Synthetic Computed Tomography Images Research Articles

A Novel Synthetic CT Generation Method Using Multitask Maximum Entropy Clustering

Due to the risk of radiation from computed tomography (CT) scanning on the human body, the number of CT scans that can be performed on an individual each year is limited. However, CT images play a very important role in medical diagnosis. Therefore, this study proposes a method of generating synthetic CT to solve this problem. Considering that magnetic resonance imaging (MRI) is not harmful to the human body, there is no limit on the number of scans that can be performed with this procedure. In this paper, an image segmentation method is used to segment an MRI, and each segment is given a corresponding Hounsfield Unit (HU) value to finally generate a synthetic CT image. Since the image segmentation performance directly affects the generated synthetic CT image, this paper introduces a multitask learning strategy into a maximum entropy clustering (MEC) algorithm. A multitask maximum entropy clustering (MT-MEC) algorithm is proposed, which is used to effectively segment the MRI of the brain. The algorithm can use knowledge from multiple tasks to improve the learning ability of all tasks, and the MEC algorithm can effectively avoid interference from noise. The experimental results show that the proposed MT-MEC algorithm has good image segmentation performance, which results in reliable performance of the final synthetic CT image.

MR-OPERA: A Multicenter/Multivendor Validation of Magnetic Resonance Imaging–Only Prostate Treatment Planning Using Synthetic Computed Tomography Images

Tovalidate the dosimetric accuracy and clinical robustness of a commercially available software for magnetic resonance (MR) to synthetic computed tomography (sCT) conversion, in an MR imaging-only workflow for 170 prostate cancer patients. The 4 participating centers had MriPlanner (Spectronic Medical), an atlas-based sCT generation software, installed as a cloud-based service. A T2-weighted MR sequence, covering the body contour, was added to the clinical protocol. The MR images were sent from the MR scanner workstation to the MriPlanner platform. The sCT was automatically returned to the treatment planning system. Four MR scanners and 2 magnetic field strengths were included in the study. For each patient, a CT-treatment plan was created and approved according to clinical practice. The sCT was rigidly registered to the CT, and the clinical treatment plan was recalculated on the sCT. The dose distributions from the CT plan and the sCT plan were compared according to a set of dose-volume histogram parameters and gamma evaluation. Treatment techniques included volumetric modulated arc therapy, intensity modulated radiation therapy, and conventional treatment using 2 treatment planning systems and different dose calculation algorithms. The overall (multicenter/multivendor) mean dose differences between sCT and CT dose distributions were below 0.3% for all evaluated organs and targets. Gamma evaluation showed a mean pass rate of 99.12% (0.63%, 1 SD) in the complete body volume and 99.97% (0.13%, 1 SD) in the planning target volume using a 2%/2-mm global gamma criteria. Results of the study show that the sCT conversion method can be used clinically, with minimal differences between sCT and CT dose distributions for target and relevant organs at risk. The small differences seen are consistent between centers, indicating that an MR imaging-only workflow using MriPlanner is robust for a variety of field strengths, vendors, and treatment techniques.

Image Guided Radiation Therapy Using Synthetic Computed Tomography Images in Brain Cancer

The development of synthetic computed tomography (CT) (synCT) derived from magnetic resonance (MR) images supports MR-only treatment planning. We evaluated the accuracy of synCT and synCT-generated digitally reconstructed radiographs (DRRs) relative to CT and determined their performance for image guided radiation therapy (IGRT). Magnetic resonance simulation (MR-SIM) and CT simulation (CT-SIM) images were acquired of an anthropomorphic skull phantom and 12 patient brain cancer cases. SynCTs were generated using fluid attenuation inversion recovery, ultrashort echo time, and Dixon data sets through a voxel-based weighted summation of 5 tissue classifications. The DRRs were generated from the phantom synCT, and geometric fidelity was assessed relative to CT-generated DRRs through bounding box and landmark analysis. An offline retrospective analysis was conducted to register cone beam CTs (n=34) to synCTs and CTs using automated rigid registration in the treatment planning system. Planar MV and KV images (n=37) were rigidly registered to synCT and CT DRRs using an in-house script. Planar and volumetric registration reproducibility was assessed and margin differences were characterized by the van Herk formalism. Bounding box and landmark analysis of phantom synCT DRRs were within 1mm of CT DRRs. Absolute planar registration shift differences ranged from 0.0 to 0.7mm for phantom DRRs on all treatment platforms and from 0.0 to 0.4mm for volumetric registrations. For patient planar registrations, the mean shift differences were 0.4±0.5mm (range, -0.6 to 1.6mm), 0.0±0.5mm (range, -0.9 to 1.2mm), and 0.1±0.3mm (range, -0.7 to 0.6mm) for the superior-inferior (S-I), left-right (L-R), and anterior-posterior (A-P) axes, respectively. The mean shift differences in volumetric registrations were 0.6±0.4mm (range, -0.2 to 1.6mm), 0.2±0.4mm (range, -0.3 to 1.2mm), and 0.2±0.3mm (range, -0.2 to 1.2mm) for the S-I, L-R, and A-P axes, respectively. The CT-SIM and synCT derived margins were <0.3mm different. DRRs generated by synCT were in close agreement with CT-SIM. Planar and volumetric image registrations to synCT-derived targets were comparable with CT for phantom and patients. This validation is the next step toward MR-only planning for the brain.

A novel Zohair filter for deblurring computed tomography medical images

Deblurring computed tomography (CT) images has been an active research topic in recent years because of the wide variety of challenges it offers. Hence, a novel filter is proposed in this article unveiling a simple, efficient, and fast deblurring process that involves few parameters, low calculations and does not utilize the undesirable iterative property or introduce the common deblurring problems. The newly proposed filter is validated on both real and synthetic blurred CT images to provide a sufficient understanding about its performance. Moreover, proper comparisons are made with high-profile deblurring methods, in which the results are evaluated using three reliable quality metrics of feature similarity index (FSIM), structural similarity (SSIM), and visual information fidelity in pixel domain (VIFP). The intensive experiments and performance evaluations exhibited the efficiency of the proposed filter, in that it outperformed all the comparative methods.

Deblurring Computed Tomography Medical Images Using a Novel Amended Landweber Algorithm.

In computed tomography (CT), blurring occurs due to different hardware or software errors and hides certain medical details that are present in an image. Image blur is difficult to avoid in many circumstances and can frequently ruin an image. For this, many methods have been developed to reduce the blurring artifact from CT images. The problems with these methods are the high implementation time, noise amplification and boundary artifacts. Hence, this article presents an amended version of the iterative Landweber algorithm to attain artifact-free boundaries and less noise amplification in a faster application time. In this study, both synthetic and real blurred CT images are used to validate the proposed method properly. Similarly, the quality of the processed synthetic images is measured using the feature similarity index, structural similarity and visual information fidelity in pixel domain metrics. Finally, the results obtained from intensive experiments and performance evaluations show the efficiency of the proposed algorithm, which has potential as a new approach in medical image processing.

An innovative technique for contrast enhancement of computed tomography images using normalized gamma-corrected contrast-limited adaptive histogram equalization

Abstract Image contrast is an essential visual feature that determines whether an image is of good quality. In computed tomography (CT), captured images tend to be low contrast, which is a prevalent artifact that reduces the image quality and hampers the process of extracting its useful information. A common tactic to process such artifact is by using histogram-based techniques. However, although these techniques may improve the contrast for different grayscale imaging applications, the results are mostly unacceptable for CT images due to the presentation of various faults, noise amplification, excess brightness, and imperfect contrast. Therefore, an ameliorated version of the contrast-limited adaptive histogram equalization (CLAHE) is introduced in this article to provide a good brightness with decent contrast for CT images. The novel modification to the aforesaid technique is done by adding an initial phase of a normalized gamma correction function that helps in adjusting the gamma of the processed image to avoid the common errors of the basic CLAHE of the excess brightness and imperfect contrast it produces. The newly developed technique is tested with synthetic and real-degraded low-contrast CT images, in which it highly contributed in producing better quality results. Moreover, a low intricacy technique for contrast enhancement is proposed, and its performance is also exhibited against various versions of histogram-based enhancement technique using three advanced image quality assessment metrics of Universal Image Quality Index (UIQI), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). Finally, the proposed technique provided acceptable results with no visible artifacts and outperformed all the comparable techniques.

Deblurring computed tomography medical images using a novel amended Landweber algorithm.

In computed tomography (CT), blurring occurs due to different hardware or software errors, and hides certain medical details that are present in an image. Image blur is difficult to avoid in many circumstances and can frequently ruin an image. For this, many methods have been developed to reduce the blurring artifact from CT images. The problems with these methods are the high implementation time, noise amplification and boundary artifacts. Hence, this article presents an amended version of the iterative Landweber algorithm to attain artifact-free boundaries and less noise amplification in a faster application time. In this study, both synthetic and real blurred CT images are used to validate the proposed method properly. Similarly, the quality of the processed synthetic images is measured using the Feature Similarity Index (FSIM), Structural Similarity (SSIM) and Visual Information Fidelity in Pixel Domain (VIFP) metrics. Finally, the results obtained from intensive experiments and performance evaluations show the efficiency of the proposed algorithm, which has potential as a new approach in medical image processing.

Metal artefact reduction in computed tomography images by a fourth-order total variation flow

Permanent metallic implants, such as dental fillings and cardiac devices, generate streaks-like artefacts in computed tomography (CT) images. In this article, we propose a strategy to perform metal artefact reduction (MAR) that relies on the total variation-H− 1 inpainting, a variational approach based on a fourth-order total variation (TV) flow. This approach has never been used to perform MAR, although it has been profitably employed in other branches of image processing. A systematic evaluation of the performance is carried out. Comparisons are made with the results obtained using classical linear interpolation and two other partial differential equation-based approaches relying, respectively, on the Fourier's heat equation and on a second order TV flow. Visual inspection of both synthetic and real CT images, as well as computation of similarity indexes, suggests that our strategy for MAR outperforms the others considered here, as it provides best image restoration, highest similarity indexes and for being the only one able to recover hidden structures, a task of primary importance in the medical field.

MR to CT Registration of Brains using Image Synthesis.

Computed tomography (CT) is the standard imaging modality for patient dose calculation for radiation therapy. Magnetic resonance (MR) imaging (MRI) is used along with CT to identify brain structures due to its superior soft tissue contrast. Registration of MR and CT is necessary for accurate delineation of the tumor and other structures, and is critical in radiotherapy planning. Mutual information (MI) or its variants are typically used as a similarity metric to register MRI to CT. However, unlike CT, MRI intensity does not have an accepted calibrated intensity scale. Therefore, MI-based MR-CT registration may vary from scan to scan as MI depends on the joint histogram of the images. In this paper, we propose a fully automatic framework for MR-CT registration by synthesizing a synthetic CT image from MRI using a co-registered pair of MR and CT images as an atlas. Patches of the subject MRI are matched to the atlas and the synthetic CT patches are estimated in a probabilistic framework. The synthetic CT is registered to the original CT using a deformable registration and the computed deformation is applied to the MRI. In contrast to most existing methods, we do not need any manual intervention such as picking landmarks or regions of interests. The proposed method was validated on ten brain cancer patient cases, showing 25% improvement in MI and correlation between MR and CT images after registration compared to state-of-the-art registration methods.

Optimizing contrast agents with respect to reducing beam hardening in nonmedical X-ray computed tomography experiments

Iodine is commonly used as a contrast agent in nonmedical science and engineering, for example, to visualize Darcy flow in porous geological media using X-ray computed tomography (CT). Undesirable beam hardening artifacts occur when a polychromatic X-ray source is used, which makes the quantitative analysis of CT images difficult. To optimize the chemistry of a contrast agent in terms of the beam hardening reduction, we performed computer simulations and generated synthetic CT images of a homogeneous cylindrical sand-pack (diameter, 28 or 56 mm; porosity, 39 vol.% saturated with aqueous suspensions of heavy elements assuming the use of a polychromatic medical CT scanner. The degree of cupping derived from the beam hardening was assessed using the reconstructed CT images to find the chemistry of the suspension that induced the least cupping. The results showed that (i) the degree of cupping depended on the position of the K absorption edge of the heavy element relative to peak of the polychromatic incident X-ray spectrum, (ii) (53)I was not an ideal contrast agent because it causes marked cupping, and (iii) a single element much heavier than (53)I ((64)Gd to (79)Au) reduced the cupping artifact significantly, and a four-heavy-element mixture of elements from (64)Gd to (79)Au reduced the artifact most significantly.

Investigation of a method for generating synthetic CT models from MRI scans of the head and neck for radiation therapy

Magnetic resonance (MR) images often provide superior anatomic and functional information over computed tomography (CT) images, but generally are not used alone without CT images for radiotherapy treatment planning and image guidance. This study aims to investigate the potential of probabilistic classification of voxels from multiple MRI contrasts to generate synthetic CT (‘MRCT’) images. The method consists of (1) acquiring multiple MRI volumes: T1-weighted, T2-weighted, two echoes from a ultra-short echo time (UTE) sequence, and calculated fat and water image volumes using a Dixon method, (2) classifying tissues using fuzzy c-means clustering with a spatial constraint, (3) assigning attenuation properties with weights based on the probability of individual tissue classes being present in each voxel, and (4) generating a MRCT image volume from the sum of attenuation properties in each voxel. The capability of each MRI contrast to differentiate tissues of interest was investigated based on a retrospective analysis of ten patients. For one prospective patient, the correlation of skull intensities between CT and MR was investigated, the discriminatory power of MRI in separating air from bone was evaluated, and the generated MRCT image volume was qualitatively evaluated. Our analyses showed that one MRI volume was not sufficient to separate all tissue types, and T2-weighted images was more sensitive to bone density variation compared to other MRI image types. The short echo UTE image showed significant improvement in contrasting air versus bone, but could not completely separate air from bone without false labeling. Generated MRCT and CT images showed similar contrast between bone and soft/solid tissues. These results demonstrate the potential of the presented method to generate synthetic CT images to support the workflow of radiation oncology treatment planning and image guidance.

Fast and Accurate Rigid Registration of 3D CT Images by Combining Feature and Intensity

Computed tomography (CT) images are widely used for the analysis of the temporal evaluation or monitoring of the progression of a disease. The follow-up examinations of CT scan images of the same patient require a 3D registration technique. In this paper, an automatic and robust registration is proposed for the rigid registration of 3D CT images. The proposed method involves two steps. Firstly, the two CT volumes are aligned based on their principal axes, and then, the alignment from the previous step is refined by the optimization of the similarity score of the image’s voxel. Normalized cross correlation (NCC) is used as a similarity metric and a downhill simplex method is employed to find out the optimal score. The performance of the algorithm is evaluated on phantom images and knee synthetic CT images. By the extraction of the initial transformation parameters with principal axis of the binary volumes, the searching space to find out the parameters is reduced in the optimization step. Thus, the overall registration time is algorithmically decreased without the deterioration of the accuracy. The preliminary experimental results of the study demonstrate that the proposed method can be applied to rigid registration problems of real patient images.

A Study on the Feasibility of Active Contours on Automatic CT Bone Segmentation

Automatic bone segmentation of computed tomography (CT) images is an important step in image-guided surgery that requires both high accuracy and minimal user interaction. Previous attempts include global thresholding, region growing, region competition, watershed segmentation, and parametric active contour (AC) approaches, but none claim fully satisfactory performance. Recently, geometric or level-set-based AC models have been developed and appear to have characteristics suitable for automatic bone segmentation such as initialization insensitivity and topology adaptability. In this study, we have tested the feasibility of five level-set-based AC approaches for automatic CT bone segmentation with both synthetic and real CT images: namely, the geometric AC, geodesic AC, gradient vector flow fast geometric AC, Chan-Vese (CV) AC, and our proposed density distance augmented CV AC (Aug. CV AC). Qualitative and quantitative evaluations have been made in comparison with the segmentation results from standard commercial software and a medical expert. The first three models showed their robustness to various image contrasts, but their performances decreased much when noise level increased. On the contrary, the CV AC's performance was more robust to noise, yet dependent on image contrast. On the other hand, the Aug. CV AC demonstrated its robustness to both noise and contrast levels and yielded improved performances on a set of real CT data compared with the commercial software, proving its suitability for automatic bone segmentation from CT images.