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Chemical Exchange Saturation Transfer (CEST) is a technique that uses specific off-resonance saturation pulses to pre-saturate targeted substances. This process influences the signal intensity of free water, thereby indirectly providing information about the pre-saturated substance. Among the clinical applications of CEST, Amide Proton Transfer (APT) is currently the most well-established. APT can be utilized for the preoperative grading of gliomas. Tumors with higher APTw signals generally indicate a higher likelihood of malignancy. In predicting preoperative molecular typing, APTw values are typically lower in tumors with favorable molecular phenotypes, such as isocitrate dehydrogenase (IDH) mutations, compared to IDH wild-type tumors. For differential diagnosis, the average APTw values of meningiomas are significantly lower than those of high-grade gliomas. Various APTw measurement indices assist in distinguishing central nervous system lesions with similar imaging features, such as progressive multifocal leukoencephalopathy, central nervous system lymphoma, solitary brain metastases, and glioblastoma. Regarding prognosis, APT effectively differentiates between tumor recurrence and treatment effects, and also possesses predictive capabilities for overall survival (OS) and progression-free survival (PFS).

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The quantitative analysis of pulsed-chemical exchange saturation transfer (CEST) using a full model-based method is computationally challenging, as it involves dealing with varying RF values in pulsed saturation. A power equivalent continuous approximation of B1 power was usually applied to accelerate the analysis. In line with recent consensus recommendations from the CEST community for pulsed-CEST at 3T, particularly recommending a high RF saturation power (B1 = 2.0 µT) for the clinical application in brain tumors, this technical note investigated the feasibility of using average power (AP) as the continuous approximation. The simulated results revealed excellent performance of the AP continuous approximation in low saturation power scenarios, but discrepancies were observed in the z-spectra for the high saturation power cases. Cautions should be taken, or it may lead to inaccurate fitted parameters, and the difference can be more than 10% in the high saturation power cases.

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Compressed sensing (CS) is a novel technique for MRI acceleration. The purpose of this paper was to assess the effects of CS on the radiomic features extracted from amide proton transfer-weighted (APTw) images. Brain tumor MRI data of 40 scans were studied. Standard images using sensitivity encoding (SENSE) with an acceleration factor (AF) of 2 were used as the gold standard, and APTw images using SENSE with CS (CS-SENSE) with an AF of 4 were assessed. Regions of interest (ROIs), including normal tissue, edema, liquefactive necrosis, and tumor, were manually drawn, and the effects of CS-SENSE on radiomics were assessed for each ROI category. An intraclass correlation coefficient (ICC) was first calculated for each feature extracted from APTw images with SENSE and CS-SENSE for all ROIs. Different filters were applied to the original images, and the effects of these filters on the ICCs were further compared between APTw images with SENSE and CS-SENSE. Feature deviations were also provided for a more comprehensive evaluation of the effects of CS-SENSE on radiomic features. The ROI-based comparison showed that most radiomic features extracted from CS-SENSE-APTw images and SENSE-APTw images had moderate or greater reliabilities (ICC ≥ 0.5) for all four ROIs and all eight image sets with different filters. Tumor showed significantly higher ICCs than normal tissue, edema, and liquefactive necrosis. Compared to the original images, filters (such as Exponential or Square) may improve the reliability of radiomic features extracted from CS-SENSE-APTw and SENSE-APTw images.

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Background: Parametrial infiltration (PMI) is an important indicator for staging and treatment of cervical cancer (CC). The potential of amide proton transfer-weighted (APTw) parameters of peritumor tissue in predicting PMI is still uncertain. This study aims to explore whether the APTw parameters of peritumor tissue can improve diagnostic value of diffusion-weighted imaging (DWI) in magnetic resonance imaging (MRI). Methods: Eighty-one patients with pathologic analysis-confirmed CC were enrolled in this retrospective study. All patients underwent APTw MRI and DWI. The APTw values of tumor (APTw-t), APTw values in peritumor tissues (APTw-p) and apparent diffusion coefficient (ADC) values were independently reviewed by two radiologists to map the regions of interest and measure the corresponding values. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of these quantitative parameters. Results: The study patients were divided into the PMI group (n=22) and non-PMI group (n=59). The APTw-t and APTw-p values (%) of PMI group were higher than those of the non-PMI group [3.71 (interquartile range, IQR, 3.60-3.98) and 2.75 (IQR, 2.68-2.77) vs. 3.33 (IQR, 3.24-3.60) and 1.98 (IQR, 1.82-2.36); P<0.001]. The ADC values of PMI group were lower than those of non-PMI group [0.88 (IQR, 0.83-0.94) ×10-3 vs. 0.95 (IQR, 0.88-1.04)×10-3 mm2/sec; P<0.001]. The area under the curve (AUC) of APTw-t, APTw-p and ADC value for PMI diagnosis were 0.810, 0.831 and 0.806 respectively. In addition, the AUC value (0.918) of APTw-p + ADC was optimal, with a sensitivity and specificity of 91.20% and 87.20% respectively. Conclusions: APTw in peritumor tissues, combined with ADC value can be used to efficiently distinguish PMI of CC.

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BACKGROUND: Preoperative prediction of lymphovascular space invasion (LVSI) is crucial for improving the prognosis of patients with cervical cancer. PURPOSE: To evaluate the value of preoperative amide proton transfer (APT) imaging combined with serum CA125 levels for predicting LVSI in cervical cancer. MATERIAL AND METHODS: This retrospective study included 80 patients with cervical cancer who underwent preoperative magnetic resonance imaging, including APT imaging. Serum CA125 levels were measured using a fully automated immunoassay analyzer and chemiluminescence method. The presence of LVSI was determined based on the pathological results after surgery. RESULTS: Among the 40 patients who met the requirements, 29 had postoperative pathological confirmation of LVSI, while 11 did not. The areas under the receiver operating characteristic curves (AUC) of preoperative APT and CA125 levels predicting LVSI were 0.889 and 0.687, respectively. When the APT value was 2.9%, the corresponding Youden index was the highest (0.702), with a sensitivity of 79.3% and specificity of 90.9%. When the critical value of the preoperative serum CA15 level was 25.3 u/mL, the corresponding Youden index was the highest (0.508), with a sensitivity of 69.0% and a specificity of 81.8%. The sensitivity and specificity of preoperative APT imaging combined with serum CA125 in predicting LVSI were 82.7% and 100%, respectively, with a Youden's index of 0.828 and an AUC of 0.923. CONCLUSION: The combination of preoperative APT imaging and serum CA125 levels is valuable for predicting LVSI in cervical cancer. Diagnostic efficacy is highest when the APT value is >2.9% and the serum CA125 level is >25.3 u/mL.

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BACKGROUND: Brown adipose tissue (BAT) is metabolically activatable and plays an important role in obesity and metabolic diseases. With reduced fat-water-fraction (FWF) compared with white adipose tissue (WAT), BAT mass and its functional activation may be quantified with Z-spectra MRI, with built-in FWF and the metabolic amide proton transfer (APT) contrasts. PURPOSE: To investigate if Z-spectral MRI can quantify the mass and metabolic activity of adipose tissues. STUDY TYPE: Prospective. SUBJECTS: Seven groups of 8-week-old male rats, including two groups (n = 7 per group) for in vivo MRI study and five groups (n = 5 per group) for ex vivo validation; 12 young and healthy volunteers with 6 male and 6 female. FIELD STRENGTH/SEQUENCE: The 7 T small animal and 3 T clinical systems, T2-weighted imaging, Rapid Acquisition with Relaxation Enhancement (RARE) readout based chemical exchange saturation transfer (CEST) Z-spectral MRI sequence. ASSESSMENT: Quantified FWF and APT from Z-spectra in rats before and after norepinephrine (NE) stimulation and in healthy human subjects; ex vivo measurements of total proteins in BAT from rats. STATISTICAL TESTS: Two-tailed unpaired Student's t-tests and repeated measures ANOVA. P-value <0.05 was considered significant. RESULTS: Decreased FWF (from 39.6% ± 7.2% before NE injection to 16.4% ± 7.2% 120 minutes after NE injection, P < 0.0001) and elevated APT (from 1.1% ± 0.5% before NE injection to 2.9% ± 0.5% 120 minutes after NE injection, P < 0.0001) signals in BAT were observed with in vivo Z-spectral MRI in rats injected with NE at 7 T MRI. At clinical 3 T, Z-spectral MRI was used to quantify the FWF (58.5% ± 7.2% in BAT and 73.7% ± 6.5% in WAT with P < 0.0001) and APT (2.6% ± 0.8% in BAT and 0.9% ± 0.3% in WAT with P < 0.0001) signals in healthy volunteers. APT signals of BAT were negatively correlated with the BMI in humans (r = 0.71). DATA CONCLUSION: Endogenous Z-spectral MRI was demonstrated to simultaneously quantify BAT mass and function based on its FWF and APT contrasts. TECHNICAL EFFICACY STAGE: 1.

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PURPOSE: To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods. METHOD: The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of one-dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis-processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric. RESULTS: In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings. CONCLUSION: The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared with other methods.

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We report the MRI findings of a patient with an atypical meningioma who presented with spontaneous infarction. A 67-year-old man with histories of recurrent meningioma complained of left ocular protrusion and a subsequent biopsy revealed atypical meningioma. Contrast-enhanced CT showed a uniformly enhancing tumour in the left ethmoid sinus, but MRI 2 days later showed no enhancement on Gd-T1WI and severe diffusion restriction on DWI, indicating spontaneous infarction. APT-CEST imaging showed slight hypointensity in comparison to the normal brain with a mean MTR asymmetry value of 0.48%. Tumour regrowth was confirmed on MRI after 2 months. The recurrent tumour showed moderate diffusion restriction on DWI and hyperintensity with a mean MTR asymmetry value of 2.59% on APT-CEST imaging. The decreased signal on APT-CEST at the time of spontaneous infarction may have been attributed to intratumoral acidosis and loss of viable tumour. APT-CEST imaging is useful for evaluating the intratumoral condition and tumour viability of the infarcted or ischemic tumour.

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Background: Early neurologic deterioration occurs in up to one-third of patients with acute ischemic stroke (IS), often leading to poor functional outcomes. At present, few studies have applied amide proton transfer (APT) imaging to the evaluation of early neurological deterioration (END). This study analyzed the value of computed tomography perfusion (CTP) combined with multimodal magnetic resonance imaging (MRI) in patients with acute IS with END. Methods: This retrospective study included patients with acute IS who were admitted to the neurology inpatient department in a tertiary hospital from October 2021 to June 2023. Patients with acute IS underwent CTP within 24 hours of stroke onset and MRI [arterial spin labeling (ASL), susceptibility-weighted imaging (SWI), and APT] within 7 days. END was defined as an elevation of ≥2 points on the National Institute of Health Stroke Scale (NIHSS) within 7 days of stroke onset. Univariable and multivariable analyses were used to compare clinical and imaging biomarkers in patients with acute IS with and without END. The performance of potential biomarkers in distinguishing between the two groups was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Among the 70 patients with acute IS, 20 (29%) had END. After conducting univariable analysis, variables were selected for entry into a binary logistic regression analysis based on our univariable analysis results, previous research findings, clinical experience, and methodological standards. The results indicated that relative cerebral blood volume (CBV) on CTP, relative cerebral blood flow (CBF) on ASL, and relative signal intensity on amide proton transfer-weighted (APTw) imaging were independent risk factors for END. The areas under the ROC curves for these risk factors were 0.710 [95% confidence interval (CI): 0.559-0.861, P=0.006], 0.839 (95% CI: 0.744-0.933, P<0.001), and 0.804 (95% CI: 0.676-0.932, P<0.001), respectively. The combined area under the curve (AUC), sensitivity, and specificity of the four indices (0.941, 100%, and 78%, respectively) were higher than those of the four indices alone. Conclusions: CTP combined with multi-modal MRI better evaluated hemodynamics, tissue metabolism, and other relevant patient information, providing an objective basis for the clinical assessment of patients with acute IS with END and facilitating the development of accurate and personalized treatment plans.

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BACKGROUND: Differentiation of glioma and solitary brain metastasis (SBM), which requires biopsy or multi-disciplinary diagnosis, remains sophisticated clinically. Histogram analysis of MR diffusion or molecular imaging hasn't been fully investigated for the differentiation and may have the potential to improve it. METHODS: A total of 65 patients with newly diagnosed glioma or metastases were enrolled. All patients underwent DWI, IVIM, and APTW, as well as the T1W, T2W, T2FLAIR, and contrast-enhanced T1W imaging. The histogram features of apparent diffusion coefficient (ADC) from DWI, slow diffusion coefficient (Dslow), perfusion fraction (frac), fast diffusion coefficient (Dfast) from IVIM, and MTRasym@3.5ppm from APTWI were extracted from the tumor parenchyma and compared between glioma and SBM. Parameters with significant differences were analyzed with the logistics regression and receiver operator curves to explore the optimal model and compare the differentiation performance. RESULTS: Higher ADCkurtosis (P = 0.022), frackurtosis (P<0.001),and fracskewness (P<0.001) were found for glioma, while higher (MTRasym@3.5ppm)10 (P = 0.045), frac10 (P<0.001),frac90 (P = 0.001), fracmean (P<0.001), and fracentropy (P<0.001) were observed for SBM. frackurtosis (OR = 0.431, 95%CI 0.256-0.723, P = 0.002) was independent factor for SBM differentiation. The model combining (MTRasym@3.5ppm)10, frac10, and frackurtosis showed an AUC of 0.857 (sensitivity: 0.857, specificity: 0.750), while the model combined with frac10 and frackurtosis had an AUC of 0.824 (sensitivity: 0.952, specificity: 0.591). There was no statistically significant difference between AUCs from the two models. (Z = -1.14, P = 0.25). CONCLUSIONS: The frac10 and frackurtosis in enhanced tumor region could be used to differentiate glioma and SBM and (MTRasym@3.5ppm)10 helps improving the differentiation specificity.

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BACKGROUND: Ki-67 and human epidermal growth factor receptor 2 (HER2) are known oncogenes involved in bladder cancer (BCa) patient risk stratification. Preoperative assessment of their expression level can assist in clinical treatment decision-making. Recently, amide proton transfer-weighted (APTw) MRI has shown promising potential in the diagnosis of several malignancies. However, few studies reported the value of APTw imaging in evaluating Ki-67 and HER2 status of BCa. PURPOSE: To investigate the feasibility of APTw MRI in assessing the aggressive and proliferative potential regarding the expression levels of Ki-67 and HER2 in BCa. STUDY TYPE: Retrospective. SUBJECTS: 114 patients (mean age, 64.78 ± 11.93 [SD] years; 97 men) were studied. FIELD STRENGTH/SEQUENCE: APTw MRI acquired by a three-dimensional fast-spin-echo sequence at 3.0 T MRI system. ASSESSMENT: Patient pathologic findings, included histologic grade and the expression status of Ki-67 and HER2, were reviewed by one uropathologist. The APTw values of BCa were independently measured by two radiologists and were compared between high-/low-tumor grade group, high-/low-Ki-67 expression group, and high-/low-HER2 expression group. STATISTICAL TESTS: The interclass correlation coefficient, independent sample t-test, Mann-Whitney U test, Spearman's rank correlation, and receiver operating characteristic curve (ROC) analysis were used. P < 0.05 was considered statistically significant. RESULTS: Significantly higher APTw values were found in high-grade BCa patients (7.72% vs. 4.29%, P < 0.001), high-Ki-67 expression BCa patients (8.40% vs. 3.25%, P < 0.001) and HER2 positive BCa patients (8.24% vs. 5.40%, P = 0.001). APTw values were positively correlated with Ki-67 (r = 0.769) and HER2 (r = 0. 356) expression status. The area under the ROC curve of the APTw values for detecting Ki-67 and HER2 expression status were 0.883 (95% CI: 0.790-0.945) and 0.713 (95% CI: 0.592-0.816), respectively. DATA CONCLUSIONS: APTw MRI is a potential method to assess the biological and proliferation potential of BCa. TECHNICAL EFFICACY: Stage 2.

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PURPOSE: To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors. METHODS: Fifteen patients with intracranial MTs and 10 patients with BTs aged 0-30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann-Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933. CONCLUSIONS: The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.

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Purpose: To explore the value of 3D amide proton transfer weighted imaging (APTWI) in the differential diagnosis between benign and malignant bone tumors, and to compare the diagnostic performance of APTWI with traditional diffusion-weighted imaging (DWI). Materials and methods: Patients with bone tumors located in the pelvis or lower limbs confirmed by puncture or surgical pathology were collected from January 2021 to July 2023 in the First Affiliated Hospital of Zhengzhou University. All patients underwent APTWI and DWI examinations. The magnetization transfer ratio with asymmetric analysis at the frequency offset of 3.5 ppm [MTRasym(3.5 ppm)] derived by APTWI and the apparent diffusion coefficient (ADC) derived by DWI for the tumors were measured. The Kolmogorou-Smirnou and Levene normality test was used to confirm the normal distribution of imaging parameters; and the independent sample t test was used to compare the differences in MTRasym(3.5 ppm) and ADC between benign and malignant bone tumors. In addition, the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of different imaging parameters in differentiation between benign and malignant bone tumors. P<0.05 means statistically significant. Results: Among 85 bone tumor patients, 33 were benign and 52 were malignant. The MTRasym(3.5 ppm) values of malignant bone tumors were significantly higher than those of benign tumors, while the ADC values were significantly lower in benign tumors. ROC analysis shows that MTRasym(3.5 ppm) and ADC values perform well in the differential diagnosis of benign and malignant bone tumors, with the area under the ROC curve (AUC) of 0.798 and 0.780, respectively. Combination of MTRasym(3.5 ppm) and ADC values can further improve the diagnostic performance with the AUC of 0.849 (sensitivity = 84.9% and specificity = 73.1%). Conclusion: MTRasym(3.5 ppm) of malignant bone tumors was significantly higher than that of benign bone tumors, reflecting the abnormal increase of protein synthesis in malignant tumors. APTWI combined with DWI can achieve a high diagnostic efficacy in differentiation between benign and malignant bone tumors.

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Purpose: To develop a SNR enhancement method for chemical exchange saturation transfer (CEST) imaging using a denoising convolutional autoencoder (DCAE), and compare its performance with state-of-the-art denoising methods. Method: The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of 1D convolutions, nonlinearity applications and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in-vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric. Results: In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirms the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared to other methods. While no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings. Conclusion: The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared to other methods.

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BACKGROUND: 1p/19q co-deletion in low-grade gliomas (LGG, World Health Organization grade II and III) is of great significance in clinical decision making. We aim to use radiomics analysis to predict 1p/19q co-deletion in LGG based on amide proton transfer weighted (APTw), diffusion weighted imaging (DWI), and conventional MRI. METHODS: This retrospective study included 90 patients histopathologically diagnosed with LGG. We performed a radiomics analysis by extracting 8454 MRI-based features form APTw, DWI and conventional MR images and applied a least absolute shrinkage and selection operator (LASSO) algorithm to select radiomics signature. A radiomics score (Rad-score) was generated using a linear combination of the values of the selected features weighted for each of the patients. Three neuroradiologists, including one experienced neuroradiologist and two resident physicians, independently evaluated the MR features of LGG and provided predictions on whether the tumor had 1p/19q co-deletion or 1p/19q intact status. A clinical model was then constructed based on the significant variables identified in this analysis. A combined model incorporating both the Rad-score and clinical factors was also constructed. The predictive performance was validated by receiver operating characteristic curve analysis, DeLong analysis and decision curve analysis. P < 0.05 was statistically significant. RESULTS: The radiomics model and the combined model both exhibited excellent performance on both the training and test sets, achieving areas under the curve (AUCs) of 0.948 and 0.966, as well as 0.909 and 0.896, respectively. These results surpassed the performance of the clinical model, which achieved AUCs of 0.760 and 0.766 on the training and test sets, respectively. After performing Delong analysis, the clinical model did not significantly differ in predictive performance from three neuroradiologists. In the training set, both the radiomic and combined models performed better than all neuroradiologists. In the test set, the models exhibited higher AUCs than the neuroradiologists, with the radiomics model significantly outperforming resident physicians B and C, but not differing significantly from experienced neuroradiologist. CONCLUSIONS: Our results suggest that our algorithm can noninvasively predict the 1p/19q co-deletion status of LGG. The predictive performance of radiomics model was comparable to that of experienced neuroradiologist, significantly outperforming the diagnostic accuracy of resident physicians, thereby offering the potential to facilitate non-invasive 1p/19q co-deletion prediction of LGG.

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BACKGROUND: To investigate the optimal B1,rms value of renal amide proton transfer-weighted (APTw) images and the reproducibility of this value, and to explore the utility of APT imaging of renal masses and kidney tissues. METHODS: APTw images with different B1,rms values were repeatedly recorded in 15 healthy volunteers to determine the optimal value. Two 4-point Likert scales (poor [1] to excellent [4]) were used to evaluate contour clarity and artifacts in masses and normal tissues. The APTw values of masses and normal tissues were then compared in evaluable images (contour clarity score > 1, artifacts score > 1). The APTw of malignant masses, normal tissues, and benign masses were calculated and compared with the Mann-Whitney U test. RESULTS: The optimal scanning parameter of B1,rms was 2 µT, and the APTw images had good agreement in the volunteers. Our study of APTw imaging examined 70 renal masses (13 benign, 57 malignant) and 49 normal kidneys (including those from 15 healthy volunteers). The mean APTw value for renal malignant masses (2.28(1.55)) was different from that for benign masses (0.91(1.30)) (P<0.001), renal cortex (1.30 (1.25)) (P<0.001), renal medulla (1.64 (1.33)) (P<0.05), and renal pelvis (5.49 (2.65)) (P<0.001). CONCLUSION: These preliminary data demonstrate that APTw imaging of the kidneys has potential use as an imaging biomarker for the differentiation of normal tissues, malignant masses, and benign masses.

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PURPOSE: CEST can image macromolecules/compounds via detecting chemical exchange between labile protons and bulk water. B1 field inhomogeneity impairs CEST quantification. Conventional B1 inhomogeneity correction methods depend on interpolation algorithms, B1 choices, acquisition number or calibration curves, making reliable correction challenging. This study proposed a novel B1 inhomogeneity correction method based on a direct saturation (DS) removed omega plot model. METHODS: Four healthy volunteers underwent B1 field mapping and CEST imaging under four nominal B1 levels of 0.75, 1.0, 1.5, and 2.0 µT at 5T. DS was resolved using a multi-pool Lorentzian model and removed from respective Z spectrum. Residual spectral signals were used to construct the omega plot as a linear function of 1/ B 1 2 $$ {B}_1^2 $$ , from which corrected signals at nominal B1 levels were calculated. Routine asymmetry analysis was conducted to quantify amide proton transfer (APT) effect. Its distribution across white matter was compared before and after B1 inhomogeneity correction and also with the conventional interpolation approach. RESULTS: B1 inhomogeneity yielded conspicuous artifact on APT images. Such artifact was mitigated by the proposed method. Homogeneous APT maps were shown with SD consistently smaller than that before B1 inhomogeneity correction and the interpolation method. Moreover, B1 inhomogeneity correction from two and four CEST acquisitions yielded similar results, superior over the interpolation method that derived inconsistent APT contrasts among different B1 choices. CONCLUSION: The proposed method enables reliable B1 inhomogeneity correction from at least two CEST acquisitions, providing an effective way to improve quantitative CEST MRI.

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PURPOSE: Conventional amide proton transfer (APT)-weighted imaging requires a chemical exchange saturation transfer (CEST) sequence with multiple saturation frequency offsets and a B0 correction sequence, plus a long acquisition time that can be reduced by applying the conventional method using CEST images with seven radiation pulses (i.e., the seven-points method). For a further reduction of acquisition times, we propose fast two-dimensional (2D) APT-weighted imaging based on a self B0 correction using the turbo spin echo (TSE)-Dixon method. We conducted a phantom study to investigate the accuracy of TSE-Dixon APT-weighted imaging. METHODS: We prepared two types of phantoms with six samples for a concentrationdependent evaluation and a pH-dependent evaluation. APT-weighted images were acquired by the conventional, seven-points, and TSE-Dixon methods. Linear regression analyses assessed the dependence between each method's APT signal intensities (SIs) and the concentration or pH. We performed a one-way analysis of variance with Tukey's honestly significant difference post hoc test to compare the APT SIs among the three methods. The agreement of the APT SIs between the conventional and seven-points or TSE-Dixon methods was assessed by a Bland- Altman plot analysis. RESULTS: The APT SIs of all three acquisition methods showed positive concentration dependence and pH dependence. No significant differences were observed in the APT SIs between the conventional and TSE-Dixon methods at each concentration. The Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in 0.42% bias and narrow 95% limits of agreement (LOA) (0.93%-0.09%) compared to the conventional method. The APT SIs measured using the TSE-Dixon method showed 0.14% bias and similar 95% LOA (-0.33% to 0.61%) compared with the seven-points method. The APT SIs of all three methods showed positive pH dependence. At each pH, no significant differences in the APT SIs were observed among the methods. Bland-Altman plot analyses showed that the APT SIs measured with the seven-points method resulted in low bias (0.03%) and narrow 95% LOA (-0.30% to 0.36%) compared to the conventional method. The APT SIs measured by the TSE-Dixon method showed slightly larger bias (0.29%) and similar 95% LOA (from -0.15% to 0.72%) compared to those measured by the seven-points method. CONCLUSION: These results demonstrated that our proposed method has the same concentration dependence and pH dependence as the conventional method and the seven-points method. We thus expect that APT-weighted imaging with less influence of motion can be obtained in clinical examinations.

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Background: Prostate cancer invades the capsule is a key factor in selecting appropriate treatment methods. Accurate preoperative prediction of extraprostatic extension (EPE) can help achieve precise selection of treatment plans. Purpose: The aim of this study is to verify the diagnostic efficacy of tumor size, length of capsular contact (LCC), apparent diffusion coefficient (ADC), and Amide proton transfer (APT) value in predicting EPE. Additionally, the study aims to investigate the potential additional value of APT for predicting EPE. Method: This study include 47 tumor organ confined patients (age, 64.16 ± 9.18) and 50 EPE patients (age, 61.51 ± 8.82). The difference of tumor size, LCC, ADC and APT value between groups were compared. Binary logistic regression was used to screen the EPE predictors. The receiver operator characteristic curve analysis was performed to assess the diagnostic performance of variables for predicting EPE. The diagnostic efficacy of combined models (model I: ADC+LCC+tumor size; model II: APT+LCC+tumor size; and model III: APT +ADC+LCC+tumor size) were also analyzed. Results: APT, ADC, tumor size and the LCC were independent predictors of EPE. The area under the curve (AUC) of APT, ADC, tumor size and the LCC were 0.752, 0.665, 0.700 and 0.756, respectively. The AUC of model I, model II, and model III were 0.803, 0.845 and 0.869, respectively. The cutoff value of APT, ADC, tumor size and the LCC were 3.65%, 0.97×10-3mm2/s, 17.30mm and 10.78mm, respectively. The sensitivity/specificity of APT, ADC, tumor size and the LCC were 76%/89.4.0%, 80%/59.6%, 54%/78.9%, 72%/66%, respectively. The sensitivity/specificity of model I, Model II and Model III were 74%/72.3%, 82%/72.5% and 84%/80.9%, respectively. Data conclusion: Amide proton transfer imaging has added value for predicting EPE. The combination model of APT balanced the sensitivity and specificity.

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Background: Neurodegeneration has been suggested to be associated with cerebral small vessel disease (CSVD). The association between different CSVD imaging markers and the extent of neurodegeneration could be indirectly confirmed by examining the relationship between CSVD imaging markers and the hippocampal amide proton transfer (APT) values. The associations between hippocampal APT values with CSVD imaging markers and CSVD total load need to be further validated. The aim of this study was to investigate potential variations in hippocampal APT values among individuals with CSVD imaging markers and varying degrees of CSVD total burden. Methods: A cross-sectional study (retrospective analysis of prospectively-acquired data) was conducted at Nanxishan Hospital of Guangxi Zhuang Autonomous Region. From May 2020 to June 2021, 165 individuals (age, 40-76 years; male/female, 103/62) were included in this study. The inclusion criteria for the participants were as follows: The presence of lacunar infarction (LI), and/or cerebral microbleed (CMB); moderate-to-severe enlarged perivascular space (EPVS) (>20); deep white matter hyperintensity (WMH) > Fazekas 2 or periventricular WMH > Fazekas. The exclusion criteria comprised the following: History of craniocerebral operation; Cases with significant pathology incidentally identified during magnetic resonance (MR) scan; Drug or alcohol abuse. The differences of hippocampal APT values between CSVD imaging makers presence or absence groups and different CSVD total burden groups were compared using independent t-test and one-way analysis of variance (ANOVA). The correlations between APT values and CSVD imaging markers were analyzed using Pearson correlation analysis. A mediation analysis model was used to investigate the mediating effect of the hippocampal APT values in the association between CSVD total loads and Montreal Cognitive Assessment (MoCA) score was assessed. Results: The hippocampal APT values among different CSVD total load groups were significantly different (P<0.001). The hippocampal APT values were significantly different between the imaging markers presence and absence groups. The P values for the LI, WMH EPVS, and CMB presence or absence groups were <0.001, <0.001, 0.034, and 0.002, respectively. The hippocampal APT values were significantly correlated with CMB (P<0.01), LI (P<0.01) and WMH (P<0.01). The mediation models demonstrated that the APT values of the hippocampus partially mediated the association between CSVD total load and MoCA score, the proportion of mediation attributable was calculated as 17.50%. Conclusions: Hippocampal APT values were associated with CSVD imaging markers and total burden. Hippocampal APT values may serve as a biomarker for the early detection of neurodegeneration in CSVD patients.