Superresolution reconstruction through object modeling and parameter estimation

Haacke, E. Mark; Liang, Zhi Pei; Izen, Steven H.

doi:10.1109/29.17545

Cited by 47 publications

(32 citation statements)

References 7 publications

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“…In the presence of present gradient constraints, reduction in TE may be achieved with asymmetric data sampling which may result in increased Gibb's artifact. However, this problem may be amenable to alternative reconstruction techniques such as half-Fourier or constrained reconstruction ( 11 ).…”

Section: Discussionmentioning

confidence: 99%

Carotid‐CNS MR flow imaging

Masaryk¹,

Laub²,

Modic³

et al. 1990

Magnetic Resonance in Med

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The authors present their 1-year experience with the use of 3DFT, time-of-flight MR angiography for the evaluation of vascular diseases of the head and neck. Their experience with over 150 patients indicates that this examination may be performed in conjunction with standard spin-echo imaging with only a minimal increase in patient examination time. This combined examination is most applicable to atherosclerotic disease of the carotid bifurcation, arterial occlusions of the primary and secondary branches of the intracranial circulation (particularly in pediatric patients such as those following ECMO or with sickle cell anemia), and patients with saccular berry aneurysms. This type of static, angiographic technique adds little to standard spin-echo imaging in patients with arteriovenous fistulae, neoplasms, and giant intracranial aneurysms. Limitations of the present technique include the inability to visualize slow flow lesions (e.g., giant aneurysms) and selected high flow states (arteriovenous fistulae, some severe stenoses).

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Section: Discussionmentioning

confidence: 99%

Carotid‐CNS MR flow imaging

Masaryk¹,

Laub²,

Modic³

et al. 1990

Magnetic Resonance in Med

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“…Such a transformed image can be obtained by applying a linear high pass filter. Equivalently, representation of the low-resolution image as a series of boxcar functions [17], leads to the Fourier Transform (FT) of the spatial derivatives modeled as a summation of complex sinusoids in noise. This framework entails the application of linear prediction for extrapolation of partial -space samples weighted using the appropriate frequency terms [18,19], referred to as the frequency-weighted -space.…”

Section: Model-based Methods For Partial -Space Fillingmentioning

confidence: 99%

“…For ease of representation, the region of positive and negative phase-encodes is denoted using + ( , ) for ≥ 0, and − ( , ) for ≤ 0. Using the boxcar representation [17], the spatial derivative of the image in the -directioñ( , ) = ( , )/ can be represented using the weighted summation of discrete impulses as a function of . Hence, the 2D-FT of the spatial derivative can be modelled as a summation of finite number of sinusoids, which is linear-predictable to a certain order [18,19].…”

Section: Formulation Of -Space As a Signal-space Modelmentioning

confidence: 99%

Application of Linear Prediction for Phase and Magnitude Correction in Partially Acquired MRI

Paul

Pillai

2013

ISRN Biomedical Imaging

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Using the boxcar representation in the spatial domain and a signal-space representation of its frequency-weighted -space, an iterative prediction method is developed to derive an improved low-resolution phase approximation for phase correction. Compared to the homodyne filter, the proposed predictor is found to be more efficient due to its capability of exhibiting an equivalent degree of performance using a lower number of fractional lines. The phase correction performance is illustrated using partially acquired susceptibility weighted images (SWI). An extension of the predictor into higher frequency regions of phaseencodes in conjunction with a signal-space projection in the frequency-weighted partial k-space is shown to provide restoration of fine structural details of sparse magnitude images. The application of subspace projection filtering is demonstrated using magnetic resonance angiogram (MRA).

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“…Linear prediction and data modeling methods have been used by several people to achieve isuper resolution in MR image reconstruction [5,6,7]. In linear prediction the choice of the [prediction order is often important and prediction equations may be ill-conditioned.…”

mentioning

confidence: 99%

“…In 1 [6] it is shown that direct application of linear prediction does not improve the resolutiori {of the image and that a multiplication of the echo signal by the time variable is needed to make linear prediction more effective. In [7] a similar modification of the signal is also proposed and linear prediction coefficients are used to estimate image parameters. The multiplication by the time variable is equivalent to taking the derivative of the signal and thus may enhance the noise.…”

mentioning

confidence: 99%

MR image resolution enhancement using a multi-layer neural network

Yan

Mao²,

Chen³

[1992] Proceedings Fifth Annual IEEE Symposium on Computer-Based Medical Systems

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F L 326102. m d Childrens Hospital. Los Angeles. CA 900543 onance (MR) imaging method, one echo signal corresponding to one line of the Fourier space data can be obtained in each cycle of the imaging sequence. The data on the line are sampled in t.he read out magnetic field gradient direction, which is related to one spatial frequency variable. Each echo signal is encoded by the phase encoding field gradieiit,, Abstract A magnetic resonance image m a y contain, truncation artifacts if there are not enough high frequency data when the conventional Fourier transform method is used for reconstructzon. I n this paper we propose a method f o r reducing the G Ttifacts using a multi-layer neural network. The network consists of one h e a r output layer and at least one non-linear hidden layer. I n this method the missing high frequency components are predicted based o n known low frequency components and are used to improve the resolution of the image. T h e method is tested with simulated data with good results. 1 624 0-8186-2742-5/92 $3.00 0 1992 IEEE

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Superresolution reconstruction through object modeling and parameter estimation

Cited by 47 publications

References 7 publications

Carotid‐CNS MR flow imaging

Carotid‐CNS MR flow imaging

Application of Linear Prediction for Phase and Magnitude Correction in Partially Acquired MRI

MR image resolution enhancement using a multi-layer neural network

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