Differentiation of low- and high-grade pediatric gliomas with amide proton transfer imaging: added value beyond quantitative relaxation times

A Combination of Amide Proton Transfer, Tumor Blood Flow, and Apparent Diffusion Coefficient Histogram Analysis Is Useful for Differentiating Malignant from Benign Intracranial Tumors in Young Patients: A Preliminary Study

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.

Amide proton transfer-weighted imaging and derived radiomics in the classification of adult-type diffuse gliomas

OBJECTIVES

To evaluate the utility of amide proton transfer-weighted (APTw) MRI imaging and its derived radiomics in classifying adult-type diffuse glioma.

MATERIALS AND METHODS

In this prospective study, APTw imaging was performed on 129 patients with adult-type diffuse gliomas. The mean APTw-related metrics (chemical exchange saturation transfer ratio (CESTR), CESTR normalized with the reference value (CESTRnr), and relaxation-compensated inverse magnetization transfer ratio (MTRRex)) and radiomic features within 3D tumor masks were extracted. APTw-radiomics models were developed using a support vector machine (SVM) classifier. Sensitivity analysis with tumor area of interest, different histogram cutoff values, and other classifiers were conducted.

RESULTS

CESTR, CESTRnr, and MTRRex in glioblastomas were all significantly higher (p < 0.0003) than those of oligodendrogliomas and astrocytomas, with no significant difference between oligodendrogliomas and astrocytomas. The APTw-related metrics for IDH-wildtype and high-grade gliomas were significantly higher (p < 0.001) than those for the IDH-mutant and low-grade gliomas, with area under the curve (AUCs) of 0.88 for CESTR. The CESTR-radiomics models demonstrated accuracies of 84% (AUC 0.87), 83% (AUC 0.83), 90% (AUC 0.95), and 84% (AUC 0.86) in predicting the IDH mutation status, differentiating glioblastomas from astrocytomas, distinguishing glioblastomas from oligodendrogliomas, and determining high/low grade prediction, respectively, but showed poor performance in distinguishing oligodendrogliomas from astrocytomas (accuracy 63%, AUC 0.63). The sensitivity analysis affirmed the robustness of the APTw signal and APTw-derived radiomics prediction models.

CONCLUSION

APTw imaging, along with its derived radiomics, presents a promising quantitative approach for prediction IDH mutation and grading adult-type diffuse glioma.

CLINICAL RELEVANCE STATEMENT

Amide proton transfer-weighted imaging, a quantitative imaging biomarker, coupled with its derived radiomics, offers a promising non-invasive approach for predicting IDH mutation status and grading adult-type diffuse gliomas, thereby informing individualized clinical diagnostics and treatment strategies.

KEY POINTS

• This study evaluates the differences of different amide proton transfer-weighted metrics across three molecular subtypes and their efficacy in classifying adult-type diffuse glioma. • Chemical exchange saturation transfer ratio normalized with the reference value and relaxation-compensated inverse magnetization transfer ratio effectively predicts IDH mutation/grading, notably the first one. • Amide proton transfer-weighted imaging and its derived radiomics holds potential to be used as a diagnostic tool in routine clinical characterizing adult-type diffuse glioma.

Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review

Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.

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

PURPOSE

To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data.

THEORY AND METHODS

Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-ω domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods.

RESULTS

The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used.

CONCLUSIONS

The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.

CEST 2022-three-dimensional amide proton transfer (APT) imaging can identify the changes of cerebral cortex in Parkinson's disease

PURPOSE

Amide proton transfer (APT) imaging has shown its diagnostic and predictive superiority in PD in our previous studies using 2D APT imaging based on deep nuclei. We hypothesized that the pathophysiological abnormality of PD will change the APT-related parameters in the cerebral cortex, and the signal changes can contribute to accurate diagnosis of Parkinson's disease.

METHODS

34 patients with sporadic Parkinson's disease (IPD) and 29 age- and sex-matched normal controls (NC) were enrolled in this prospective study. 3D-APT imaging and 3D-T1WI was performed in our participants. A volume-based morphometry algorithm was used and get automated cortical segmentations. Quantitative parameter maps of APT-related metrics were calculated by using SPM and MATLAB. The unpaired Student's t-test or Mann-Whitney U test was used for comparison of these values between IPD and NC groups. The associations between APT-related metrics and clinical assessments were investigated by Spearman correlation analysis. The receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performances. The binary logistic regression model was used to combine the imaging parameters.

RESULTS

There wasn't any correlations between cortical APT-related signals and clinical assessment, including the H&Y scale, the disease duration, the UPDRS III scores and the MMSE scores. The MTRasym, CESTRnr and MTRRex had significantly higher values (p <0.001, corrected by Bonferroni methods) in the IPD group than NC groups in the region of bilateral and total temporal grey matter. The single parameters achieved the best diagnostic performance among all APT-related metrics was MTRRex on the right temporal grey matter, with an area under the ROC curve (AUC) of 0.865. The combined parameters achieved the highest diagnostic performance (AUC: 0.932).

CONCLUSIONS

3D-APT imaging could identify the changes of the cerebral cortex in Parkinson's disease. The cortical changes of APT-related parameters could potentially serve as imaging biomarkers to aid in the non-invasive diagnosis of PD.

Histological and molecular classifications of pediatric glioma with time-dependent diffusion MRI-based microstructural mapping

BACKGROUND

Gliomas are the most common type of central nervous system tumors in children, and the combination of histological and molecular classification is essential for prognosis and treatment. Here, we proposed a newly developed microstructural mapping technique based on diffusion-time-dependent diffusion MRI td-dMRI theory to quantify tumor cell properties and tested these microstructural markers in identifying histological grade and molecular alteration of H3K27.

METHODS

This prospective study included 69 pediatric glioma patients aged 6.14 ± 3.25 years old, who underwent td-dMRI with pulsed and oscillating gradient diffusion sequences on a 3T scanner. dMRI data acquired at varying tds were fitted into a 2-compartment microstructural model to obtain intracellular fraction (fin), cell diameter, cellularity, etc. Apparent diffusivity coefficient (ADC) and T1 and T2 relaxation times were also obtained. H&E stained histology was used to validate the estimated microstructural properties.

RESULTS

For histological classification of low- and high-grade pediatric gliomas, the cellularity index achieved the highest area under the receiver-operating-curve (AUC) of 0.911 among all markers, while ADC, T1, and T2 showed AUCs of 0.906, 0.885, and 0.886. For molecular classification of H3K27-altered glioma in 39 midline glioma patients, cell diameter showed the highest discriminant power with an AUC of 0.918, and the combination of cell diameter and extracellular diffusivity further improved AUC to 0.929. The td-dMRI estimated fin correlated well with the histological ground truth with r = 0.7.

CONCLUSIONS

The td-dMRI-based microstructural properties outperformed routine MRI measurements in diagnosing pediatric gliomas, and the different microstructural features showed complementary strength in histological and molecular classifications.

Acquisition sequences and reconstruction methods for fast chemical exchange saturation transfer imaging

Chemical exchange saturation transfer (CEST) imaging is an emerging molecular magnetic resonance imaging (MRI) technique that has been developed and employed in numerous diseases. Based on the unique saturation transfer principle, a family of CEST-detectable biomolecules in vivo have been found capable of providing valuable diagnostic information. However, CEST MRI needs a relatively long scan time due to the common long saturation labeling module and typical acquisition of multiple frequency offsets and signal averages, limiting its widespread clinical applications. So far, a plethora of imaging schemes and techniques has been developed to accelerate CEST MRI. In this review, the key acquisition and reconstruction methods for fast CEST imaging are summarized from a practical and systematic point of view. The first acquisition sequence section describes the major development of saturation schemes, readout patterns, ultrafast z-spectroscopy, and saturation-editing techniques for rapid CEST imaging. The second reconstruction method section lists the important advances of parallel imaging, compressed sensing, sparsity in the z-spectrum, and algorithms beyond the Fourier transform for speeding up CEST MRI.

Saturation-prolongated and inhomogeneity-mitigated chemical exchange saturation transfer imaging with parallel transmission

Chemical exchange saturation transfer (CEST) imaging benefits from a longer saturation duration and a higher saturation duty cycle. Dielectric shading effects occur when the radiofrequency (RF) wavelength approaches the object size. Here, we proposed a simultaneous parallel transmission-based CEST (pTx-CEST) sequence to prolongate the saturation duration at a 100% duty cycle and improve the RF saturation homogeneity in CEST imaging. The simultaneous pTx-CEST sequence was implemented by switching the CEST saturation module from the non-pTx to pTx mode, using the pTx functionality with both transmit channels being driven simultaneously (instead of time-interleaved). The optimization of amplitude ratio and phase difference settings between RF channels for best B1 homogeneity was performed in phantoms of two different sizes mimicking the human brain and abdomen. The optimal amplitude and phase settings generating the best B1 homogeneity in the phantoms were used in pTx-CEST scans of the human study. The comparison of the maximum achievable saturation duration between the non-pTx-CEST and pTx-CEST sequences was performed in a protein phantom, healthy volunteers, and a metastatic brain tumor patient. The optimal amplitude ratio and phase difference setting between transmit channels manifested circular and elliptical polarization in the head-sized and abdomen-sized phantoms. In the brain, the maximum saturation durations achieved at a 100% duty cycle using the simultaneous pTx-CEST sequence were prolonged to 2240, 3220, and 4200 ms compared with 980 ms using the non-pTx-CEST sequence at repetition times of 3, 4, and 5 s, respectively. The longer saturation duration helped improve the image contrast between the tumor and the normal tissue in the patient. The optimized elliptical polarization mode saturation pulses yielded improved uniformity of CEST signals acquired from the human abdomen. The proposed simultaneous pTx-CEST sequence enabled essentially arbitrarily long saturation duration at a 100% duty cycle and helped reduce the dielectric shading effects with the optimized RF setting.

Recapitulating the Key Advances in the Diagnosis and Prognosis of High-Grade Gliomas: Second Half of 2021 Update

High-grade gliomas (World Health Organization grades III and IV) are the most frequent and fatal brain tumors, with median overall survivals of 24–72 and 14–16 months, respectively. We reviewed the progress in the diagnosis and prognosis of high-grade gliomas published in the second half of 2021. A literature search was performed in PubMed using the general terms “radio* and gliom*” and a time limit from 1 July 2021 to 31 December 2021. Important advances were provided in both imaging and non-imaging diagnoses of these hard-to-treat cancers. Our prognostic capacity also increased during the second half of 2021. This review article demonstrates slow, but steady improvements, both scientifically and technically, which express an increased chance that patients with high-grade gliomas may be correctly diagnosed without invasive procedures. The prognosis of those patients strictly depends on the final results of that complex diagnostic process, with widely varying survival rates.

Joint K-space and Image-space Parallel Imaging (KIPI) for accelerated chemical exchange saturation transfer acquisition

PURPOSE

To develop an auto-calibrated technique by joint K-space and Image-space Parallel Imaging (KIPI) for accelerated CEST acquisition.

THEORY AND METHODS

The KIPI method selects a calibration frame with a low acceleration factor (AF) and auto-calibration signals (ACS) acquired, from which the coil sensitivity profiles and artifact correction maps are calculated after restoring the k-space by GRAPPA. Then the other frames with high AF and without ACS can be reconstructed by SENSE and artifact suppression. The signal leakage due to the T₂ -decay filtering in k-space compromises the SENSE reconstruction, which can be corrected by the artifact suppression algorithm of KIPI. The 2D and 3D imaging experiments were done on the phantom, healthy volunteer, and brain tumor patient with a 3T scanner.

RESULTS

The proposed KIPI method was evaluated by retrospectively undersampled data with variable AFs and compared against existing parallel imaging methods (SENSE/auto, GRAPPA, and ESPIRiT). KIPI enabled CEST frames with random AFs to achieve similar image quality, eliminated the strong aliasing artifacts, and generated significantly smaller errors than the other methods (p < 0.01). The KIPI method permitted an AF up to 12-fold in both phase-encoding and slice-encoding directions for 3D CEST source images, achieving an overall 8.2-fold speedup in scan time.

CONCLUSION

KIPI is a novel auto-calibrated parallel imaging method that enables variable AFs for different CEST frames, achieves a significant reduction in scan time, and does not compromise the accuracy of CEST maps.

Amide Proton Transfer-weighted MRI in Predicting Histologic Grade of Bladder Cancer

Background Bladder cancer is classified into high and low grades with different clinical treatments and prognoses. Thus, accurate preoperative evaluation of the histologic grade through imaging techniques is essential. Purpose To investigate the potential of amide proton transfer-weighted (APTw) MRI in evaluating the grade of bladder cancer and to evaluate whether APTw MRI can add value to diffusion-weighted imaging (DWI) at MRI. Materials and Methods In this single-center prospective study, participants with pathologic analysis-confirmed bladder cancer with no previous treatment, lesions larger than 10 mm, and adequate MRI quality were enrolled from July 2020 to September 2021 in a university teaching hospital. All participants underwent preoperative multiparametric MRI, including APTw MRI and DWI. The mean APTw and apparent diffusion coefficient (ADC) values of the primary tumor were measured independently by two radiologists. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of these quantitative parameters. Results In total, 83 participants (mean age, 64 years ± 13 [SD]; 72 men) were evaluated: 51 with high-grade and 32 with low-grade bladder cancer. High-grade bladder cancer showed higher APTw values (6% [IQR, 4%-12%] vs 2% [IQR, 1%-3%]; P < .001) and lower ADC values (0.92 × 10^-3 mm²/sec ± 0.17 vs 1.21 × 10^-3 mm²/sec ± 0.25; P < .001) than low-grade bladder cancer. The area under the receiver operating characteristic curve (AUC) of APTw and ADC for differentiating low- and high-grade bladder cancer was similar (0.84 for both; P = .94). Moreover, the combination of the two techniques improved the diagnostic performance (AUC, 0.93; all P = .01). Conclusion The combination of amide proton transfer-weighted and diffusion-weighted MRI has the potential to improve the histologic characterization of bladder cancer by differentiating low- from high-grade cancers. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Milot in this issue.

Molecular Imaging of Brain Tumors and Drug Delivery Using CEST MRI: Promises and Challenges

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) detects molecules in their natural forms in a sensitive and non-invasive manner. This makes it a robust approach to assess brain tumors and related molecular alterations using endogenous molecules, such as proteins/peptides, and drugs approved for clinical use. In this review, we will discuss the promises of CEST MRI in the identification of tumors, tumor grading, detecting molecular alterations related to isocitrate dehydrogenase (IDH) and O-6-methylguanine-DNA methyltransferase (MGMT), assessment of treatment effects, and using multiple contrasts of CEST to develop theranostic approaches for cancer treatments. Promising applications include (i) using the CEST contrast of amide protons of proteins/peptides to detect brain tumors, such as glioblastoma multiforme (GBM) and low-grade gliomas; (ii) using multiple CEST contrasts for tumor stratification, and (iii) evaluation of the efficacy of drug delivery without the need of metallic or radioactive labels. These promising applications have raised enthusiasm, however, the use of CEST MRI is not trivial. CEST contrast depends on the pulse sequences, saturation parameters, methods used to analyze the CEST spectrum (i.e., Z-spectrum), and, importantly, how to interpret changes in CEST contrast and related molecular alterations in the brain. Emerging pulse sequence designs and data analysis approaches, including those assisted with deep learning, have enhanced the capability of CEST MRI in detecting molecules in brain tumors. CEST has become a specific marker for tumor grading and has the potential for prognosis and theranostics in brain tumors. With increasing understanding of the technical aspects and associated molecular alterations detected by CEST MRI, this young field is expected to have wide clinical applications in the near future.