Noise reduction in diffusion weighted MRI of the pancreas using an L1-regularized iterative SENSE reconstruction

Kamal, Omar; McTavish, Sean; Harder, F; Van, Anh T.; Peeters, Johannes M.; Weiss, Kilian; Makowski, Marcus R.; Karampinos, Dimitrios C.; Braren, Rickmer

doi:10.1016/j.mri.2021.11.009

Cited by 9 publications

(11 citation statements)

References 17 publications

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“…However, in the current study a quantitative SNR improvement in volunteer scans was not reflected by qualitative scores in patient exams, where no improvement was shown for L1-DWI in image noise and artifacts rating. While there may be various factors contributing to this discrepancy, including local ROI selection vs. global visual inspection, body size and composition variabilities in the volunteer vs. patient cohorts, etc., detailed investigations are needed to study its impact in image quality and, particularly, delineation of normal appearing tissues and lesions 28 .…”

Section: Discussionmentioning

confidence: 99%

“…liver parenchyma, appears smoother and less noisy via CS denoising, the visibility of small FLLs may become less apparent, which can no longer be retrieved by the sparsifying constraint in the CSbased reconstruction with L1-regularized SENSE including wavelet-based denoising, even after adding multiple measurements or acquisitions (see Figure, Supplemental Digital Content 2, which shows a numeric simulation of the effect of CS-based reconstruction with wavelet-based denoising in small lesion detection). In fact, although in line with most CS reconstruction techniques in clinical applications 9,27,28 , the same equidistant k-space acquisition pattern as in conventional single-shot EPI was used here for CS-based reconstruction, instead of a specifically tailored incoherent sampling pattern 3,6,7 . This might also limit the denoising performance.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to that, scan parameters of both sequences were matched as closely as possible, with shortest possible repetition times and same number of slices in the interleaved order in each case. For L1-DWI reconstruction, a regularized, iterative L1-norm minimization was applied assuring image sparsity in the wavelet domain and data consistency, as in typical CS reconstruction by taking the SENSE coil information into account 20,21,26,28 . Standard denoising was applied (Supplemental Digital Content 2).…”

Section: Mr Imagingmentioning

confidence: 99%

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Liver diffusion-weighted MR imaging with L1-regularized iterative sensitivity encoding reconstruction based on single-shot echo-planar imaging: initial clinical experience

Bode

Zhang²,

Terwolbeck³

et al. 2022

Sci Rep

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To investigate whether combining L1-regularized iterative sensitivity encoding (SENSE) reconstruction and single-shot echo planar imaging (EPI) is useful in hepatic DWI. Single-shot EPI-DWI with L1-regularized iterative SENSE reconstruction (L1-DWI) and conventional parallel imaging-based reconstruction (conv-DWI) in liver MRI were compared in volunteers and patients. For the patient cohort, 75 subjects (60 ± 13 years) with 349 focal liver lesions (FLL) were included. Patient groups A and B were used to reduce acquisition time or improve spatial resolution, respectively. Image parameters were rated on a 5-point scale. The number of FLLs was recorded; in case of discrepancy, the reason for non-detectability was analyzed. In volunteers, higher signal-to-noise ratio (24.4 ± 5.6 vs. 12.2 ± 2.3, p < 0.001 at b = 0; 19.3 ± 2.8 vs. 9.8 ± 1.6, p < 0.001 at b = 800) and lower standard deviation of the apparent diffusion coefficient-values (0.17 vs. 0.20 mm2/s, p < 0.05) were found on L1-DWI compared to conv-DWI. In patients, image ratings were similar for all parameters except for “conspicuity of FLLs” which was rated significantly lower on L1-DWI vs. conv-DWI (4.7 ± 0.6 vs. 4.2 ± 0.9, p < 0.05) in group A. In five patients, 11/349 FLLs were not detectable on L1-DWI, but on conv-DWI. L1-regularized iterative reconstruction of single-shot EPI DWI can accelerate image acquisition or improve spatial resolution. However, our finding that FLLs were non-detectable on L1-DWI warrants further research.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Mr Imagingmentioning

confidence: 99%

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Liver diffusion-weighted MR imaging with L1-regularized iterative sensitivity encoding reconstruction based on single-shot echo-planar imaging: initial clinical experience

Bode

Zhang²,

Terwolbeck³

et al. 2022

Sci Rep

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“…However, although regular sampling may not be ideal, it has been shown that the integration of PI and L1-regularized denoising is beneficial also in this case. [79][80][81][82][83][84] Compressed SENSE, the integration of SENSE and L1-regularized denoising in an iterative reconstruction loop, is normally used with random sampling. Recently, however, it has also been applied to single-shot EPI (EPI with compressed SENSE [EPICS]).…”

Section: Technical Advancements In Abdominal Dwimentioning

confidence: 99%

Technical Advancements in Abdominal Diffusion-weighted Imaging

Obara¹,

Kwon²,

Yoneyama³

et al. 2023

MRMS

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Since its first observation in the 18th century, the diffusion phenomenon has been actively studied by many researchers. Diffusion-weighted imaging (DWI) is a technique to probe the diffusion of water molecules and create a MR image with contrast based on the local diffusion properties. The DWI pixel intensity is modulated by the hindrance the diffusing water molecules experience. This hindrance is caused by structures in the tissue and reflects the state of the tissue. This characteristic makes DWI a unique and effective tool to gain more insight into the tissue's pathophysiological condition. In the past decades, DWI has made dramatic technical progress, leading to greater acceptance in clinical practice. In the abdominal region, however, acquiring DWI with good quality is challenging because of several reasons, such as large imaging volume, respiratory and other types of motion, and difficulty in achieving homogeneous fat suppression. In this review, we discuss technical advancements from the past decades that help mitigate these problems common in abdominal imaging. We describe the use of scan acceleration techniques such as parallel imaging and compressed sensing to reduce image distortion in echo planar imaging. Then we compare techniques developed to mitigate issues due to respiratory motion, such as free-breathing, respiratory-triggering, and navigator-based approaches. Commonly used fat suppression techniques are also introduced, and their effectiveness is discussed. Additionally, the influence of the abovementioned techniques on image quality is demonstrated. Finally, we discuss the current and future clinical applications of abdominal DWI, such as whole-body DWI, simultaneous multiple-slice excitation, intravoxel incoherent motion, and the use of artificial intelligence. Abdominal DWI has the potential to develop further in the future, thanks to scan acceleration and image quality improvement driven by technological advancements. The accumulation of clinical proof will further drive clinical acceptance.

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“…Magnetic resonance imaging (MRI) is a widely applied noninvasive modality for medical diagnosis as it provides high-quality images and good soft tissue contrast. But one limitation of MRI is its long scan time, which results in significant artifacts in the images due to physiological motion and movements of patient during the prolonged scan process [1,2]. Compressed sensing (CS) [3][4][5] has shown its potential to shorten MRI scan time while producing images adequate for diagnosis.…”

Section: Introductionmentioning

confidence: 99%

MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform

Zhao

2022

International Journal of Biomedical Imaging

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The methods of compressed sensing magnetic resonance imaging (CS-MRI) can be divided into two categories roughly based on the number of target variables. One group devotes to estimating the complex-valued MRI image. And the other calculates the magnitude and phase parts of the complex-valued MRI image, respectively, by enforcing separate penalties on them. We propose a new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI. Owing to the separate regularization frame, this method reduces the impact of the phase jumps (that means the jumps or discontinuities of phase values) on magnitude reconstruction. Moreover, by virtue of the excellent features of DT CWT, such as nonoscillating envelope of coefficients and multidirectional selectivity, the proposed method is capable of capturing more details in the magnitude and phase images. The experimental results show that the proposed method recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.

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Noise reduction in diffusion weighted MRI of the pancreas using an L1-regularized iterative SENSE reconstruction

Cited by 9 publications

References 17 publications

Liver diffusion-weighted MR imaging with L1-regularized iterative sensitivity encoding reconstruction based on single-shot echo-planar imaging: initial clinical experience

Liver diffusion-weighted MR imaging with L1-regularized iterative sensitivity encoding reconstruction based on single-shot echo-planar imaging: initial clinical experience

Technical Advancements in Abdominal Diffusion-weighted Imaging

MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform

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