scholarly journals A Novel 3D Reconstruction Algorithm of Motion-Blurred CT Image

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhang Jing ◽  
Guo Qiang ◽  
Han Fang ◽  
Li Zhan-Li ◽  
Li Hong-An ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Signal To Noise Ratio ◽  
Golden Section ◽  
Reconstruction Algorithm ◽  
Spatial Arrangement ◽  
Ct Images ◽  
Normal Vector ◽  
Ct Image ◽  
Clear Image ◽  
Image Translation

The majority of medical workers are eager to obtain realistic and real-time CT 3D reconstruction results. However, autonomous or involuntary motion of patients can cause blurring of CT images. For the 3D reconstruction scene of motion-blurred CT image, this paper consists of two parts: firstly, a GAN image translation network deblurring algorithm is proposed to remove blurred results. This algorithm adopts the clear image to supervise the training process of the blurred image, which creates solutions that are close to the clear image. Secondly, this paper proposes a Marching Cubes (MC) algorithm based on the fusion of golden section and isosurface direction smooth (GI-MC) for 3D reconstruction of CT images. The golden section algorithm is used to calculate the equivalent points and normal vectors, which reduces the calculation numbers from four to one. The isosurface direction smooth algorithm computes the mean value of the normal vector, so as to smooth the direction of all triangular patches in spatial arrangement. The experimental results show that for different blurred angle and blurred amplitude, comparing the results of the Shannon entropy ratio and peak signal-to-noise ratio, our GAN image translation network deblurring algorithm has better restoration than other algorithms. Furthermore, for different types of liver patients, the reconstruction accuracy of our GI-MC algorithm is 9.9%, 7.7%, and 3.9% higher than that of the traditional MC algorithm, Li’s algorithm, and Pratomo’s algorithm, respectively.

scholarly journals Study on threshold segmentation of multi-resolution 3D human brain CT image

2018 ◽  
Vol 11 (06) ◽  
pp. 1850037
Author(s):  
Ling-ling Cui ◽  
Hui Zhang
Keyword(s):  
Human Brain ◽  
Signal To Noise Ratio ◽  
Ct Images ◽  
Pathological Diagnosis ◽  
Ct Image ◽  
Segmentation Method ◽  
Signal To Noise ◽  
Threshold Segmentation ◽  
Edge Pixel ◽  
Brain Ct

In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images, a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper. In this method, first, original 3D human brain image information is collected, and CT image filtering is performed to the collected information through the gradient value decomposition method, and edge contour features of the 3D human brain CT image are extracted. Then, the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points, and the 3D human brain CT image is reconstructed with the salient feature point as center. Simulation results show that the method proposed in this paper can provide accuracy up to 100% when the signal-to-noise ratio is 0, and with the increase of signal-to-noise ratio, the accuracy provided by this method is stable at 100%. Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is significantly better than traditional methods in pathological feature estimation accuracy, and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.

scholarly journals Computed Tomography Image under Three-Dimensional Reconstruction Algorithm Based in Diagnosis of Renal Tumors and Retroperitoneal Laparoscopic Partial Nephrectomy

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Haijie Zhang ◽  
Fu Yin ◽  
Liyang Yang ◽  
Anqi Qi ◽  
Weiwei Cui ◽  
...  
Keyword(s):  
Computed Tomography ◽  
3D Reconstruction ◽  
Partial Nephrectomy ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Ct Images ◽  
Renal Tumors ◽  
Dimensional Reconstruction ◽  
Group A ◽  
Group B

This study was to explore the clinical application value of computed tomography (CT) images based on a three-dimensional (3D) reconstruction algorithm for laparoscopic partial nephrectomy (LPN) in patients with renal tumors. 30 cases of renal cell carcinoma (RCC) patients admitted to the hospital were selected as the research objects and were rolled into two groups using a random table method. The patients who received PLN under the three-dimensional reconstruction and laparoscopic technique were included in the experimental group (group A), and the patients who received LPN using CT images only were included in the control group (group B). In addition, the treatment results of the two groups of patients were compared and analyzed. Results. The effective rate of the established model was 93.3%; the total renal arteriovenous variability of group A (13.3%) was higher than that of group B (6.7%), and the operation time (131.5 ± 32.1 minutes) was much lower than that of group B (158.7 ± 36.2 minutes), showing statistical significance ( P  < 0.05). Conclusion. CT images based on 3D reconstruction algorithms had high clinical application value for LPN in patients with renal tumors, which could improve the efficiency and safety of LPN.

scholarly journals Three-Dimensional Reconstruction Algorithm of Computed Tomography Imaging for Surgical Treatment and Rapid Rehabilitation Nursing of Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lijun Wang ◽  
Haiyan Lin ◽  
Hong Zheng ◽  
Yuying Jiang
Keyword(s):  
Computed Tomography ◽  
3D Reconstruction ◽  
Renal Cell ◽  
Three Dimensional ◽  
Life Quality ◽  
Reconstruction Algorithm ◽  
Nursing Intervention ◽  
Ct Images ◽  
3D Ct ◽  
Self Rating

This study was carried out to explore the promotion role of computed tomography (CT) imaging three-dimensional (3D) reconstruction technology and rapid rehabilitation nursing intervention (RRNI) in the treatment of patients with renal cell cancer (RCC) laparoscopic radical nephrectomy (LRN) in view of the patient’s condition. 98 RCC patients who were admitted to the hospital from July 2019 to July 2020 were selected as the research subjects, and all patients underwent the LRN and the RRIN. Of which, 46 RCC patients were scanned with CT images (regarded as the CT group), and 46 RCC patients were scanned with CT images based on 3D reconstruction algorithms (regarded as the 3D CT group). The clinical efficacy and the life quality, pain degree, and adverse mood changes before and after the RRN were analyzed and compared. The results showed that the surgery time in the 3D CT group and the CT group was 130.2 ± 42.8 minutes and 162.4 ± 38.5 minutes, respectively ( P < 0.05 ). The recurrence rate of RCC in both groups was 0%. The estimated blood loss in the 3D CT group and the CT group was 93.6 ± 35.5 mL and 90.3 ± 40.2 mL, respectively; the complication rate in the 3D CT and CT group was 5% and 12%, respectively; the hospital stay in the 3D CT and CT group was 12.5 ± 4.7 days and 12.1 ± 3.2 days, respectively, which had no statistical significance ( P > 0.05 ). The scores of visual analogue scale (VAS), 36-Item Short-Form Health Survey (SF-36), self-rating depression scale (SDS), and self-rating anxiety scale (SAS) of patients in the two groups were statistically significant ( P < 0.05 ). It indicated that CT images based on the 3D reconstruction algorithm could be applied in LRN of RCC patients to shorten the surgery time and improve the surgical effect, and implementation of the RRN could relieve the adverse mood of RCC patients and effectively improve their life quality.

Nondestructive Inspection of Thin, Low-Z Samples using Multiplexed Compton Scatter Tomography

1997 ◽  
Vol 503 ◽  
Author(s):  
B. L. Evans ◽  
J. B. Martin ◽  
L. W. Burggraf
Keyword(s):  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Line Broadening ◽  
Compton Scatter ◽  
X Ray ◽  
Tomography System ◽  
Transmission Computed Tomography ◽  
High Purity Germanium ◽  
Scattered Energy

ABSTRACTThe viability of a Compton scattering tomography system for nondestructively inspecting thin, low Z samples for corrosion is examined. This technique differs from conventional x-ray backscatter NDI because it does not rely on narrow collimation of source and detectors to examine small volumes in the sample. Instead, photons of a single energy are backscattered from the sample and their scattered energy spectra are measured at multiple detector locations, and these spectra are then used to reconstruct an image of the object. This multiplexed Compton scatter tomography technique interrogates multiple volume elements simultaneously. Thin samples less than 1 cm thick and made of low Z materials are best imaged with gamma rays at or below 100 keV energy. At this energy, Compton line broadening becomes an important resolution limitation. An analytical model has been developed to simulate the signals collected in a demonstration system consisting of an array of planar high-purity germanium detectors. A technique for deconvolving the effects of Compton broadening and detector energy resolution from signals with additive noise is also presented. A filtered backprojection image reconstruction algorithm with similarities to that used in conventional transmission computed tomography is developed. A simulation of a 360–degree inspection gives distortion-free results. In a simulation of a single-sided inspection, a 5 mm × 5 mm corrosion flaw with 50% density is readily identified in 1-cm thick aluminum phantom when the signal to noise ratio in the data exceeds 28.

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

Model-based reconstruction algorithm in the detection of acute trauma-related lesions in brain CT examinations

2021 ◽  
pp. 197140092110087
Author(s):  
Andrea De Vito ◽  
Cesare Maino ◽  
Sophie Lombardi ◽  
Maria Ragusi ◽  
Cammillo Talei Franzesi ◽  
...  
Keyword(s):  
Image Quality ◽  
Iterative Reconstruction ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Signal To Noise ◽  
Subjective Image Quality ◽  
Model Based ◽  
Hybrid Iterative Reconstruction ◽  
Noise Ratio ◽  
Enhanced Computed Tomography

Background and purpose To evaluate the added value of a model-based reconstruction algorithm in the assessment of acute traumatic brain lesions in emergency non-enhanced computed tomography, in comparison with a standard hybrid iterative reconstruction approach. Materials and methods We retrospectively evaluated a total of 350 patients who underwent a 256-row non-enhanced computed tomography scan at the emergency department for brain trauma. Images were reconstructed both with hybrid and model-based iterative algorithm. Two radiologists, blinded to clinical data, recorded the presence, nature, number, and location of acute findings. Subjective image quality was performed using a 4-point scale. Objective image quality was determined by computing the signal-to-noise ratio and contrast-to-noise ratio. The agreement between the two readers was evaluated using k-statistics. Results A subjective image quality analysis using model-based iterative reconstruction gave a higher detection rate of acute trauma-related lesions in comparison to hybrid iterative reconstruction (extradural haematomas 116 vs. 68, subdural haemorrhages 162 vs. 98, subarachnoid haemorrhages 118 vs. 78, parenchymal haemorrhages 94 vs. 64, contusive lesions 36 vs. 28, diffuse axonal injuries 75 vs. 31; all P<0.001). Inter-observer agreement was moderate to excellent in evaluating all injuries (extradural haematomas k=0.79, subdural haemorrhages k=0.82, subarachnoid haemorrhages k=0.91, parenchymal haemorrhages k=0.98, contusive lesions k=0.88, diffuse axonal injuries k=0.70). Quantitatively, the mean standard deviation of the thalamus on model-based iterative reconstruction images was lower in comparison to hybrid iterative one (2.12 ± 0.92 vsa 3.52 ± 1.10; P=0.030) while the contrast-to-noise ratio and signal-to-noise ratio were significantly higher (contrast-to-noise ratio 3.06 ± 0.55 vs. 1.55 ± 0.68, signal-to-noise ratio 14.51 ± 1.78 vs. 8.62 ± 1.88; P<0.0001). Median subjective image quality values for model-based iterative reconstruction were significantly higher ( P=0.003). Conclusion Model-based iterative reconstruction, offering a higher image quality at a thinner slice, allowed the identification of a higher number of acute traumatic lesions than hybrid iterative reconstruction, with a significant reduction of noise.

scholarly journals Optimization of SPECT/CT imaging protocols for quantitative and qualitative 99mTc SPECT

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Dennis Kupitz ◽  
Heiko Wissel ◽  
Jan Wuestemann ◽  
Stephanie Bluemel ◽  
Maciej Pech ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Quantitative Imaging ◽  
Scatter Correction ◽  
Reconstruction Algorithm ◽  
Weighting Factor ◽  
Quantitative Spect ◽  
Pet Ct ◽  
Spect Acquisition ◽  
The Mean ◽  
A Minor

Abstract Background The introduction of hybrid SPECT/CT devices enables quantitative imaging in SPECT, providing a methodological setup for quantitation using SPECT tracers comparable to PET/CT. We evaluated a specific quantitative reconstruction algorithm for SPECT data using a 99mTc-filled NEMA phantom. Quantitative and qualitative image parameters were evaluated for different parametrizations of the acquisition and reconstruction protocol to identify an optimized quantitative protocol. Results The reconstructed activity concentration (ACrec) and the signal-to-noise ratio (SNR) of all examined protocols (n = 16) were significantly affected by the parametrization of the weighting factor k used in scatter correction, the total number of iterations and the sphere volume (all, p < 0.0001). The two examined SPECT acquisition protocols (with 60 or 120 projections) had a minor impact on the ACrec and no significant impact on the SNR. In comparison to the known AC, the use of default scatter correction (k = 0.47) or object-specific scatter correction (k = 0.18) resulted in an underestimation of ACrec in the largest sphere volume (26.5 ml) by − 13.9 kBq/ml (− 16.3%) and − 7.1 kBq/ml (− 8.4%), respectively. An increase in total iterations leads to an increase in estimated AC and a decrease in SNR. The mean difference between ACrec and known AC decreased with an increasing number of total iterations (e.g., for 20 iterations (2 iterations/10 subsets) = − 14.6 kBq/ml (− 17.1%), 240 iterations (24i/10s) = − 8.0 kBq/ml (− 9.4%), p < 0.0001). In parallel, the mean SNR decreased significantly from 2i/10s to 24i/10s by 76% (p < 0.0001). Conclusion Quantitative SPECT imaging is feasible with the used reconstruction algorithm and hybrid SPECT/CT, and its consistent implementation in diagnostics may provide perspectives for quantification in routine clinical practice (e.g., assessment of bone metabolism). When combining quantitative analysis and diagnostic imaging, we recommend using two different reconstruction protocols with task-specific optimized setups (quantitative vs. qualitative reconstruction). Furthermore, individual scatter correction significantly improves both quantitative and qualitative results.

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