localizer radiograph (LR), which makes a visualized scan range planning and provides information for individual patient oriented automatic exposure control, is now a standard procedure before a major diagnostic scan. We proposed a rotating projection based localizer radiograph (RPLR), in contrast to current realization as static tube positioned scanning. With this approach, the localizer Radiograph can be realized to improve the whole clinical workflow by providing no geometry deformation from multiple viewing directions within one scan, with balanced image performance and controllable x-ray dose, . Somehow with this feasibility check on current 16-row CT gantry, the scanned dose is still higher than traditional situation, but with dedicated dose reduction techniques proposed with the RPLP, the exposure dose for the scanning can be dramatically decreased.
Subjective reading is still the majority way in current medical image diagnostics, and the image visualization effect to the observer is very important for the reading performance. And the display window settings play the significant role on the display quality of CT images. To improve the greyscale-based image contrast detectability, we propose a new idea that the window settings can be automatically adjusted in accordance with human visual properties. With the optimized window settings, the greyscalebased image contrast is enhanced, reading performance is improved by maximizing the visibility of targeting objects which the observer focusing on, and image impression is maintained as some level of consistency.
In CT system, the distance between the scanned object and the detector gives a significant impact on the total scatter intensity arriving at the detector. As a result, if an object is scanned off-center, its relative distance to the detector is changing when CT system is rotating, and the detector received scatter intensity will also be changing throughout the scan. In general only after the location information is obtained from the entire 360 degree raw data, the precise scatter radiation can be predicted. This could slow down reconstruction performance a lot. In this paper, we would like to propose a purely projection based scatter correction algorithm, which can be processed independently inside each projection. The promising results of the proposed algorithm suggests the variance of scatter received at different locations could be well predicted, meanwhile the reconstruction speed could be well maintained.
In modern multi-slice CT systems, bowtie shape wedge filter is widely used for optimizing patient received radiation dose distribution. Since wedge filter is usually fixed in the path of X-ray, the induced scatter radiation could give impact on image quality under certain cases. In order to compensate this extra scatter radiation and improve the image quality, we introduced a wedge scatter correction algorithm integrated in the raw data pre-processing workflow. After the algorithm is implemented on our latest CT systems, the improvement can be seen from both water phantom images and clinical patient images.
Scatter radiation in multi-slice CT system nowadays is playing a more and more important role, as the slice number is getting larger and larger, e.g. from 16 to 64 and even more. Scatter radiation may downgrade image quality e.g. create inhomogeneity and decrease the image contrast. Although in current multi-slice CT, anti-scatter collimation is widely used to reduce the scatter radiation received, as the detector is getting wider, its efficiency is getting weaker. Although beam hardening correction can somehow guarantee image homogeneity, as beam hardening effect and scatter radiation have different physical origin, a delegated scatter correction algorithm is desired. In this paper, we would like to propose a scatter correction algorithm working during data pre-processing before image reconstruction. After we implemented this algorithm in Siemens latest released Somatom Go. CT system, we obtain good image quality especially under certain clinical cases.
With the online z-axis tube current modulation (OZTCM) technique proposed by this work, full automatic exposure
control (AEC) for CT systems could be realized with online feedback not only for angular tube current modulation
(TCM) but also for z-axis TCM either. Then the localizer radiograph was not required for TCM any more. OZTCM
could be implemented with 2 schemes as attenuation based μ-OZTCM and image noise level based μ-OZTCM.
Respectively the maximum attenuation of projection readings and standard deviation of reconstructed images can be
used to modulate the tube current level in z-axis adaptively for each half (180 degree) or full (360 degree) rotation.
Simulation results showed that OZTCM achieved better noise level than constant tube current scan case by using same
total dose in mAs. The OZTCM can provide optimized base tube current level for angular TCM to realize an effective
auto exposure control when localizer radiograph is not available or need to be skipped for simplified scan protocol in
case of emergency procedure or children scan, etc.
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