Quantitative metabolic characteristics in the peritumoral region of gliomas at 7T

Yan, Gen; Yi, Meizhi; Li, Shengkai; Yang, Lin; Dai, Zhuozhi; Xuan, Yali; Wu, Renhua

doi:10.3233/thc-218048

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Chemical exchange saturation transfer (CEST)-MRI has been widely used in metabolite evaluation because of its sensitivity to the exchange process between molecules with exchangeable protons and bulk water protons. [1][2][3][4][5] Although CEST-MRI is powerful and versatile in the detection and research of tumors [6][7][8] and in renal [9][10][11] and cardiac imaging, 12 the CEST effect is limited by experimental conditions, such as the amplitude 13,14 and duration of RF saturation. 15,16 In fact, there are two competing processes in the CEST effect: signal reduction due to saturation transfer from irradiated labile protons and signal recovery through relaxation.…”

Section: Introductionmentioning

confidence: 99%

Deep learning to reconstruct quasi‐steady‐state chemical exchange saturation transfer from a non‐steady‐state experiment

Xiao

Zhang

Yang³

et al. 2023

NMR in Biomedicine

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The insufficiently long RF saturation duration and relaxation delay in chemical exchange saturation transfer (CEST)‐MRI experiments may result in underestimation of CEST measurements. To maintain the CEST effect without prolonging the saturation duration and reach quasi‐steady state (QUASS), a deep learning method was developed to reconstruct a QUASS CEST image pixel by pixel from non‐steady‐state CEST acquired in experiments. In this work, we established a tumor‐bearing rat model on a 7 T horizontal bore small‐animal MRI scanner, allowing ground‐truth generation, after which a bidirectional long short‐term memory network was formulated and trained on simulated CEST Z‐spectra to reconstruct the QUASS CEST; finally, the ground truth yielded by experiments was used to evaluate the performance of the reconstruction model by comparing the estimates with the ground truth. For quantitation evaluation, linear regression analysis, structural similarity index (SSIM) and peak signal‐to‐noise ratio (peak SNR) were used to assess the proposed model in the QUASS CEST reconstruction. In the linear regression analysis of in vivo data, the coefficient of determination for six different representative frequency offsets was at least R2 = 0.9521. Using the SSIM and peak SNR as evaluation metrics, the reconstruction accuracies of in vivo QUASS CEST were found to be 0.9991 and 46.7076, respectively. Experimental results demonstrate that the proposed model provides a robust and accurate solution for QUASS CEST reconstruction using a deep learning mechanism.

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Section: Introductionmentioning

confidence: 99%

Deep learning to reconstruct quasi‐steady‐state chemical exchange saturation transfer from a non‐steady‐state experiment

Xiao

Zhang

Yang³

et al. 2023

NMR in Biomedicine

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In vivo C6 glioma models: an update and a guide toward a more effective preclinical evaluation of potential anti-glioblastoma drugs

Pournajaf,

Afsordeh,

Pourgholami

2023

Reviews in the Neurosciences

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Glioblastoma multiform (GBM) is the most common primary brain tumor with a poor prognosis and few therapeutic choices. In vivo, tumor models are useful for enhancing knowledge of underlying GBM pathology and developing more effective therapies/agents at the preclinical level, as they recapitulate human brain tumors. The C6 glioma cell line has been one of the most widely used cell lines in neuro-oncology research as they produce tumors that share the most similarities with human GBM regarding genetic, invasion, and expansion profiles and characteristics. This review provides an overview of the distinctive features and the different animal models produced by the C6 cell line. We also highlight specific applications of various C6 in vivo models according to the purpose of the study and offer some technical notes for more convenient/repeatable modeling. This work also includes novel findings discovered in our laboratory, which would further enhance the feasibility of the model in preclinical GBM investigations.

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Magnetic resonance imaging analysis of embryonal rhabdomyosarcoma of the prostate

Wang¹,

Zhang²,

Wang³

2023

THC

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BACKGROUND: Rhabdomyosarcoma (RMS) is a highly malignant tumor that originates from myogenic progenitor cells. OBJECTIVE: To investigate the magnetic resonance imaging (MRI) characteristics of prostate embryonal rhabdomyosarcoma (ERMS). METHODS: We retrospectively analyzed the clinical and MRI imaging data of 9 cases of prostate ERMS that were confirmed pathologically. The patients were aged between 14∼49 years with a median age of 27 years, and they all underwent MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). RESULTS: The MRI scan of the lesions showed an irregular shape, mixed signals, uneven equal/long T1 signal and an equal/long T2 signal, cystic necrosis in 9 cases and hemorrhage in 6 cases; DWI and ADC images showed a mixed high/low signal, and the tumor parenchyma showed ADC low signal, with an average ADC value of 0.666 × 10-3 mm2/s. There were 5 cases of DCE-MRI TIC type II and 4 cases of DCE-MRI TIC type I. The average value of Tpeak was 120 s and the average value of MCER was 172.3%. After the enhancement, the signal of tumor enhancement was uneven, and showed patchy and reticular enhancement, however, the cyst degeneration, necrosis area, and hemorrhage focus were not enhanced. There were 3 cases with multiple pelvic lymph nodes and 1 case with multiple bone metastases. CONCLUSION: The MRI manifestations of prostate ERMS have certain characteristics, and the combination of DWI and DCE-MRI are helpful in the diagnosis.

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Quantitative metabolic characteristics in the peritumoral region of gliomas at 7T

Cited by 3 publications

References 20 publications

Deep learning to reconstruct quasi‐steady‐state chemical exchange saturation transfer from a non‐steady‐state experiment

Deep learning to reconstruct quasi‐steady‐state chemical exchange saturation transfer from a non‐steady‐state experiment

In vivo C6 glioma models: an update and a guide toward a more effective preclinical evaluation of potential anti-glioblastoma drugs

Magnetic resonance imaging analysis of embryonal rhabdomyosarcoma of the prostate

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