Prior image based temporally constrained reconstruction algorithm for magnetic resonance guided high intensity focused ultrasound

Prakash, Jaya; Todd, Nick; Yalavarthy, Phaneendra K.

doi:10.1118/1.4934829

Cited by 3 publications

(2 citation statements)

References 33 publications

(53 reference statements)

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“…One of the commonly used algorithms in optimization of the regularized cost functions is the Alternate Direction Method of Multipliers 52,53,57,69,93,95,100,101,103 . Other techniques include the gradient descent algorithm, 72‐74 the Fast Composite Splitting Algorithm 61,82 and majorization–minimization‐based algorithms such as the fast iterative shrinkage‐thresholding algorithm 106 …”

Section: Resultsmentioning

confidence: 99%

“…Different transform operators 𝜙 and priors have been proposed to find a sparse representation of the images. This includes the wavelet transform, 27,[47][48][49][50][51][52] the total variation transform, 26,50,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] group sparsity where images are divided into multiple sparse regions, 69 weighted quadratic prior that aims to suppress the noise and reconstruction artifacts based on the intensity differences between neighboring voxels, 56 gradient across the contrast dimension, 53,[70][71][72][73][74] second-order discrete derivative in the contrast dimension, 75,76 principal component analysis-based transform, 75,[77][78][79][80] image ratio constraints, where the ratio between a low-resolution image and the reconstructed image is used as a constraint, 50 and learned sparsifying transform 𝜙 from the measurements. 81 Apart from these, alternative ways to use regularizers and transform domains have been proposed.…”

Section: Regularized Reconstructionmentioning

confidence: 99%

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Systematic review of reconstruction techniques for accelerated quantitative MRI

Shafieizargar

Byanju

Sijbers

et al. 2023

Magnetic Resonance in Med

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To systematically review the techniques that address undersampling artifacts in accelerated quantitative magnetic resonance imaging (qMRI). Methods: A literature search was conducted using the Embase, Medline, Web of Science Core Collection, Coherence Central Register of Controlled Trials, and Google Scholar databases for studies, published before July 2022 proposing reconstruction techniques for accelerated qMRI. Studies are reviewed according to inclusion criteria, and included studies are categorized based on the methodology used. Results: A total of 292 studies included in the review are categorized. A technical overview of each category is provided, and the categories are described in a unified mathematical framework. The distribution of the reviewed studies over time, application domain, and parameters of interest is illustrated. Conclusion: An increasing trend in the number of articles that propose new techniques for accelerated qMRI reconstruction indicates the importance of acceleration in qMRI. The techniques are mostly validated for relaxometry parameters and brain scans. The categories of techniques are compared based on theoretical grounds, highlighting existing trends and potential gaps in the field.

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Section: Resultsmentioning

confidence: 99%

Section: Regularized Reconstructionmentioning

confidence: 99%

Systematic review of reconstruction techniques for accelerated quantitative MRI

Shafieizargar

Byanju

Sijbers

et al. 2023

Magnetic Resonance in Med

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Temporal differences (TED) compressed sensing: a method for fast MRgHIFU temperature imaging

2020

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This work proposes the Temporal Differences (TED) Compressed Sensing (CS) method for accelerating thermal monitoring in MR‐guided High‐Intensity Focused Ultrasound (MRgHIFU) treatments. TED combines k‐space subsampling, parallel imaging, and a unique CS recovery of the temporal differences between pre‐heating and post‐heating multi‐coil data. TED was validated through retrospective experiments with (i) two phantom datasets acquired with 1.5 T and 3 T MRgHIFU systems from different vendors, (ii) data from an in vivo animal model experiment, and (iii) four datasets from clinical in vivo MRgHIFU treatments of prostate cancer in humans. TED produced highly accurate temperature change maps from subsampled k‐space data for all datasets. For the clinical in vivo data, an analysis of 105 time frames showed that the average TED reconstruction error is 1.06‐1.67 °C. Furthermore, TED consistently outperforms two state‐of‐the‐art methods, l1‐SPIRiT and the K‐space Hybrid Method, and offers errors that are significantly lower, by 29% or more. Moreover, TED offers robust performance over a range of its tunable parameters, stability across MRgHIFU systems from different vendors, and a short runtime of 1.7 s. In summary, TED enables k‐space subsampling while retaining high‐temperature mapping accuracy.

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A Novel Method for Detecting Thickness Defects in Metal Components Based on Point Cloud and Model Registration

Li,

Zhou,

Wang

et al. 2024

Learning and Analytics in Intelligent Systems

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Prior image based temporally constrained reconstruction algorithm for magnetic resonance guided high intensity focused ultrasound

Abstract: The PITCR approach is able to perform more accurate reconstructions of temperature maps compared to the TCR approach with highly undersampled data in MR guided high intensity focused ultrasound.

Cited by 3 publications

References 33 publications

Systematic review of reconstruction techniques for accelerated quantitative MRI

Systematic review of reconstruction techniques for accelerated quantitative MRI

Temporal differences (TED) compressed sensing: a method for fast MRgHIFU temperature imaging

A Novel Method for Detecting Thickness Defects in Metal Components Based on Point Cloud and Model Registration

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