Computed Tomography Image Reconstruction Research Articles

Sparse representation based computed tomography images reconstruction by coupled dictionary learning algorithm

It is very interesting to reconstruct high-resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K-singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low-resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal-to-noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high-resolution image. These parameters play an essential role in the reconstruction of the HR images.

Developments in X-Ray Contrast Media and the Potential Impact on Computed Tomography.

Over the past 120 years, continuous developments in medical imaging has improved diagnosis and treatment for many diseases and has thereby improved treatment outcome and quality of life of many patients. The number of computed tomography (CT) examinations is today increasing by 4% per year worldwide, for a total of approximately 300 million CT scans per year. About 40% of CT scans are contrast enhanced. Intravenous iodinated contrast media are commonly used for contrast enhancement in CT scans to evaluate diseases and determine treatment response. The current gold standards for intravenous x-ray contrast media in CT or interventional angiography are iodinated low- and iso-osmolar compounds such as iopromide, iohexol, or iodixanol. Both classes have similar and favorable efficacy and safety profiles. Although iodine is biologically inert, iodinated contrast media can cause adverse reactions. In the future, one possibility would be to develop iodine-free contrast media that are better suited to higher x-ray tube voltage ranges, allowing greater flexibility for scanning protocols and thus leading to techniques that can provide equivalent diagnostic value at lower doses of radiation. Iodine-free contrast media would in addition provide an alternative to the market standard that could offer benefits for patients with known reactions to low-osmolality contrast media or thyroid disorders. The development of a new contrast medium, however, needs to be put in context with all upcoming technological advances in x-ray and CT. New detector technologies and artificial intelligence algorithms will in the future also improve the CT image reconstruction enabling the reduction of contrast media and radiation doses.

Analysis of Image Magnification in X-Ray fan beam Computed Tomography

X-ray computed tomography (CT) is a non-destructive measurement method that allows inspection into both external and internal geometry of a scanned sample. This work is mainly concerned with industrial X-ray CT and its 2D image reconstruction parameters. The purpose of our study was to investigate how image magnification in 2D X-ray CT is influenced by the object position for a 50.5 mm x 50.5 mm cuboid. Using a standard definition of magnification, equations were derived to enable geometrical magnification to be determined from its position and vice versa knowing the diameter and machine parameters. Experimental data are compared and observed with regard to geometrical magnification. Results from experimental data showed a strong correlation between geometrical magnification and image magnification obtained from the reconstructed CT image. Derived sizes of cuboid from magnification were more consistent horizontally with geometrical magnification than vertically.

Acoustic emission and CT investigation on fracture evolution of granite containing two flaws subjected to freeze–thaw and cyclic uniaxial increasing-amplitude loading conditions

The deterioration mechanism and fatigue fracturing evolution of granite with two pre-existing flaws experiencing freeze–thaw (F-T) treatment are investigated in this work. The flaws in the rock sample were prepared as a combination of a horizontal flaw with an upper inclined flaw above the horizontal flaw according to the joint characteristics in an open pit slope. In-situ acoustic emission monitoring combined with the post-test 3D computed tomography (CT) technique was employed to reveal the fracture evolution behaviors of rock treated with 0, 50, and 80 freeze–thaw cycles. Results show that the freeze–thaw damage impacts the frost heaving force, cyclic deformation, AE activates, crack coalescence pattern and fatigue life of the granite samples. The accumulative AE count/energy decreases with increasing number of freeze–thaw cycles, and the accumulated AE count/energy in a loading stage gradually grows faster. In addition, AE spectral frequency analysis reveals the impact of previous freeze–thaw damage on the formation of the crack scale, the sample is prone to producing large scale cracks under high freeze–thaw treatment. Moreover, 3D reconstructed CT images present an internal crack network pattern, and the most striking finding is that a simple crack network forms for a sample experiencing high F-T fatigue damage. It is suggested that deterioration of the rock bridge structure is strongly impacted by the accumulative freeze–thaw damage. The testing results are helpful to understand the influence of freeze–thaw and fatigue loading on the fracture evolution characteristics of rock in cold regions.

Three-Dimensional Volumetric Renal Reconstruction Based on Geometrical Coefficients.

The article presents an innovative method of 3D computer tomography (CT) image reconstruction of kidney. Diagnosis based on CT scanning allows to obtain projections of multi-dimensional object, made from different directions in order to create cross-sectional (2D) slices. Standard techniques for identifying kidneys in CT images analyze each 2D slice separately. It causes different reconstruction accuracy for the same object at its different heights. This is the main problem of a machine-learning systems. Reconstruction error of end-slices of the kidney model is often greater than the error of the kidney's middle part. The main idea of the technique presented in this paper is to analyze the largest coherent 3D spatial-areas. This technique allows to increase the accuracy of kidney detection as well as to decrease the FP (false positive) error. An additional advantage of the developed algorithm is the possibility of obtaining a precise model representing the 3D view of an entire kidney.

Changes of incudostapedial joint angle in stapedotomy: does it impact hearing outcomes?

The aims of this article are: (1) is there an ideal incudostapedial joint(ISJ) angle after stapedotomy? (2) is there any difference between pre- and postoperative ISJ angle? and (3) what is the significance of the ISJ angle in postoperative hearing outcomes? Forty six ears from 39 different adult patients (28 women and 11 men; 21 left and 25 right ears) with a mean age of 39years with clinical otosclerosis who underwent stapedotomy between May 2017 and May 2019 were retrospectively registered, including seven bilateral surgery cases. ISJ angle and intravestibular depth of the stapes prosthesis were measured from multiple planar reconstruction-computed tomography images and the length of the prosthesis was measured during surgery. Relationships between the ISJ angle parameters and postoperative hearing outcomes and parameters of the prosthesis were analyzed. The mean ISJ angle was 93.3° ± 8.8° preoperatively and 101.9° ± 6.3° postoperatively, increasing by 8.6° during stapedotomy (p < 0.01). There were weak and negative correlations between ISJ angle changes and postoperative air conduction gains at frequencies ≤1kHz and bone conduction gains at 0.5kHz. When the postoperative ISJ angle changed more than 20°, the success rate of the procedure decreased to 0%. The stapedotomy operation increased the ISJ angle. The success of postoperative auditory outcomes had more to do with the ISJ angle change than the value of the angle itself, indicating there is no universal ideal ISJ angle that surgeons should aim for during stapedotomy.

Computed tomography reconstruction for evaluating response in axillary lymph nodes of breast cancer after neoadjuvant chemotherapy.

To assess the performance of computed tomography (CT) reconstruction in evaluating the response in metastatic lymph nodes after neoadjuvant chemotherapy (NAC) in breast cancer patients. Patients undergoing pre-NAC and post-NAC CT were identified from the Peking University Cancer Hospital database. Axillary Lymph nodes (ALNs) that shrunk to < 5mm in short-axis diameter after NAC on CT reconstruction images were classified as clinical complete response. Evaluations of CT reconstruction on ALNs were correlated with residual nodal disease in the final pathology. Overall, 53 invasive breast cancer patients between October 2016 and March 2018 were eligible for our study. The median age was 48 (range 35-70) years. Most women presented with T2 tumors (35/53, 66.0%). After NAC, 20 (37.7%) patients without residual nodal disease were defined as pCR. Of 53 patients, 18 (33.9%) showed negative conversion of ALNs on CT reconstruction evaluation; axillary pCR was present in 16/18 (88.9%) patients. Among 35 patients with abnormal nodes on post-NAC CT reconstruction, 31 (88.6%) had axillary metastasis on the final pathology. The sensitivity and specificity of CT reconstruction in predicting node-negative status were 80.0% and 93.9%, respectively. The positive predictive value was 84.9% for lymph node pCR. The area under the receiver operating characteristic curve of each imaging modality was 0.870 (95% confidence interval 0.755-0.984). CT reconstruction was useful for evaluating ALNs response following NAC and may be used to predict axillary nodes' pCR with adequate accuracy.

Patient Counseling Through the Pelvicalyceal-shaped Labyrinth: In Search of an Easy Understanding of the Upcoming Stone Removal: A Pilot Study

ObjectiveTo investigate the impact of a mobile application (app) displaying a visualization of the internal anatomy of the pelvicalyceal systemPCS and of kidney stones on patient understanding of their affected kidney anatomy and their upcoming percutaneous nephrolithotripsy (PCNL). Materials and methodsOne-hundred patients who required PCNL were included in this study, and all patients were nonrandomly distributed into 2 groups: counseled using only 3D-reconstructed computed tomography (CT) images (group 1) or using only the “InsKid” app (group 2). Patient data were obtained from CT scans as Digital Imaging and Communications in Medicine format and converted into stereolithography (STL) format. All patients completed the questionnaire after counseling, and the results were compared between groups. ResultsPatients from group 2 better understood the anatomy of their affected kidney by 53%; awareness regarding their stone location was better by 32%; the steps of planned surgery, as well as possible complications, were more transparent for patients using the app by 24% and 56%, respectively. The number of patients who were dissatisfied with the mobile counseling dropped by 70%. The average duration of consultation with the 3D reconstruction of CT images was 10.9 ± 0.6 min, while counseling using our program reliably led to a reduction in this duration (7.3 ± 0.5 min). ConclusionThe InsKid app is a freely available, easy-to-use educational software that improves patient counseling without considerable financial expense or long waiting periods for use.

Numerically robust tetrahedron-based tomographic forward and backward projectors on parallel architectures

X-ray tomographic reconstruction typically uses voxel basis functions to represent volumetric images. Due to the structure in voxel basis representations, efficient ray-tracing methods exist allowing fast, GPU accelerated implementations. Tetrahedral mesh basis functions are a valuable alternative to voxel based image representations as they provide flexible, inhomogeneous partitions which can be used to provide reconstructions with reduced numbers of elements or with arbitrarily fine object surface representations. We thus present a robust parallelizable ray-tracing method for volumetric tetrahedral domains developed specifically for Computed Tomography image reconstruction. Tomographic image reconstruction requires algorithms that are robust to numerical errors in floating point arithmetic whilst typical data sizes encountered in tomography require the algorithm to be parallelisable in GPUs which leads to additional constraints on algorithm choices. Based on these considerations, this article presents numerical solutions to the design of efficient ray-tracing algorithms for the projection and backprojection operations. Initial reconstruction results using CAD data to define a triangulation of the domain demonstrate the advantages of our method and contrast tetrahedral mesh based reconstructions to voxel based methods.

Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network

In clinical analysis and diagnosis, high resolution (HR) computed tomography (CT) images are required for proper treatment of a patient. Developing HR medical images by X-ray CT devices require extended radiation exposure with large radiative dosages, putting the patient at potential risk of inducing cancer. So, radiation exposure should be reduced. However, photon starvation and beam hardening in low-dose X-rays will cause severe artifacts. Thus, an accurate reconstruction of low-dose X-ray CT images is required. To this end, we propose a wavelet based multi-channel and multi-scale cross connected residual-in-dense grouped convolutional neural network (WCRDGCNN) for accurate super resolution (SR) of medical images. The adopted filter groups reduce the connection weights, thereby reducing the computational complexity. Gradient vanishing problem is tackled by using residual and dense skip connections. The extensive experimentation results on benchmark datasets show that our method outperforms the state-of-the-art SR methods.

AirNet: Fused analytical and iterative reconstruction with deep neural network regularization for sparse-data CT.

Sparse-data computed tomography (CT) frequently occurs, such as breast tomosynthesis, C-arm CT, on-board four-dimensional cone-beam CT (4D CBCT), and industrial CT. However, sparse-data image reconstruction remains challenging due to highly undersampled data. This work develops a data-driven image reconstruction method for sparse-data CT using deep neural networks (DNN). The new method so-called AirNet is designed to incorporate the benefits from analytical reconstruction method (AR), iterative reconstruction method (IR), and DNN. It is built upon fused analytical and iterative reconstruction (AIR) that synergizes AR and IR via the optimization framework of modified proximal forward-backward splitting (PFBS). By unrolling PFBS into IR updates of CT data fidelity and DNN regularization with residual learning, AirNet utilizes AR such as FBP during the data fidelity, introduces dense connectivity into DNN regularization, and learns PFBS coefficients and DNN parameters that minimize the loss function during the training stage; and then AirNet with trained parameters can be used for end-to-end image reconstruction. A CT atlas of 100 prostate scans was used to validate the AirNet in comparison with state-of-art DNN-based postprocessing and image reconstruction methods. The validation loss in AirNet had the fastest decreasing rate, owing to inherited fast convergence from AIR. AirNet was robust to noise in projection data and content differences between the training set and the images to be reconstructed. The impact of image quality on radiotherapy treatment planning was evaluated for both photon and proton therapy, and AirNet achieved the best treatment plan quality, especially for proton therapy. For example, with limited-angle data, the maximal target dose for AirNet was 109.5% in comparison with the ground truth 109.1%, while it was significantly elevated to 115.1% and 128.1% for FBPConvNet and LEARN, respectively. A new image reconstruction AirNet is developed for sparse-data CT image reconstruction. AirNet achieved the best image reconstruction quality both visually and quantitatively among all methods under comparison for all sparse-data scenarios (sparse-view and limited-angle), and provided the best photon and proton treatment plan quality based on sparse-data CT.

Prevalence of lumbar spondylolysis and spondylolisthesis in patients with degenerative spinal disease

Lumbar spondylolysis generally occurs in adolescent athletes. Bony union can be expected with conservative treatment, however, the fracture does not heal in some cases. When the fracture becomes a pseudoarthrosis, spondylolysis patients have the potential to develop isthmic spondylolisthesis. A cross-sectional study was performed to determine the incidence of spondylolysis and spondylolisthesis, and to elucidate when and how often spondylolisthesis occurs in patients with or without spondylolysis. Patients undergoing computed tomography (CT) scans of abdominal or lumbar regions for reasons other than low back pain were included (n = 580). Reconstruction CT images were obtained, and the prevalence of spondylolysis and spondylolisthesis were evaluated. Of the 580 patients, 37 patients (6.4%) had spondylolysis. Of these 37 patients, 19 patients (51.4%) showed spondylolisthesis, whereas only 7.4% of non-spondylolysis patients showed spondylolisthesis (p < 0.05). When excluding unilateral spondylolysis, 90% (18/20) of spondylolysis patients aged ≥60 years-old showed spondylolisthesis. None of the patients with isthmic spondylolisthesis had received fusion surgery, suggesting that most of these patients didn’t have a severe disability requiring surgical treatment. Our results showed that the majority of bilateral spondylolysis patients aged ≥60 years-old show spondylolisthesis, and suggest that spondylolisthesis occurs very frequently and may develop at a younger age when spondylolysis exists.

Pore Connectivity and Dewatering Mechanism of Tailings Bed in Raking Deep-Cone Thickener Process

Paste and thickened tailings (PTT) technology can improve the utilization and management of tailings from processing plants. The pore size distribution (PSD) and microstructure evolution affected by the rake shear in thickening tailings beds are essential to produce a high-density tailings underflow. Continuous thickening and computed tomography (CT) scanning tests were conducted to study the PSD with and without shear. The pore morphology was studied to reveal the shearing-dewatering performance of the tailings bed. The results show that at a flocculant solution concentration of 0.01 wt % and a feed slurry concentration of 10 wt%, the underflow concentration with and without shear can reach 58.5 wt %and 55.8 wt %, respectively. The CT image reconstruction models demonstrated that the porosity of the sheared tailings bed increased with the bed height. When the bed height increased from 2.5 to 10 cm, the porosity increased from 35.1% to 41.9%, the pore fractal dimension increased from the range 1.8–1.95 to the range 2.1–2.15, and the pore quantity decreased by 21.39%. The average pore volume increased with increasing height by 13.93%, 16.57% and 12.07%. The pore structure became more complex with the bed height, and the connectivity between pores increased to form water-flow channels, which were beneficial to the drainage of sealed water.

Low Dose CT Image Reconstruction Based on Structure Tensor Total Variation Using Accelerated Fast Iterative Shrinkage Thresholding Algorithm.

Low dose computed tomography (CT) has drawn much attention in the medical imaging field because of its ability to reduce the radiation dose. Recently, statistical iterative reconstruction (SIR) with total variation (TV) penalty has been developed to low dose CT image reconstruction. Nevertheless, the TV penalty has the drawback of creating blocky effects in the reconstructed images. To overcome the limitations of TV, in this paper we firstly introduce the structure tensor total variation (STV1) penalty into SIR framework for low dose CT image reconstruction. Then, an accelerated fast iterative shrinkage thresholding algorithm (AFISTA) is developed to minimize the objective function. The proposed AFISTA reconstruction algorithm was evaluated using numerical simulated low dose projection based on two CT images and realistic low dose projection data of a sheep lung CT perfusion. The experimental results demonstrated that our proposed STV1-based algorithm outperform FBP and TV-based algorithm in terms of removing noise and restraining blocky effects.

Modic Changes (MCs) Associated with Endplate Sclerosis Can Prevent Cage Subsidence in Oblique Lumbar Interbody Fusion (OLIF) Stand-Alone

To investigate the association between Modic changes (MCs) and endplate sclerosis; and to investigate the effect of MCs and endplate sclerosis on cage subsidence in oblique lumbar interbody fusion (OLIF) stand-alone. We included 78 inpatients who underwent OLIF stand-alone from August 2015 to August 2017. All patients underwent computed tomography (CT) and magnetic resonance imaging. The presence and the types of MCs were assessed. Endplate sclerosis was evaluated from sagittally reconstructed CT images. Cage subsidence was evaluated at the 1-, 3-, 6-, and 12-month follow-up. Of 78 patients, 92 discs underwent OLIF without posterior instrumentation; 32 of 92 (34.8%) had MCs. Type I, II, and III MCs were seen in 10 (10.9%), 19 (20.7%), and 3 (3.2%) endplates, respectively. Among 32 endplates with MCs, 10 (31.3%) showed evidence of sclerosis on CT images, including 2 of 10 endplates (20%) with type I MCs, 5 of 19 (26.3%) with type II, and 3 of 3 (100%) with type III. Among 60 endplates without MCs, 5 (8.3%) showed evidence of sclerosis. Cage subsidence rate in the no-MCs group was higher than that in the MCs group (P<0.01). We found only 1 case of cage subsidence in the MCs group (n= 32), which was classified as MCs type I, whereas 6 cases of cage subsidence were observed in the no-MCs group (n= 60). Hounsfield unit ratios for sclerotic and no-sclerotic endplates were 2.2 ± 0.3 and 1.1± 0.1, respectively. No subsidence of cage was found in the sclerotic endplates group (n= 15), whereas 7 cases of cage subsidence were found in the no-sclerotic group (n= 77). MCs associated with endplate sclerosis can prevent cage subsidence in OLIF stand-alone.

ADAPTIVE-NET: deep computed tomography reconstruction network with analytical domain transformation knowledge

Recently, the paradigm of computed tomography (CT) reconstruction has shifted as the deep learning technique evolves. In this study, we proposed a new convolutional neural network (called ADAPTIVE-NET) to perform CT image reconstruction directly from a sinogram by integrating the analytical domain transformation knowledge. In the proposed ADAPTIVE-NET, a specific network layer with constant weights was customized to transform the sinogram onto the CT image domain via analytical back-projection. With this new framework, feature extractions were performed simultaneously on both the sinogram domain and the CT image domain. The Mayo low dose CT (LDCT) data was used to validate the new network. In particular, the new network was compared with the previously proposed residual encoder-decoder (RED)-CNN network. For each network, the mean square error (MSE) loss with and without VGG-based perceptual loss was compared. Furthermore, to evaluate the image quality with certain metrics, the noise correlation was quantified via the noise power spectrum (NPS) on the reconstructed LDCT for each method. CT images that have clinically relevant dimensions of 512×512 can be easily reconstructed from a sinogram on a single graphics processing unit (GPU) with moderate memory size (e.g., 11 GB) by ADAPTIVE-NET. With the same MSE loss function, the new network is able to generate better results than the RED-CNN. Moreover, the new network is able to reconstruct natural looking CT images with enhanced image quality if jointly using the VGG loss. The newly proposed end-to-end supervised ADAPTIVE-NET is able to reconstruct high-quality LDCT images directly from a sinogram.

Improving grazing-incidence small-angle X-ray scattering-computed tomography images by total variation minimization.

Grazing-incidence small-angle X-ray scattering (GISAXS) coupled with computed tomography (CT) has enabled the visualization of the spatial distribution of nanostructures in thin films. 2D GISAXS images are obtained by scanning along the direction perpendicular to the X-ray beam at each rotation angle. Because the intensities at the q positions contain nanostructural information, the reconstructed CT images individually represent the spatial distributions of this information (e.g. size, shape, surface, characteristic length). These images are reconstructed from the intensities acquired at angular intervals over 180°, but the total measurement time is prolonged. This increase in the radiation dosage can cause damage to the sample. One way to reduce the overall measurement time is to perform a scanning GISAXS measurement along the direction perpendicular to the X-ray beam with a limited interval angle. Using filtered back-projection (FBP), CT images are reconstructed from sinograms with limited interval angles from 3 to 48° (FBP-CT images). However, these images are blurred and have a low image quality. In this study, to optimize the CT image quality, total variation (TV) regularization is introduced to minimize sinogram image noise and artifacts. It is proposed that the TV method can be applied to downsampling of sinograms in order to improve the CT images in comparison with the FBP-CT images.

Comparison of stand-alone cage and cage-with-plate for monosegmental cervical fusion and impact of virtual reality in evaluating surgical results

ObjectivesAfter ventral decompression of monosegmental cervical spondylotic stenosis, a stand-alone cage (SC) or cage-with-plate (CP) can be inserted for fusion. Postoperative radiological evaluation can be achieved using different imaging modalities. We retrospectively compared complications, as well as clinical and radiological outcomes for both fusion techniques, and analyzed the possible role of virtual reality (VR) in evaluating the postoperative results. Patients and methodsOne hundred seventeen patients were included (SC/CP: 54/63). Complications, as well as clinical and radiological outcomes of both fusion techniques were compared. Computed tomography (CT) scans were visualized via VR to measure the smallest cross-sectional area of the intervertebral neuroforamen in the lateral resection region, and to assess the degree of the intersegmental ossification. ResultsThere were no significant differences between the two fusion techniques regarding perioperative complication rates, fusion rates, or pain parameter (visual analogue scale (VAS) of arm pain, neck disability index). However, advantages regarding subsidence, kyphosis, and VAS of neck pain were found when using the CP versus SC. Using the VR technique, there was no significant difference between the two fusion techniques in the mean size of the cross-sectional area at the end of follow-up. ConclusionDue to the long-term advantages of CP fusion, we prefer a monosegmental cervical spinal fusion using CP. Reconstruction of postsurgical two-dimensional CT images into three-dimensional images, and the spatial and anatomical presentation in VR models, improved the evaluation of these postoperative results.

A pitfall of using the circular‐edge technique with image averaging for spatial resolution measurement in iteratively reconstructed CT images

The circular‐edge technique using a low‐contrast cylindrical object is commonly used to measure the modulation transfer functions (MTFs) in computed tomography (CT) images reconstructed with iterative reconstruction (IR) algorithms. This method generally entails averaging multiple images of the cylinder to reduce the image noise. We suspected that the cylinder edge shape depicted in the IR images might exhibit slight deformation with respect to the true shape because of the intrinsic nonlinearity of IR algorithms. Image averaging can reduce the image noise, but does not effectively improve the deformation of the edge shape; thereby causing errors in the MTF measurements. We address this issue and propose a method to correct the MTF. We scanned a phantom including cylindrical objects with a CT scanner (Ingenuity Elite, Philips Healthcare). We obtained cylinder images with iterative model reconstruction (IMR) algorithms. The images suggested that the depicted edge shape deforms and fluctuates depending on slice positions. Because of this deformation, image averaging can potentially cause additional blurring. We define the deformation function D that describes the additional blurring, and obtain D by analyzing multiple images. The MTF measured by the circular‐edge method (referred to as MTF') can be thought of as the multiplication of the true MTF by the Fourier transformation (FT) of D. We thus obtain the corrected MTF (MTFcorrected) by dividing MTF' by the FT of D. We validate our correction method by comparing the calculated images based on the convolution theorem using MTF' and MTFcorrected with the actual images obtained with the scanner. The calculated image using MTFcorrected is more similar to the actual image compared with the image calculated using MTF', particularly in edge regions. We describe a pitfall in MTF measurement using the circular‐edge technique with image averaging, and suggest a method to correct it.

Study of CT image reconstruction algorithm based on high order total variation

The traditional total variation (TV) minimization algorithm is an image reconstruction algorithm based on compressed sensing, which can accurately reconstruct images from sparse data or highly noisy data and has been widely used in low-dose computed tomography (CT). Sometimes it may lead to staircase effect if the reconstructed image has not obvious piecewise constant feature. Recently, researches in the field of image processing suggested that the high order total variation (HOTV) can effectively suppress staircase effect. Whereas the HOTV reconstruction algorithm has not been carried out deeply and extensively in image reconstruction. Herein, we propose a HOTV reconstruction model and design its adaptive steepest descent-projection onto convex sets (ASD-POCS) solving algorithm, and then characterize its reconstruction performance. We construct the second order TV norm using the second order gradient, and design the data fidelity constrained, second order TV minimization model, and derive the its ASD-POCS algorithm. In order to characterize its reconstruction performance, we use the Shepp-Logan simulation phantom of wavy background, the gray-changing simulation phantom and the chest X-ray image simulation phantom to perform reconstructions. Sparse reconstruction results show that compared to the traditional TV algorithm, the HOTV algorithm can effectively suppress the staircase effect and improve the reconstruction accuracy. Noisy-projections reconstruction results show that the traditional TV algorithm and the HOTV algorithm both have good denoising effect but the HOTV algorithm is slightly better for it can better protect the image edge information. The HOTV algorithm is a better reconstruction algorithm than the TV algorithm in image reconstruction if the piecewise constant feature of the reconstructed image is not so obvious but the grayscale fluctuation feature is prominent. The proposed HOTV algorithm can be extended to various CT configurations and other imaging modalities.