Accelerating CEST imaging using a model‐based deep neural network with synthetic training data

Xu, Jianping; Zu, Tao; Hsu, Yi‐Cheng; Wang, Xiaoli; Chan, Kannie W. Y.; Zhang, Yi

doi:10.1002/mrm.29889

Cited by 7 publications

(4 citation statements)

References 71 publications

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“…To address this issue, one approach is to include neighboring-offset images in the network input when reconstructing each center CEST source image. 47 The other potential reason could be the various degrees of improvement in spatial resolution of the CEST source images across different offset frequencies, as evidenced by the PSNR and SSIM values (Figures 4B, 5C and 6). To generate the Z-spectrum, a pixel intensity at different offset frequencies is normalized by the pixel's intensity at M 0 before plotting the pixel intensity values as a function of offset frequencies.…”

Section: The Influence Of the Pretraining Datasetmentioning

confidence: 99%

“…Moreover, with its adaptability to any acquisition sequence, non-requirement of additional hardware such as array receiver coils and its superior performance, DLSR has been adopted for a wide range of clinical scans, including brain, 43,44 musculoskeletal, 32 and cardiac MRI. 45 However, using DLSR for CEST MRI has only been explored in small-scale research studies 46,47 due to a lack of the large CEST datasets required for network development.…”

Section: Introductionmentioning

confidence: 99%

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Deep‐learning‐based super‐resolution for accelerating chemical exchange saturation transfer MRI

Pemmasani Prabakaran,

Park,

Lai

et al. 2024

NMR in Biomedicine

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Chemical exchange saturation transfer (CEST) MRI is a molecular imaging tool that provides physiological information about tissues, making it an invaluable tool for disease diagnosis and guided treatment. Its clinical application requires the acquisition of high‐resolution images capable of accurately identifying subtle regional changes in vivo, while simultaneously maintaining a high level of spectral resolution. However, the acquisition of such high‐resolution images is time consuming, presenting a challenge for practical implementation in clinical settings. Among several techniques that have been explored to reduce the acquisition time in MRI, deep‐learning‐based super‐resolution (DLSR) is a promising approach to address this problem due to its adaptability to any acquisition sequence and hardware. However, its translation to CEST MRI has been hindered by the lack of the large CEST datasets required for network development. Thus, we aim to develop a DLSR method, named DLSR‐CEST, to reduce the acquisition time for CEST MRI by reconstructing high‐resolution images from fast low‐resolution acquisitions. This is achieved by first pretraining the DLSR‐CEST on human brain T1w and T2w images to initialize the weights of the network and then training the network on very small human and mouse brain CEST datasets to fine‐tune the weights. Using the trained DLSR‐CEST network, the reconstructed CEST source images exhibited improved spatial resolution in both peak signal‐to‐noise ratio and structural similarity index measure metrics at all downsampling factors (2–8). Moreover, amide CEST and relayed nuclear Overhauser effect maps extrapolated from the DLSR‐CEST source images exhibited high spatial resolution and low normalized root mean square error, indicating a negligible loss in Z‐spectrum information. Therefore, our DLSR‐CEST demonstrated a robust reconstruction of high‐resolution CEST source images from fast low‐resolution acquisitions, thereby improving the spatial resolution and preserving most Z‐spectrum information.

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Section: The Influence Of the Pretraining Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep‐learning‐based super‐resolution for accelerating chemical exchange saturation transfer MRI

Pemmasani Prabakaran,

Park,

Lai

et al. 2024

NMR in Biomedicine

Self Cite

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“…These approaches may enable the acquisition of incomplete data with higher efficiency. Conventional iterative [29][30][31]34 (e.g., CS-SENSE, k−t Sparse SENSE) and deep-learning 41,42 based MRI acceleration approaches, have not fully utilized CEST-related priors in their reconstruction. Recently a k−ω ROSA approach 33 incorporates the symmetry and asymmetry properties of z-spectra and achieves 5× acceleration.…”

Section: F I G U R Ementioning

confidence: 99%

“…With recent advances in deep learning techniques for MRI reconstruction, [36][37][38][39][40] deep learning networks have been exploited to reconstruct de-aliased CEST images from under-sampled k-space data. 41,42 These networks have shown higher reconstruction accuracy compared to compressed sensing-based methods and/or parallel imaging. However, previous deep learning approaches for CEST acceleration have seldom incorporated CEST spectral priors directly into the network architecture.…”

Section: Introductionmentioning

confidence: 99%

Highly‐accelerated CEST MRI using frequency‐offset‐dependent k‐space sampling and deep‐learning reconstruction

Liu,

Li,

Chen

et al. 2024

Magnetic Resonance in Med

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PurposeTo develop a highly accelerated CEST Z‐spectral acquisition method using a specifically‐designed k‐space sampling pattern and corresponding deep‐learning‐based reconstruction.MethodsFor k‐space down‐sampling, a customized pattern was proposed for CEST, with the randomized probability following a frequency‐offset‐dependent (FOD) function in the direction of saturation offset. For reconstruction, the convolution network (CNN) was enhanced with a Partially Separable (PS) function to optimize the spatial domain and frequency domain separately. Retrospective experiments on a self‐acquired human brain dataset (13 healthy adults and 15 brain tumor patients) were conducted using k‐space resampling. The prospective performance was also assessed on six healthy subjects.ResultsIn retrospective experiments, the combination of FOD sampling and PS network (FOD + PSN) showed the best quantitative metrics for reconstruction, outperforming three other combinations of conventional sampling with varying density and a regular CNN (nMSE and SSIM, p < 0.001 for healthy subjects). Across all acceleration factors from 4 to 14, the FOD + PSN approach consistently outperformed the comparative methods in four contrast maps including MTRasym, MTRrex, as well as the Lorentzian Difference maps of amide and nuclear Overhauser effect (NOE). In the subspace replacement experiment, the error distribution demonstrated the denoising benefits achieved in the spatial subspace. Finally, our prospective results obtained from healthy adults and brain tumor patients (14×) exhibited the initial feasibility of our method, albeit with less accurate reconstruction than retrospective ones.ConclusionThe combination of FOD sampling and PSN reconstruction enabled highly accelerated CEST MRI acquisition, which may facilitate CEST metabolic MRI for brain tumor patients.

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Prospective acceleration of whole‐brain CEST imaging by golden‐angle view ordering in Cartesian coordinates and joint k‐space and image‐space parallel imaging (KIPI)

Zu,

Yong,

Dai

et al. 2024

Magnetic Resonance in Med

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PurposeTo prospectively accelerate whole‐brain CEST acquisition by joint k‐space and image‐space parallel imaging (KIPI) with a proposed golden‐angle view ordering technique (GAVOT) in Cartesian coordinates.Theory and MethodsThe T2‐decay effect will vary across frames with variable acceleration factors (AF) in the prospective acquisition using sequences with long echo trains. The GAVOT method uses a subset strategy to eliminate the T2‐decay inconsistency, where all frames use a subset of shots from the calibration frame to form their k‐space view ordering. The golden‐angle rule is adapted to ensure uniform k‐space coverage for arbitrary AFs in Cartesian coordinates. Phantom and in vivo studies were conducted on a 3 T scanner.ResultsThe GAVOT view ordering yielded a higher g‐factor than conventional uniformly centric ordering, whereas the noise propagation in amide proton transfer (APT) weighted images was similar between different view ordering. Compared to centric ordering, GAVOT successfully eliminated the T2‐decay inconsistency across all frames, resulting in fewer image artifacts for both KIPI and conventional parallel imaging methods. The synergy of GAVOT and KIPI mitigated strong aliasing artifacts and achieved high‐quality reconstruction of prospective variable‐AF datasets. GAVOT‐KIPI reduced the scan time to 2.1 min for whole‐brain APT weighted imaging and 4.7 min for quantitative APT signal (APT#) mapping.ConclusionGAVOT makes the prospective variable AF strategy flexible and practical, and, in conjunction with KIPI, ensures high‐quality reconstruction from highly undersampled data, facilitating the clinical translation of whole‐brain CEST imaging.

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Accelerating CEST imaging using a model‐based deep neural network with synthetic training data

Cited by 7 publications

References 71 publications

Deep‐learning‐based super‐resolution for accelerating chemical exchange saturation transfer MRI

Deep‐learning‐based super‐resolution for accelerating chemical exchange saturation transfer MRI

Highly‐accelerated CEST MRI using frequency‐offset‐dependent k‐space sampling and deep‐learning reconstruction

Prospective acceleration of whole‐brain CEST imaging by golden‐angle view ordering in Cartesian coordinates and joint k‐space and image‐space parallel imaging (KIPI)

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