Low-dose CT image denoising method based on generative adversarial network

In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial network(GAN)was proposed.First,a noise model based on style GAN2 was constructed to estimate the real noise distribution,and the noise information similar to the real noise distribution was generated as the experimental noise data set.Then,a network model with encoder-decoder architecture as the core based on GAN idea was constructed,and the network model was trained with the generated noise data set until it reached the optimal value.Finally,the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network.The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training,removed image noise and artifacts,and reconstructed image with rich texture and realistic visual effect.

Image J测量CT扫描图像脂肪面积的应用

目的探索一种可靠简便、不依赖CT图像工作站软件测量CT扫描图像腹部脂肪面积(VFA)的方法。方法对8名健康受试者行CT腹部平扫,定义采用西门子CT扫描仪自带软件测量VFA为方法 A,西门子CT扫描后的图像采用Image J软件来测量为方法 B。以方法 A为金标准,评价方法 B测定结果的准确性及稳定性。结果 (1)准确性:方法 A与方法 B测定的内脏VFA和腹部脂肪总面积均高度相关(r=0.965 8,95%CI:0.817 7~0.994,P<0.001;r=0.997 7,95%CI:0.986 9~0.999 6,P<0.001);Bland-Altman检验证实方法 A与方法 B的一致性良好。方法 A与方法 B测量VFA的变异系数为4.86%。(2)稳定性:采用方法 B测量,观察者组内相关系数ICCs=1.0(95%CI:0.998~1.0),观察者间组内相关系数为ICCs=0.999 8(95%CI:0.999 4~1)。结论采用Image J软件分析腹部及腹内VFA结果准确稳定,不受专业软件的局限。

Investigation of prior image constrained compressed sensing-based spectral X-ray CT image reconstruction

To improve spectral X-ray CT reconstructed image quality, the energy-weighted reconstructed image xbins^W and the separable paraboloidal surrogates（SPS） algorithm are proposed for the prior image constrained compressed sensing（PICCS）-based spectral X-ray CT image reconstruction. The PICCS-based image reconstruction takes advantage of the compressed sensing theory, a prior image and an optimization algorithm to improve the image quality of CT reconstructions.To evaluate the performance of the proposed method, three optimization algorithms and three prior images are employed and compared in terms of reconstruction accuracy and noise characteristics of the reconstructed images in each energy bin.The experimental simulation results show that the image xbins^W is the best as the prior image in general with respect to the three optimization algorithms; and the SPS algorithm offers the best performance for the simulated phantom with respect to the three prior images. Compared with filtered back-projection（FBP）, the PICCS via the SPS algorithm and xbins^W as the prior image can offer the noise reduction in the reconstructed images up to 80. 46%, 82. 51%, 88. 08% in each energy bin,respectively. M eanwhile, the root-mean-squared error in each energy bin is decreased by 15. 02%, 18. 15%, 34. 11% and the correlation coefficient is increased by 9. 98%, 11. 38%,15. 94%, respectively.

ImageJ软件在三维立体CT图像处理中的应用

通过使用ImageJ这款开放源代码的图像处理软件对一组人头部颅内的CT切片进行处理,着重介绍了ImageJ在三维CT图像处理中的应用。给出了ImageJ进行三维CT图像处理主要的操作方法和处理后的效果图,为更好地使用ImageJ对CT图像进行三维处理提供参考。通过先计算各个感兴趣区域的面积,再累加实现了三维模型体积的计算。如果进一步知道图像的物理分辨率及CT切片的物理间距,则可以计算出感兴趣目标真实体积。

Beam and image experiment of beam deflection electron gun for distributed X-ray sources

Distributed X-ray sources comprise a single vacuum chamber containing multiple X-ray sources that are triggered and emit X-rays at a specific time and location. This process facilitates an application for innovative system concepts in X-ray and computer tomography. This paper proposes a novel electron beam focusing, shaping,and deflection electron gun for distributed X-ray sources.The electron gun uses a dispenser cathode as an electron emitter, a mesh grid to control emission current, and two electrostatic lenses for beam shaping, focusing, and deflection. Novel focusing and deflecting electrodes were designed to increase the number of focal spots in the distributed source. Two identical half-rectangle opening electrodes are controlled by adjusting the potential of the two electrodes to control the electron beam trajectory, and then, multifocal spots are obtained on the anode target. The electron gun can increase the spatial density of the distributed X-ray sources, thereby improving the image quality. The beam experimental results show that the focal spot sizes of the deflected(deflected amplitude 10.5 mm)and non-deflected electron beams at full width at half maximum are 0.80 mm 90.50 mm and 0.55 mm 90.40 mm, respectively(anode voltage 160 kV; beam current 30 mA). The imaging experimental results demonstrate the excellent spatial resolution and time resolution of an imaging system built with the sources, which has an excellent imaging effect on a field-programmable gate array chip and a rotating metal disk.

Image reconstruction based on total-variation minimization and alternating direction method in linear scan computed tomography

Linear scan computed tomography （LCT） is of great benefit to online industrial scanning and security inspection due to its characteristics of straight-line source trajectory and high scanning speed. However, in practical applications of LCT, there are challenges to image reconstruction due to limited-angle and insufficient data. In this paper, a new reconstruction algorithm based on total-variation （TV） minimization is developed to reconstruct images from limited-angle and insufficient data in LCT. The main idea of our approach is to reformulate a TV problem as a linear equality constrained problem where the objective function is separable, and then minimize its augmented Lagrangian function by using alternating direction method （ADM） to solve subproblems. The proposed method is robust and efficient in the task of reconstruction by showing the convergence of ADM. The numerical simulations and real data reconstructions show that the proposed reconstruction method brings reasonable performance and outperforms some previous ones when applied to an LCT imaging problem.

Automatic Segmentation of Liver Tumor in CT Images with Deep Convolutional Neural Networks

Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. The CNNs is one of deep learning models with some convolutional filters which can learn hierarchical features from data. We compared the CNNs model to popular machine learning algorithms: AdaBoost, Random Forests (RF), and support vector machine (SVM). These classifiers were trained by handcrafted features containing mean, variance, and contextual features. Experimental evaluation was performed on 30 portal phase enhanced CT images using leave-one-out cross validation. The average Dice Similarity Coefficient (DSC), precision, and recall achieved of 80.06% ± 1.63%, 82.67% ± 1.43%, and 84.34% ± 1.61%, respectively. The results show that the CNNs method has better performance than other methods and is promising in liver tumor segmentation.

A Robust Zero-Watermarking Based on SIFT-DCT for Medical Images in the Encrypted Domain

Remote medical diagnosis can be realized by using the Internet,but when transmitting medical images of patients through the Internet,personal information of patients may be leaked.Aim at the security of medical information system and the protection of medical images,a novel robust zero-watermarking based on SIFT-DCT(Scale Invariant Feature Transform-Discrete Cosine Transform)for medical images in the encrypted domain is proposed.Firstly,the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images.Then,the SIFT-DCT is used to extract the feature sequences of encrypted medical images.Next,zero-watermarking technology is used to ensure that the region of interest of medical images are not changed.Finally,the robust of the algorithm is evaluated by the correlation coefficient between the original watermark and the attacked watermark.A series of attack experiments are carried out on this method,and the results show that the algorithm is not only secure,but also robust to both traditional and geometric attacks,especially in clipping attacks.

Reduction of artifacts in dental cone beam CT images to improve the three dimensional image reconstruction

Cone-beam CT (CBCT) scanners are based on volumetric tomography, using a 2D extended digital array providing an area detector [1,2]. Compared to traditional CT, CBCT has many advantages, such as less X-ray beam limitation, and rapid scan time, etc. However, in CBCT images the x-ray beam has lower mean kilovolt (peak) energy, so the metal artifact is more pronounced on. The position of the shadowed region in other views can be tracked by projecting the 3D coordinates of the object. Automatic image segmentation was used to replace the pixels inside the metal object with the boundary pixels. The modified projection data, using synthetically Radon Transformation, were then used to reconstruct a new back projected CBCT image. In this paper, we present a method, based on the morphological, area and pixel operators, which we applied on the Radon transformed image, to reduce the metal artifacts in CBCT, then we built the Radon back project images using the radon invers transformation. The artifacts effects on the 3d-reconstruction is that, the soft tissues appears as bones or teeth. For the preprocessing of the CBCT images, two methods are used to recognize the noisy black areas that the first depends on thresholding and closing algorithm, and the second depends on tracing boundaries after using thresholding algorithm too. The intensity of these areas is the lowest in the image than other tissues, so we profit this property to detect the edges of these areas. These two methods are applied on phantom and patient image data. It deals with reconstructed CBCT dicom images and can effectively reduce such metal artifacts. Due to the data of the constructed images are corrupted by these metal artifacts, qualitative and quantitative analysis of CBCT images is very essential.

Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging:Comparative Analysis of 2D,2.5D,and 3D Approaches Using UNet Transformer

The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome prediction.Motivated by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical images.Specifically,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution.The primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and transformers.Our proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer models.The models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and SegRap2023.Performance was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation accuracy.For instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model configurations.The 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the 2D model,although effective,generally underperformed compared to its 2.5D and 3D counterparts.Compared to related literature,our study confirms the advantages of incorporating additional spatial context,as seen in the improved performance of the 2.5D model.This research fills a significant gap by providing a detailed comparative analysis of different model dimensions and their impact on H&N segmentation accuracy in dual PET/CT imaging.

Transparent and Accurate COVID-19 Diagnosis:Integrating Explainable AI with Advanced Deep Learning in CT Imaging

In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.

The Use of Artificial Intelligence on Segmental Volumes, Constructed from MRI and CT Images, in the Diagnosis and Staging of Cervical Cancers and Thyroid Cancers: A Study Protocol for a Randomized Controlled Trial

Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.

A New Medical Image Enhancement Algorithm Based on Fractional Calculus

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images.The captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis process.To overcome this problem,this paper presents a new fractional integral entropy(FITE)that estimates the unforeseeable probabilities of image pixels,posing as the main contribution of the paper.The proposed model dynamically enhances the image based on the image contents.The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’probability.Initially,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image.Next,the contrast of the image is then adjusted to enhance the regions with low visibility.Finally,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image.Tests were conducted on brain MRI,lungs CT,and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality.The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores.Overall,this model improves the details of brain MRI,lungs CT,and kidney MRI scans,and could therefore potentially help the medical staff during the diagnosis process.

基于CT影像儿童枢椎正常发育与变异的解剖特征

背景:枢椎发育演变过程复杂,研究报道较少。CT成像可以显示枢椎的正常发育过程、解剖结构、发育变异与畸形,明确枢椎骨化中心出现与骺板闭合时间及其演变过程和规律具有重要的临床价值。目的:基于CT影像展示儿童枢椎正常发育与变异的解剖结构。方法:回顾性分析2016年6月至2019年11月行颈部扫描的732例0-15岁儿童CT图像。观察指标包括枢椎齿状突、双侧椎弓、椎体骨化中心,齿突尖部二次骨化中心,椎弓、齿突基底部及后正中骺板,以及枢椎发育变异或畸形。分析与比较各项指标在不同年龄下的变化情况,并利用SPSS 17.0统计学软件包进行数据分类处理及统计学分析。结果与结论:(1)732例研究对象包括枢椎正常发育718例(98.1%),畸形或发育异常14例(1.9%);(2)枢椎5个骨化中心,其中双侧椎弓及齿突、椎体骨化中心在出生时已出现;齿突尖部二次骨化中心出现的中位年龄是5.7岁,年龄四分位差(IQR)是4.1-7岁,最早出现的为8个月22 d,最迟未出现的为12岁10个月;(3)齿突尖骺板融合的中位年龄是6岁,IQR是5-8岁,未融合最大年龄是8岁9个月,融合的最小年龄是4岁3个月;(4)双侧椎弓骺板闭合的中位年龄约3.8岁,IQR约2.9-4.6岁,闭合的最小年龄是2岁3个月,未闭合的最大年龄是6岁;(5)齿突基底部骺板闭合的中位数5.2岁,IQR为3.5-6.8岁,闭合最小年龄是2岁6个月,最晚未闭合年龄是9岁6个月;(6)后正中骺板闭合年龄中位数为1.5岁,IQR为1.0-2.1岁,最晚未闭合2例分别为2岁5个月、14岁,最早闭合为6个月20 d;(7)枢椎畸形或发育异常,包括出现副骨化中心及副骺板7例、枢椎游离骨化小骨3例、后正中骺板不闭合2例、无齿突尖部二次骨化中心2例及枢椎齿突骨化中心未出现1例;(8)提示多层螺旋CT扫描、结合多平面重组技术可以完整显示枢椎的解剖结构,评估其正常发育变异及畸形。

Phantom study of the impact of adaptive statistical iterative reconstruction (ASiR^TM) on image quality for paediatric computed tomography

Quantitative analysis of image quality will be helpful for designing ASiRTM-enhanced paediatric CT protocols, balancing image quality and radiation dose. Catphan600 phantom studies were performed on a GE Discovery HD750 64-slice CT scanner. Images were reconstructed with 0% - 100% ASiRTM (tube current 150 mA, variable kVp 80 - 140) in order to determine the optimal ASiRTM-Filtered Back Projection (FBP) blend. Images reconstructed with a 50% ASiRTM-50% FBP blend were compared to FBP images (0% ASiRTM) over a wide range of kVp (80 - 140) and mA (10 - 400) values. Measurements of image noise, CT number accuracy and uniformity, spatial and contrast resolution, and low contrast detectability were performed on axial and reformatted coronal images. Improvements in CNR, low contrast detectability and radial uniformity were observed in ASiRTM images compared to FBP images. 50% ASiRTM was associated with a 26% - 30% reduction in image noise. Changes in noise texture were observed at higher % ASiRTM blends with impact on visualisation of low and high contrast objects. A small decrease in limiting spatial resolution was detected with addition of ASiRTM, more appreciable at very low tube currents. The preferred blend for paediatric body protocols in our study, as determined by the image quality parameters investigated, was 50% ASiRTM when used with tube currents greater than 50 mA.

Co-delivery of retinoic acid and miRNA by functional Au nanoparticles for improved survival and CT imaging tracking of MSCs in pulmonary fibrosis therapy

Mesenchymal stem cells(MSCs)have emerged as promising candidates for idiopathic pulmonary fibrosis(IPF)therapy.Increasing the MSC survival rate and deepening the understanding of the behavior of transplanted MSCs are of great significance for improving the efficacy of MSC-based IPF treatment.Therefore,dual-functional Au-based nanoparticles(Au@PEG@PEI@TAT NPs,AuPPT)were fabricated by sequential modification of cationic polymer polyetherimide(PEI),polyethylene glycol(PEG),and transactivator of transcription(TAT)penetration peptide on AuNPs,to co-deliver retinoic acid(RA)and microRNA(miRNA)for simultaneously enhancing MSC survive and real-time imaging tracking of MSCs during IPF treatment.AuPPT NPs,with good drug loading and cellular uptake abilities,could efficiently deliver miRNA and RA to protect MSCs from reactive oxygen species and reduce their expression of apoptosis executive protein Caspase 3,thus prolonging the survival time of MSC after transplantation.In themeantime,the intracellular accumulation of AuPPT NPs enhanced the computed tomography imaging contrast of transplantedMSCs,allowing them to be visually tracked in vivo.This study establishes an Au-based dual-functional platform for drug delivery and cell imaging tracking,which provides a new strategy for MSC-related IPF therapy.

Low-Dose CT Image Denoising Based on Improved WGAN-gp

In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.

A new imaging mode based on X-ray CT as prior image and sparsely sampled projections for rapid clinical proton CT

Proton computed tomography(CT)has a distinct practical significance in clinical applications.It eliminates 3–5%errors caused by the transformation of Hounsfield unit(HU)to relative stopping power(RSP)values when using X-ray CT for positioning and treatment planning systems(TPSs).Following the development of FLASH proton therapy,there are increased requirements for accurate and rapid positioning in TPSs.Thus,a new rapid proton CT imaging mode is proposed based on sparsely sampled projections.The proton beam was boosted to 350 MeV by a compact proton linear accelerator(LINAC).In this study,the comparisons of the proton scattering with the energy of 350 MeV and 230 MeV are conducted based on GEANT4 simulations.As the sparsely sampled information associated with beam acquisitions at 12 angles is not enough for reconstruction,X-ray CT is used as a prior image.The RSP map generated by converting the X-ray CT was constructed based on Monte Carlo simulations.Considering the estimation of the most likely path(MLP),the prior image-constrained compressed sensing(PICCS)algorithm is used to reconstruct images from two different phantoms using sparse proton projections of 350 MeV parallel proton beam.The results show that it is feasible to realize the proton image reconstruction with the rapid proton CT imaging proposed in this paper.It can produce RSP maps with much higher accuracy for TPSs and fast positioning to achieve ultra-fast imaging for real-time image-guided radiotherapy(IGRT)in clinical proton therapy applications.

A new approach for classification of human brain CT images based on morphological operations

Automatic diagnosis may help to decrease human based diagnosis error and assist physicians to focus on the correct disease and its treatment and to avoid wasting time on diagnosis. In this paper computer aided diagnosis is applied to the brain CT image processing. We compared performance of morphological operations in extracting three types of features, i.e. gray scale, symmetry and texture. Some classifiers were applied to classify normal and abnormal brain CT images. It showed that morphological operations can improve the result of accuracy. Moreover SVM classifier showed better result than other classifiers.

Improved image resolution on thoracic carcinomas by quantitative 18F-FDG coincidence SPECT/CT in comparison to 18F-FDG PET/CT

Currently,18F-FDG coincidence SPECT(Co-SPECT)/CT scan still serves as an important tool for diagnosis,staging,and evaluation of cancer treatment in developing countries.We implemented full physical corrections(FPC) to Co-SPECT(quantitative Co-SPECT) to improve the image resolution and contrast along with the capability for image quantitation.FPC included attenuation,scatter,resolution recovery,and noise reduction.A standard NEMA phantom filled with 10:1 F-18 activity concentration ratio in spheres and background was utilized to evaluate image performance.Subsequently,15 patients with histologically confirmed thoracic carcinomas were included to undergo a 18 F-FDG Co-SPECT/CT scan followed by a 18 F-FDG PET/CT scan.Functional parameters as SUVmax,SUVmean,SULpeak,and MTV from both quantitative Co-SPECT and PET were analyzed.Image resolution of Co-SPECT for NEMA phantom was improved to reveal the smallest sphere from a diameter of 28 mm to 22 mm(17 mm for PET).The image contrast was enhanced from 1.7 to 6.32(6.69 for PET) with slightly degraded uniformity in background(3.1% vs.6.7%)(5.6% for PET).Patients’ SUVmax,SUVmean,SULpeak,and MTV measured from quantitative Co-SPECT were overall highly correlated with those from PET(r=0.82-0.88).Adjustment of the threshold of SUVmax and SUV to determine SUVmean and MTV did not further change the correlations with PET(r=0.81-0.88).Adding full physical corrections to Co-SPECT images can significantly improve image resolution and contrast to reveal smaller tumor lesions along with the capability to quantify functional parameters like PET/CT.