Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study.

BACKGROUND: The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. METHODS: In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. FINDINGS: In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). INTERPRETATION: Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. FUNDING: Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation.

Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene silencing trial has recently started, there is an urgent need for quantitative, sensitive imaging markers at pre-symptomatic stages for stratification and treatment assessment. This work introduces CerebNet, a fully automated, extensively validated, deep learning method for the lobular segmentation of the cerebellum, including the separation of gray and white matter. For training, validation, and testing, T1-weighted images from 30 participants were manually annotated into cerebellar lobules and vermal sub-segments, as well as cerebellar white matter. CerebNet combines FastSurferCNN, a UNet-based 2.5D segmentation network, with extensive data augmentation, e.g. realistic non-linear deformations to increase the anatomical variety, eliminating additional preprocessing steps, such as spatial normalization or bias field correction. CerebNet demonstrates a high accuracy (on average 0.87 Dice and 1.742mm Robust Hausdorff Distance across all structures) outperforming state-of-the-art approaches. Furthermore, it shows high test-retest reliability (average ICC >0.97 on OASIS and Kirby) as well as high sensitivity to disease effects, including the pre-ataxic stage of spinocerebellar ataxia type 3 (SCA3). CerebNet is compatible with FreeSurfer and FastSurfer and can analyze a 3D volume within seconds on a consumer GPU in an end-to-end fashion, thus providing an efficient and validated solution for assessing cerebellum sub-structure volumes. We make CerebNet available as source-code (https://github.com/Deep-MI/FastSurfer).

Anterior chamber enhancement predicts optic nerve infiltration in retinoblastoma.

OBJECTIVES: As described recently, intravenously injected gadolinium-based contrast agent (GBCA) penetrates into the anterior eye chamber (AC) and is drained from the retina to the distal optic nerve (ON) along perivascular spaces, which serves retinal homeostasis and was termed the orbital glymphatic system (GS). Independently, AC enhancement predicted ON infiltration, a major risk factor for advanced retinoblastoma (RB), in a small RB patient cohort. We aimed to review the supposed imaging biomarker for ON infiltration in a large RB cohort and with respect to the recently described orbital GS. METHODS: This IRB-approved retrospective single-center study encompassed 539 orbital MRIs performed with an orbital coil and with the children under general anesthesia. Differences of signal intensity ratios (∆SIRs) of the AC to the lens were determined between non-contrast and GBCA-enhanced T1-weighted images and were correlated with histopathologic presence of ON infiltration. RESULTS: ∆SIR of the RB eye was an independent, significant predictor for ON invasion in multivariate analysis with adjustment for tumor size (p < 0.05) and increased with infiltration level. CONCLUSIONS: GBCA enhancement of the AC predicts ON infiltration. This might be caused by impairment of the orbital glymphatic system, which is supposed to clear toxic metabolites from the retina to the postlaminar ON. In RB with ON infiltration, this efflux path is likely to be inhibited, which is supposed to result in disturbed retinal homeostasis, release of vascular endothelial growth factor, and iris neovascularization, which increases penetration of GBCA into the AC. KEY POINTS: • Infiltration of the optic nerve can be predicted by anterior chamber enhancement after intravenous MRI contrast agent administration. • Increased anterior chamber enhancement in retinoblastoma with optic nerve infiltration might result from dysfunction of the orbital glymphatic system with disturbance of retinal homeostasis and consecutive iris neovascularization.

The anterior eye chamber: entry of the natural excretion pathway of gadolinium contrast agents?

OBJECTIVE: Previous studies provided evidence that gadolinium can be found in the aqueous chamber (AC) of the eye several hours post injection (p.i.) of gadolinium-based contrast agents (GBCAs). This study aimed to investigate whether gadolinium can be detected promptly after injection of a macrocyclic GBCA on contrast-enhanced T1-weighted MRI in the AC of children. METHODS: This retrospective study encompassed MRI of 200 healthy eyes of children suffering from retinoblastoma of the contralateral eye. MRI was performed with an orbital coil with the children in a state of general anesthesia. Differences of signal intensity ratios (∆SIRs) of the AC to the lens were determined between pre and post contrast-enhanced T1-weighted images (Dotarem®, Guerbet, 0.1 ml/kg body weight, mean (standard deviation) p.i. time = 12:24 (± 2:31) min). RESULTS: A highly significant signal intensity increase was found in the AC of healthy eyes 12 min after GBCA injection (median ∆SIR (interquartile range) = + 0.08 (0.05-0.12), p < 0.0001). In addition, gadolinium enhancement showed a strong negative correlation with children's age in multivariate analysis with adjustment for p.i. time (p < 0.0001). CONCLUSIONS: GBCA leakage into the AC of healthy infantile eyes was found promptly after injection. The negative correlation between patient age and GBCA enhancement might be explained by a maturation process of the blood-aqueous barrier or Schlemm's canal. Future studies should assess the duration and potential diagnostic applications as well as possible safety concerns of gadolinium presence in the AC. KEY POINTS: • Leakage of gadolinium-based contrast agent into the aqueous chamber of infantile eyes was found promptly after intravenous injection (p < 0.0001). • Gadolinium enhancement of the anterior eye chamber was negatively correlated with the children's age (p < 0.0001).

Abbreviated MRI Protocols in Breast Cancer Diagnostics.

Oncologic imaging focused on the detection of breast cancer is of increasing importance, with over 1.7 million new cases detected each year worldwide. MRI of the breast has been described to be one of the most sensitive imaging modalities in breast cancer detection; however, clinical use is limited due to high costs. In the past, the objective and clinical routine of oncologic imaging was to provide one extended imaging protocol covering all potential needs and clinical implications regardless of the specific clinical indication or question. Future protocols might be more focused according to a "keep it short and simple" approach, with a reduction of patient magnet time and a limited number of images to review. Rather than replacing conventional full-diagnostic breast MRI protocols, these approaches aim at introducing a new thinking in oncologic imaging using a diversification of available imaging approaches targeted to the dedicated clinical needs of the individual patient. Here we review current approaches on using abbreviated protocols that aim to increase the clinical availability and use of breast MRI for improved early detection of breast cancer. Level of Evidence: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:647-658.

Relaxation-compensated amide proton transfer (APT) MRI signal intensity is associated with survival and progression in high-grade glioma patients.

OBJECTIVES: The purpose of this study was to investigate the association of relaxation-compensated chemical exchange saturation transfer (CEST) MRI with overall survival (OS) and progression-free survival (PFS) in newly diagnosed high-grade glioma (HGG) patients. METHODS: Twenty-six patients with newly diagnosed high-grade glioma (WHO grades III-IV) were included in this prospective IRB-approved study. CEST MRI was performed on a 7.0-T whole-body scanner. Association of patient OS/PFS with relaxation-compensated CEST MRI (amide proton transfer (APT), relayed nuclear Overhauser effect (rNOE)/NOE, downfield-rNOE-suppressed APT (dns-APT)) and diffusion-weighted imaging (apparent diffusion coefficient) were assessed using the univariate Cox proportional hazards regression model. Hazard ratios (HRs) and corresponding 95% confidence intervals were calculated. Furthermore, OS/PFS association with clinical parameters (age, gender, O6-methylguanine-DNA methyltransferase (MGMT) promotor methylation status, and therapy: biopsy + radio-chemotherapy vs. debulking surgery + radio-chemotherapy) were tested accordingly. RESULTS: Relaxation-compensated APT MRI was significantly correlated with patient OS (HR = 3.15, p = 0.02) and PFS (HR = 1.83, p = 0.009). The strongest association with PFS was found for the dns-APT metric (HR = 2.61, p = 0.002). These results still stand for the relaxation-compensated APT contrasts in a homogenous subcohort of n = 22 glioblastoma patients with isocitrate dehydrogenase (IDH) wild-type status. Among the tested clinical parameters, patient age (HR = 1.1, p = 0.001) and therapy (HR = 3.68, p = 0.026) were significant for OS; age additionally for PFS (HR = 1.04, p = 0.048). CONCLUSION: Relaxation-compensated APT MRI signal intensity is associated with overall survival and progression-free survival in newly diagnosed, previously untreated glioma patients and may, therefore, help to customize treatment and response monitoring in the future. KEY POINTS: • Amide proton transfer (APT) MRI signal intensity is associated with overall survival and progression in glioma patients. • Relaxation compensation enhances the information value of APT MRI in tumors. • Chemical exchange saturation transfer (CEST) MRI may serve as a non-invasive biomarker to predict prognosis and customize treatment.

Deep learning-based defacing tool for CT angiography: CTA-DEFACE.

The growing use of artificial neural network (ANN) tools for computed tomography angiography (CTA) data analysis underscores the necessity for elevated data protection measures. We aimed to establish an automated defacing pipeline for CTA data. In this retrospective study, CTA data from multi-institutional cohorts were utilized to annotate facemasks (n = 100) and train an ANN model, subsequently tested on an external institution's dataset (n = 50) and compared to a publicly available defacing algorithm. Face detection (MTCNN) and verification (FaceNet) networks were applied to measure the similarity between the original and defaced CTA images. Dice similarity coefficient (DSC), face detection probability, and face similarity measures were calculated to evaluate model performance. The CTA-DEFACE model effectively segmented soft face tissue in CTA data achieving a DSC of 0.94 ± 0.02 (mean ± standard deviation) on the test set. Our model was benchmarked against a publicly available defacing algorithm. After applying face detection and verification networks, our model showed substantially reduced face detection probability (p < 0.001) and similarity to the original CTA image (p < 0.001). The CTA-DEFACE model enabled robust and precise defacing of CTA data. The trained network is publicly accessible at www.github.com/neuroAI-HD/CTA-DEFACE . RELEVANCE STATEMENT: The ANN model CTA-DEFACE, developed for automatic defacing of CT angiography images, achieves significantly lower face detection probabilities and greater dissimilarity from the original images compared to a publicly available model. The algorithm has been externally validated and is publicly accessible. KEY POINTS: The developed ANN model (CTA-DEFACE) automatically generates facemasks for CT angiography images. CTA-DEFACE offers superior deidentification capabilities compared to a publicly available model. By means of graphics processing unit optimization, our model ensures rapid processing of medical images. Our model underwent external validation, underscoring its reliability for real-world application.

Whole-Brain Intracellular pH Mapping of Gliomas Using High-Resolution 31P MR Spectroscopic Imaging at 7.0 T.

Malignant tumors commonly exhibit a reversed pH gradient compared with normal tissue, with a more acidic extracellular pH and an alkaline intracellular pH (pHi). In this prospective study, pHi values in gliomas were quantified using high-resolution phosphorous 31 (31P) spectroscopic MRI at 7.0 T and were used to correlate pHi alterations with histopathologic findings. A total of 12 participants (mean age, 58 years ± 18 [SD]; seven male, five female) with histopathologically proven, newly diagnosed glioma were included between September 2018 and November 2019. The 31P spectroscopic MRI scans were acquired using a double-resonant 31P/1H phased-array head coil together with a three-dimensional (3D) 31P chemical shift imaging sequence (5.7-mL voxel volume) performed with a 7.0-T whole-body system. The 3D volumetric segmentations were performed for the whole-tumor volumes (WTVs); tumor subcompartments of necrosis, gadolinium enhancement, and nonenhancing T2 (NCE T2) hyperintensity; and normal-appearing white matter (NAWM), and pHi values were compared. Spearman correlation was used to assess association between pHi and the proliferation index Ki-67. For all study participants, mean pHi values were higher in the WTV (7.057 ± 0.024) compared with NAWM (7.006 ± 0.012; P < .001). In eight participants with high-grade gliomas, pHi was increased in all tumor subcompartments (necrosis, 7.075 ± 0.033; gadolinium enhancement, 7.075 ± 0.024; NCE T2 hyperintensity, 7.043 ± 0.015) compared with NAWM (7.004 ± 0.014; all P < .01). The pHi values of WTV positively correlated with Ki-67 (R2 = 0.74, r = 0.78, P = .001). In conclusion, 31P spectroscopic MRI at 7.0 T enabled high-resolution quantification of pHi in gliomas, with pHi alteration associated with the Ki-67 proliferation index, and may aid in diagnosis and treatment monitoring. Keywords: 31P MRSI, pH, Glioma, Glioblastoma, Ultra-High-Field MRI, Imaging Biomarker, 7 Tesla Supplemental material is available for this article. © RSNA, 2023.

Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in Neuroradiology.

ABSTRACT: Deep learning approaches are playing an ever-increasing role throughout diagnostic medicine, especially in neuroradiology, to solve a wide range of problems such as segmentation, synthesis of missing sequences, and image quality improvement. Of particular interest is their application in the reduction of gadolinium-based contrast agents, the administration of which has been under cautious reevaluation in recent years because of concerns about gadolinium deposition and its unclear long-term consequences. A growing number of studies are investigating the reduction (low-dose approach) or even complete substitution (zero-dose approach) of gadolinium-based contrast agents in diverse patient populations using a variety of deep learning methods. This work aims to highlight selected research and discusses the advantages and limitations of recent deep learning approaches, the challenges of assessing its output, and the progress toward clinical applicability distinguishing between the low-dose and zero-dose approach.

Reduction of Gadolinium-Based Contrast Agents in MRI Using Convolutional Neural Networks and Different Input Protocols: Limited Interchangeability of Synthesized Sequences With Original Full-Dose Images Despite Excellent Quantitative Performance.

OBJECTIVES: The purpose of this study was to implement a state-of-the-art convolutional neural network used to synthesize artificial T1-weighted (T1w) full-dose images from corresponding noncontrast and low-dose images (using various settings of input sequences) and test its performance on a patient population acquired prospectively. MATERIALS AND METHODS: In this monocentric, institutional review board-approved study, a total of 138 participants were included who received an adapted imaging protocol with acquisition of a T1w low dose after administration of 10% of the standard dose and acquisition of a T1w full dose after administration of the remaining 90% of the standard dose of a gadolinium-containing contrast agent. A total of 83 participants formed the training sample (51.7 ± 16.5 years, 36 women), 25 the validation sample (55.3 ± 16.4 years, 11 women), and 30 the test sample (55.0 ± 15.0 years, 9 women). Four input settings were differentiated: only the T1w noncontrast and T1w low-dose images (standard setting), only the T1w noncontrast and T1w low-dose images with a prolonged postinjection time of 5 minutes (5-minute setting), multiple noncontrast sequences (T1w, T2w, diffusion) and the T1w low-dose images (extended setting), and only noncontrast sequences (T1w, T2w, diffusion) were used (zero-dose setting). For each setting, a deep neural network was trained to synthesize artificial T1w full-dose images, which were assessed on the test sample using an objective evaluation based on quantitative metrics and a subjective evaluation through a reader-based study. Three readers scored the overall image quality, the interchangeability in regard to the clinical conclusion compared with the true T1w full-dose sequence, the contrast enhancement of lesions, and their conformity to the respective references in the true T1w full dose. RESULTS: Quantitative analysis of the artificial T1w full-dose images of the standard setting provided a peak signal-to-noise ratio of 33.39 ± 0.62 (corresponding to an average improvement of the low-dose sequences of 5.2 dB) and a structural similarity index measure of 0.938 ± 0.005. In the 4-fold cross-validation, the extended setting yielded similar performance to the standard setting in terms of peak signal-to-noise ratio ( P = 0.20), but a slight improvement in structural similarity index measure ( P < 0.0001). For all settings, the reader study found comparable overall image quality between the original and artificial T1w full-dose images. The proportion of scans scored as fully or mostly interchangeable was 55%, 58%, 43%, and 3% and the average counts of false positives per case were 0.42 ± 0.83, 0.34 ± 0.71, 0.82 ± 1.15, and 2.00 ± 1.07 for the standard, 5-minute, extended, and zero-dose setting, respectively. Using a 5-point Likert scale (0 to 4, 0 being the worst), all settings of synthesized full-dose images showed significantly poorer contrast enhancement of lesions compared with the original full-dose sequence (difference of average degree of contrast enhancement-standard: -0.97 ± 0.83, P = <0.001; 5-minute: -0.93 ± 0.91, P = <0.001; extended: -0.96 ± 0.97, P = <0.001; zero-dose: -2.39 ± 1.14, P = <0.001). The average scores of conformity of the lesions compared with the original full-dose sequence were 2.25 ± 1.21, 2.22 ± 1.27, 2.24 ± 1.25, and 0.73 ± 0.93 for the standard, 5-minute, extended, and zero-dose setting, respectively. CONCLUSIONS: The tested deep learning algorithm for synthesis of artificial T1w full-dose sequences based on images after administration of only 10% of the standard dose of a gadolinium-based contrast agent showed very good quantitative performance. Despite good image quality in all settings, both false-negative and false-positive signals resulted in significantly limited interchangeability of the synthesized sequences with the original full-dose sequences.

Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI: A Literature Review.

ABSTRACT: Brain and cardiac MRIs are fundamental noninvasive imaging tools, which can provide important clinical information and can be performed without or with gadolinium-based contrast agents (GBCAs), depending on the clinical indication. It is currently a topic of debate whether it would be feasible to extract information such as standard gadolinium-enhanced MRI while injecting either less or no GBCAs. Artificial intelligence (AI) is a great source of innovation in medical imaging and has been explored as a method to synthesize virtual contrast MR images, potentially yielding similar diagnostic performance without the need to administer GBCAs. If possible, there would be significant benefits, including reduction of costs, acquisition time, and environmental impact with respect to conventional contrast-enhanced MRI examinations. Given its promise, we believe additional research is needed to increase the evidence to make these AI solutions feasible, reliable, and robust enough to be integrated into the clinical framework. Here, we review recent AI studies aimed at reducing or replacing gadolinium in brain and cardiac imaging while maintaining diagnostic image quality.

Deep-learning based detection of vessel occlusions on CT-angiography in patients with suspected acute ischemic stroke.

Swift diagnosis and treatment play a decisive role in the clinical outcome of patients with acute ischemic stroke (AIS), and computer-aided diagnosis (CAD) systems can accelerate the underlying diagnostic processes. Here, we developed an artificial neural network (ANN) which allows automated detection of abnormal vessel findings without any a-priori restrictions and in <2 minutes. Pseudo-prospective external validation was performed in consecutive patients with suspected AIS from 4 different hospitals during a 6-month timeframe and demonstrated high sensitivity (≥87%) and negative predictive value (≥93%). Benchmarking against two CE- and FDA-approved software solutions showed significantly higher performance for our ANN with improvements of 25-45% for sensitivity and 4-11% for NPV (p ≤ 0.003 each). We provide an imaging platform ( https://stroke.neuroAI-HD.org ) for online processing of medical imaging data with the developed ANN, including provisions for data crowdsourcing, which will allow continuous refinements and serve as a blueprint to build robust and generalizable AI algorithms.

Pre-examinations Improve Automated Metastases Detection on Cranial MRI.

MATERIALS AND METHODS: Our local ethics committee approved this retrospective monocenter study.First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion of also the native T1-weighted images, T2-weighted images, and FLAIR sequences of both time points (CNNdual_all).Second, results were compared with the corresponding single time approaches, in which the CNN was provided exclusively the respective sequences of the diagnosis MRI.Casewise diagnostic performance parameters were calculated from 5-fold cross-validation. RESULTS: In total, 94 cases with 494 MMs were included. Overall, the highest diagnostic performance was achieved by inclusion of only the contrast-enhanced T1-weighted images of the diagnosis and of a prediagnosis MRI (CNNdual_ce, sensitivity = 73%, PPV = 25%, F1-score = 36%). Using exclusively contrast-enhanced T1-weighted images as input resulted in significantly less false-positives (FPs) compared with inclusion of further sequences beyond contrast-enhanced T1-weighted images (FPs = 5/7 for CNNdual_ce/CNNdual_all, P < 1e-5). Comparison of contrast-enhanced dual and mono time approaches revealed that exclusion of prediagnosis MRI significantly increased FPs (FPs = 5/10 for CNNdual_ce/CNNce, P < 1e-9).Approaches with only native sequences were clearly inferior to CNNs that were provided contrast-enhanced sequences. CONCLUSIONS: Automated MM detection on contrast-enhanced T1-weighted images performed with high sensitivity. Frequent FPs due to artifacts and vessels were significantly reduced by additional inclusion of prediagnosis MRI, but not by inclusion of further sequences beyond contrast-enhanced T1-weighted images. Future studies might investigate different change detection architectures for computer-aided detection.

Assessment of Sodium MRI at 7 Tesla as Predictor of Therapy Response and Survival in Glioblastoma Patients.

The purpose of this work was to prospectively investigate sodium (23Na) MRI at 7 Tesla (T) as predictor of therapy response and survival in patients with glioblastoma (GBM). Thus, 20 GBM patients underwent 23Na MRI at 7T before, immediately after and 6 weeks after chemoradiotherapy (CRT). The median tissue sodium concentration (TSC) inside the whole tumor excluding necrosis was determined. Initial response to CRT was assessed employing the updated response assessment in neuro-oncology working group (RANO) criteria. Clinical parameters, baseline TSC and longitudinal TSC differences were compared between patients with initial progressive disease (PD) and patients with initial stable disease (SD) using Fisher's exact tests and Mann-Whitney-U-tests. Univariate proportional hazard models for progression free survival (PFS) and overall survival (OS) were calculated using clinical parameters and TSC metrics as predictor variables. The analyses demonstrated that TSC developed heterogeneously over all patients following CRT. None of the TSC metrics differed significantly between cases of initial SD and initial PD. Furthermore, TSC metrics did not yield a significant association with PFS or OS. Conversely, the initial response according to the RANO criteria could significantly predict PFS [univariate HR (95%CI) = 0.02 (0.0001-0.21), p < 0.001] and OS [univariate HR = 0.17 (0.04-0.65), p = 0.005]. In conclusion, TSC showed treatment-related changes in GBM following CRT, but did not significantly correlate with the initial response according to the RANO criteria, PFS or OS. In contrast, the initial response according to the RANO criteria was a significant predictor of PFS and OS. Future investigations need to elucidate the reasons for treatment-related changes in TSC and their clinical value for response prediction in glioblastoma patients receiving CRT.

Ultra-high-field sodium MRI as biomarker for tumor extent, grade and IDH mutation status in glioma patients.

PURPOSE: This prospective clinical trial investigated sodium (23Na) MRI at 7 Tesla (T) field strength as biomarker for tumor extent, isocitrate dehydrogenase (IDH) mutation and O6-methylguanine DNA methyltransferase (MGMT) promotor methylation in glioma patients. METHODS: 28 glioma patients underwent 23Na MRI on a 7T scanner (Siemens Healthcare, Erlangen, Germany) parallel to standard 3T MRI before chemoradiation. Areas of Gadolinium-contrast enhancement (gdce), non-enhancing T2-hyperintensity (regarded as edema), necrosis, and normal-appearing white matter (nawm) were segmented on 3T MRI imaging and were co-registered with the 23Na images. The median total 23Na concentrations of all areas were compared by pairwise t-tests. Furthermore, areas of gdce and edema were merged to yield the whole tumor area without necrosis. Subsequently, the difference in median of the 23Na concentration of this whole tumor area was compared between IDH-mutated and IDH wild-type gliomas as well as MGMT methylated and MGMT not-methylated glioblastomas using Whitney-Mann U-tests. All p-values were corrected after the Bonferroni-Holm procedure. RESULTS: The 23Na concentration increased successively from nawm to necrotic areas (mean ± sd: nawm = 37.84 ± 5.87 mM, edema = 54.69 ± 10.64 mM, gdce = 61.72 ± 12.95 mM, necrosis = 81.88 ± 17.53 mM) and the concentrations differed statistically significantly between all regarded areas (adjusted p-values for all pairwise comparisons < 0.05). Furthermore, IDH-mutated gliomas showed significantly higher 23Na concentrations than IDH wild-type gliomas (median [interquartile range]: IDH wild-type = 52.37 mM [45.98 - 58.56 mM], IDH mutated = 65.02 mM [58.87-67.05 mM], p = 0.039). Among the glioblastomas, there was a trend towards increased 23Na concentration in MGMT methylated tumors that did not reach statistical significance (median [interquartile range]: MGMT methylated = 57.59 mM [50.70 - 59.17 mM], MGMT not methylated = 48.78 mM [45.88 - 53.91 mM], p = 1.0). CONCLUSIONS: 23Na MRI correlates with the IDH mutation status and could therefore enhance image guidance towards biopsy sites as wells as image-guided surgery and radiotherapy. Furthermore, the successive decrease of 23Na concentration from central necrosis to normal-appearing white matter suggests a correlation with tumor infiltration.

Is Small Fiber Neuropathy Induced by Gadolinium-Based Contrast Agents?

OBJECTIVES: In recent years, complaints of patients about burning pain in arms and legs after the injection of gadolinium-based contrast agents (GBCAs) have been reported. In the current study, we investigated changes of small fibers in the epidermis as a potential cause of the patient complaints in a mouse model. METHODS: Six groups of 8 mice were intravenously injected with either a macrocyclic GBCA (gadoteridol, gadoterate meglumine, gadobutrol), a linear GBCA (gadodiamide or gadobenate dimeglumine) (1 mmol/kg body weight), or saline (NaCl 0.9%). Four weeks after injection, animals were euthanized, and footpads were assessed using immunofluorescence staining. Intraepidermal nerve fiber density (IENFD) was calculated, and the median number of terminal axonal swellings (TASs) per IENFD was determined. RESULTS: Nonparametric Wilcoxon signed-rank test revealed significantly lower IENFDs for all GBCAs compared with the control group (P < 0.0001) with the linear GBCAs showing significantly lower IENFDs than the macrocyclic GBCAs (P < 0.0001). The linear GBCAs presented significantly more TAS per IENFD than the control group (P < 0.0001), whereas no significant increase of TAS per IENFD compared with the control group was found for macrocyclic GBCAs (P < 0.237). INTERPRETATION: It is unclear whether or at what dosage the decrease of IENFDs and the increase of TAS per IENFD found in the current animal model will appear in humans and if it translates into clinical symptoms. However, given the highly significant findings of the current study, more research in this field is required.

No Changes in T1 Relaxometry After a Mean of 11 Administrations of Gadobutrol.

OBJECTIVES: Quantitative T1 relaxometry is the benchmark in imaging potential gadolinium deposition and known to be superior to semiquantitative signal intensity ratio analyses. However, T1 relaxometry studies are rare, commonly limited to a few target structures, and reported results are inconsistent.We systematically investigated quantitative T1 relaxation times (qT1) of a variety of brain nuclei after serial application of gadobutrol. MATERIALS AND METHODS: Retrospectively, qT1 measurements were performed in a patient cohort with a mean number of 11 gadobutrol applications (n = 46) and compared with a control group with no prior gadolinium-based contrast agent administration (n = 48). The following target structures were evaluated: dentate nucleus, globus pallidus, thalamus, hippocampus, putamen, caudate, amygdala, and different white matter areas. Subsequently, multivariate regression analysis with adjustment for age, presence of brain metastases and previous cerebral radiotherapy was performed. RESULTS: No assessed site revealed a significant correlation between qT1 and number of gadobutrol administrations in multivariate regression analysis. However, a significant negative correlation between qT1 and age was found for the globus pallidus as well as anterior and lateral thalamus (P < 0.05 each). CONCLUSIONS: No T1 relaxation time shortening due to gadobutrol injection was found in any of the assessed brain structures after serial administration of 11 doses of gadobutrol.

Relaxation-compensated CEST (chemical exchange saturation transfer) imaging in breast cancer diagnostics at 7T.

PURPOSE: To investigate whether fat-corrected and relaxation-compensated amide proton transfer (APT) and guanidyl CEST-MRI enables the detection of signal intensity differences between breast tumors and normal-appearing fibroglandular tissue in patients with newly-diagnosed breast cancer. METHOD: Ten patients with newly-diagnosed breast cancer and seven healthy volunteers were included in this prospective IRB-approved study. CEST-MRI was performed on a 7 T-whole-body scanner followed by a multi-Lorentzian fit analysis. APT and guanidyl CEST signal intensities were quantified in the tumor and in healthy fibroglandular tissue after correction of B0/B1-field inhomogeneities, fat signal contribution, T1- and T2-relaxation; signal intensity differences of APT and guanidyl resonances were compared using Mann-Whitney-U-tests. Pearson correlations between tumor CEST signal intensities and the proliferation index Ki-67 were performed. RESULTS: APT CEST signal in tumor tissue (6.70 ±â€¯1.38%Hz) was increased compared to normal-appearing fibroglandular tissue of patients (3.56 ±â€¯0.54%Hz, p = 0.001) and healthy volunteers (3.70 ±â€¯0.68%Hz, p = 0.001). Further, a moderate positive correlation was found between the APT signal and the proliferation index Ki-67 (R2 = 0.367, r = 0.606, p = 0.11). Guanidyl CEST signal was also increased in tumor tissue (5.24 ±â€¯1.85%Hz) compared to patients' (2.42 ±â€¯0.45%Hz, p = 0.006) and volunteers' (2.36 ±â€¯0.54%Hz, p < 0.001) normal-appearing fibroglandular tissue and a positive correlation with the Ki-67 level was observed (R2 = 0.365, r = 0.604, p = 0.11). APT and guanidyl CEST signal in normal-appearing fibroglandular tissue was not different between patients and healthy volunteers (p = 0.88; p = 0.93). CONCLUSION: Relaxation-compensated and fat-corrected CEST-MRI allowed a non-invasive differentiation of breast cancer and normal-appearing breast tissue. Thus, this approach represents a contrast agent-free method that may help to increase diagnostic accuracy in MR-mammography.

Glymphatic Pathway of Gadolinium-Based Contrast Agents Through the Brain: Overlooked and Misinterpreted.

BACKGROUND: The "glymphatic system" (GS), a brain-wide network of cerebrospinal fluid microcirculation, supplies a pathway through and out of the central nervous system (CNS); malfunction of the system is implicated in a variety of neurological disorders. In this exploratory study, we analyzed the potential of a new imaging approach that we coined delayed T2-weighted gadolinium-enhanced imaging to visualize the GS in vivo. METHODS: Heavily T2-weighted fluid-attenuated inversion recovery (hT2w-FLAIR) magnetic resonance imaging was obtained before, and 3 hours and 24 hours after intravenous gadolinium-based contrast agent (GBCA) application in 33 neurologically healthy patients and 7 patients with an impaired blood-brain barrier (BBB) due to cerebral metastases. Signal intensity (SI) was determined in various cerebral fluid spaces, and white matter hyperintensities were quantified by applying the Fazekas scoring system. FINDINGS: Delayed hT2w-FLAIR showed GBCA entry into the CNS via the choroid plexus and the ciliary body, with GBCA drainage along perineural sheaths of cranial nerves and along perivascular spaces of penetrating cortical arteries. In all patients and all sites, a significant SI increase was found for the 3 hours and 24 hours time points compared with baseline. Although no significant difference in SI was found between neurologically healthy patients and patients with an impaired BBB, a significant positive correlation between Fazekas scoring system and SI increase in the perivascular spaces 3 hours post injection was shown. INTERPRETATION: Delayed T2-weighted gadolinium-enhanced imaging can visualize the GBCA pathway into and through the GS. Presence of GBCAs within the GS might be regarded as part of the natural excretion process and should not be mixed up with gadolinium deposition. Rather, the correlation found between deep white matter hyperintensities, an imaging sign of vascular dementia, and GS functioning demonstrated feasibility to exploit the pathway of GBCAs through the GS for diagnostic purposes.

Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study.

OBJECTIVES: Gadolinium-based contrast agents (GBCAs) have become an integral part in daily clinical decision making in the last 3 decades. However, there is a broad consensus that GBCAs should be exclusively used if no contrast-free magnetic resonance imaging (MRI) technique is available to reduce the amount of applied GBCAs in patients. In the current study, we investigate the possibility of predicting contrast enhancement from noncontrast multiparametric brain MRI scans using a deep-learning (DL) architecture. MATERIALS AND METHODS: A Bayesian DL architecture for the prediction of virtual contrast enhancement was developed using 10-channel multiparametric MRI data acquired before GBCA application. The model was quantitatively and qualitatively evaluated on 116 data sets from glioma patients and healthy subjects by comparing the virtual contrast enhancement maps to the ground truth contrast-enhanced T1-weighted imaging. Subjects were split in 3 different groups: enhancing tumors (n = 47), nonenhancing tumors (n = 39), and patients without pathologic changes (n = 30). The tumor regions were segmented for a detailed analysis of subregions. The influence of the different MRI sequences was determined. RESULTS: Quantitative results of the virtual contrast enhancement yielded a sensitivity of 91.8% and a specificity of 91.2%. T2-weighted imaging, followed by diffusion-weighted imaging, was the most influential sequence for the prediction of virtual contrast enhancement. Analysis of the whole brain showed a mean area under the curve of 0.969 ± 0.019, a peak signal-to-noise ratio of 22.967 ± 1.162 dB, and a structural similarity index of 0.872 ± 0.031. Enhancing and nonenhancing tumor subregions performed worse (except for the peak signal-to-noise ratio of the nonenhancing tumors). The qualitative evaluation by 2 raters using a 4-point Likert scale showed good to excellent (3-4) results for 91.5% of the enhancing and 92.3% of the nonenhancing gliomas. However, despite the good scores and ratings, there were visual deviations between the virtual contrast maps and the ground truth, including a more blurry, less nodular-like ring enhancement, few low-contrast false-positive enhancements of nonenhancing gliomas, and a tendency to omit smaller vessels. These "features" were also exploited by 2 trained radiologists when performing a Turing test, allowing them to discriminate between real and virtual contrast-enhanced images in 80% and 90% of the cases, respectively. CONCLUSIONS: The introduced model for virtual gadolinium enhancement demonstrates a very good quantitative and qualitative performance. Future systematic studies in larger patient collectives with varying neurological disorders need to evaluate if the introduced virtual contrast enhancement might reduce GBCA exposure in clinical practice.