Improved Image Fusion in PET/CT Using Hybrid Image Reconstruction and Super‐Resolution

Kennedy, John A.; Israel, Ora; Frenkel, Alex; Bar‐Shalom, Rachel; Azhari, Haim

doi:10.1155/2007/46846

Cited by 42 publications

(27 citation statements)

References 21 publications

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“…For comparison purposes, Bicubic interpolation and the iterative back-projection (IBP) method [4] were evaluated. The IBP method was tested since it has been used by Greenspan et al [1] and Kennedy et al [2], [3] for medical image superresolution purposes. To evaluate the performance of the proposed method in a quantitative manner, the Peak-Signal-to-Noise Ratio (PSNR) was computed for the resolution enhanced images obtained with the tested algorithms, relative to the reference image used to generate the low resolution images.…”

Section: Resultsmentioning

confidence: 99%

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Phase-Adaptive Superresolution of Mammographic Images Using Complex Wavelets

Wong

Scharcanski

2009

IEEE Trans. on Image Process.

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Abstract-This paper describes a new superresolution approach for enhancing the resolution of mammographic images using complex wavelet frequency information. This method allows regions of interest of a mammographic image to be viewed in enhanced resolution while reducing the patient exposure to radiation. The proposed method exploits the structural characteristics of breast tissues being imaged, and produces higher resolution mammographic images with sufficient visual fidelity that fine image details can be discriminated more easily. In our approach, the superresolution problem is formulated as a constrained optimization problem using a third-order Markov prior model, and adapts the priors based on the phase variations of the low-resolution mammographic images. Experimental results indicate the proposed method is more effective at preserving the visual information when compared with existing resolution enhancement methods.

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Section: Resultsmentioning

confidence: 99%

“…Greenspan et al [1] and Kennedy et al [2], [3] utilized the iterative back-projection (IBP) method proposed by Irani et. al [4] to construct high resolution magnetic resonance (MR) and positron emission tomography (PET) images, respectively, from spatially-shifted images.…”

Section: Introductionmentioning

confidence: 99%

Phase-Adaptive Superresolution of Mammographic Images Using Complex Wavelets

Wong

Scharcanski

2009

IEEE Trans. on Image Process.

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“…Irani & Peleg [5]. The original procedure includes a method to estimate the movement between the different images corresponding to the same scene.…”

Section: B Softwarementioning

confidence: 99%

“…However, with this configuration the geometric efficiency of the detector and the FOV size are strongly affected by the source-tocollimator distance, which is a major drawback of pinhole collimation using small-area detectors. More recently, superresolution approaches have come up as an alternative to overcome the limits imposed by image sensors and therefore they can represent a low-cost solution to enhance spatial resolution in clinical nuclear medicine equipments [3], having already demonstrated satisfactory results in clinical PET imaging [4][5].…”

Section: Introduction Smentioning

confidence: 99%

A super-resolution feasibility study in small-animal SPECT imaging

Villena

Lage

Carlos

et al. 2008

2008 IEEE Nuclear Science Symposium Conference Record

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Abstract-Lack of spatial resolution is a major drawback in small-animal SPECT imaging, particularly when parallel hole collimators are employed. This work evaluates the feasibility of enhancing the spatial resolution of a small-animal SPECT scanner by applying an approach based on a super-resolution technique combined with dedicated hardware. The detectors are based on PS-PMTs (Hamamatsu, H8500), NaI(TI) crystal arrays and parallel hole collimators. The system enables the mechanical shifting of the gamma-cameras and the object under study during the acquisition in steps smaller than the detector intrinsic sampling pitch. This fact allows using a super-resolution technique without the necessity of blind movement estimators between projections. In the absence of noise the algorithm converged to the original image, and with real noisy data it achieved improvements in resolution between 28% (tomographic) and 42% (planar) in phantom studies.

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“…Image registration plays an important role in various biomedical applications, such as medical image superresolution [1], [2], medical image fusion [3], disease diagnosis [4], and computer-assisted surgery [5]. Of particular interest is multimodal image registration, where the underlying goal is to find the optimal geometric transformation that aligns images acquired, using different imaging modalities [e.g., magnetic resonance imaging (MRI)/computed tomography (CT)/positron emission tomography (PET)].…”

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confidence: 99%

An Adaptive Monte Carlo Approach to Phase-Based Multimodal Image Registration

Wong

2010

IEEE Trans. Inform. Technol. Biomed.

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Abstract-In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.

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Improved Image Fusion in PET/CT Using Hybrid Image Reconstruction and Super‐Resolution

Cited by 42 publications

References 21 publications

Phase-Adaptive Superresolution of Mammographic Images Using Complex Wavelets

Phase-Adaptive Superresolution of Mammographic Images Using Complex Wavelets

A super-resolution feasibility study in small-animal SPECT imaging

An Adaptive Monte Carlo Approach to Phase-Based Multimodal Image Registration

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