Mitigating motion artifacts in FDK based 3D Cone-beam Brain Imaging System using markers

Bhowmik, Ujjal Kumar; Iqbal, M. Zafar; Adhami, Reza R.

doi:10.2478/s13531-012-0011-7

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…To ensure we are going through the process correctly, we used the TomoPhantom software package [6], to determine what the best similarity measure might be in our case. the simulator environment utilizes the Shepp-Logan phantom [7] to test similarity measures.…”

Section: Methodsmentioning

confidence: 99%

“…The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. SSIM is used for measuring the similarity between two images7 Peak signal-to-noise ratio (PSNR) is the ratio between the maximum possible power of an image and the power of corrupting noise that affects the quality of its representation. To estimate the PSNR of an image, it is necessary to compare that image to an ideal clean image with the maximum possible power 8.…”

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confidence: 99%

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Geometrical Self-Calibration of CBCT Systems

Saedpanah¹,

Hosseini²,

Hosseini³

et al. 2023

eJNDT

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In this paper, we intend to provide a method for calibration of geometric CBCT systems without the need for phantom and prior knowledge related to the object which is normally used in 2D-3D registration methods for geometrical calibration. To this end, we use an iterative algorithm based on comparison between measured data and DRRs1 which are obtained from reconstructed images with different geometries to find geometrical parameters of the system. Our proposed method shows strong agreement with phantom based methods.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Geometrical Self-Calibration of CBCT Systems

Saedpanah¹,

Hosseini²,

Hosseini³

et al. 2023

eJNDT

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“…The first as shown in Fig. 4 is generated by the 3D Shepp-Logan phantom [44], and the second as shown in Fig. 5 is a insect leg, both of which have the same dimension of Q = Q x ×Q y ×Q z = 32×32×4 = 4096 voxels.…”

Section: Simulation and Analysismentioning

confidence: 99%

Conveyor X-Ray Tomosynthesis Imaging With Optimized Structured Sequential Illumination

Wang

Cuadros

et al. 2020

IEEE Photonics J.

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Compressive X-ray tomosynthesis is a computational imaging technique used to reconstruct three-dimensional objects from a set of projection measurements, where the masks are used to modulate the structured illumination to reduce the radiation dose while retaining the reconstruction performance. This paper proposes a conveyor X-ray tomosynthesis imaging method with optimized structured sequential illumination. Variations of this geometry where the object is static but the measurement gantry is dynamic are possible within the proposed framework. In this system, several X-ray sources are successively used to interrogate the moving object lying on a conveyor, where the compressive measurements are received by a set of low-cost strip detectors. The dynamic imaging model and reconstruction framework of the proposed system are established taking into account sensing geometry along with the movement of the object. Subsequently, a genetic algorithm is developed to optimize the exposure sequence of X-ray sources and mask patterns and during the dynamic measurement process. The optimization problem is formulated based on the restricted isometry property of compressive sensing theory to ameliorate the ill-posed inverse tomosynthesis problem. The optimized structured sequential illumination is proved to significantly improve the imaging performance of the conveyor X-ray tomosynthesis system based on a set of simulations.

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“…We note that the number of slices, slice spacing, and voxel resolution of different CT images are generally different, which can cause big diversities of CT images. Moreover, 3D CT images are susceptible to a number of artifacts, such as patient movement [5], representation method [6] and radiation dose [7]. To achieve the best performance, we focus on the face area in CT, which occupies the majority of CT images, especially in head and neck CT.…”

Section: Introductionmentioning

confidence: 99%

Human Recognition Using Face in Computed Tomography

Zhu¹,

Han²,

Zhou³

2020

Preprint

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With the mushrooming use of computed tomography (CT) images in clinical decision making, management of CT data becomes increasingly difficult. From the patient identification perspective, using the standard DICOM tag to track patient information is challenged by issues such as misspelling, lost file, site variation, etc. In this paper, we explore the feasibility of leveraging the faces in 3D CT images as biometric features. Specifically, we propose an automatic processing pipeline that first detects facial landmarks in 3D for ROI extraction and then generates aligned 2D depth images, which are used for automatic recognition. To boost the recognition performance, we employ transfer learning to reduce the data sparsity issue and to introduce a group sampling strategy to increase inter-class discrimination when training the recognition network. Our proposed method is capable of capturing underlying identity characteristics in medical images while reducing memory consumption. To test its effectiveness, we curate 600 3D CT images of 280 patients from multiple sources for performance evaluation. Experimental results demonstrate that our method achieves a 1:56 identification accuracy of 92.53% and a 1:1 verification accuracy of 96.12%, outperforming other competing approaches.

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Mitigating motion artifacts in FDK based 3D Cone-beam Brain Imaging System using markers

Cited by 8 publications

References 14 publications

Geometrical Self-Calibration of CBCT Systems

Geometrical Self-Calibration of CBCT Systems

Conveyor X-Ray Tomosynthesis Imaging With Optimized Structured Sequential Illumination

Human Recognition Using Face in Computed Tomography

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