In vivo assessment of artefacts in MRI images caused by conventional twistflex and various fixed orthodontic CAD/CAM retainers.

PURPOSE: To assess magnetic resonance imaging (MRI) artefacts caused by different computer-aided design/computer-aided manufacturing (CAD/CAM) retainers in comparison with conventional hand bent stainless steel twistflex retainers in vivo. MATERIALS AND METHODS: MRI scans (3 Tesla) were performed on a male volunteer with different CAD/CAM retainers (cobalt-chromium, CoCr; nickel-titanium, NiTi; grade 5 titanium, Ti5) and twistflex retainers inserted. A total of 126 landmarks inside and outside the retainer area (RA; from canine to canine) were evaluated by two blinded radiologists using an established five-point visibility scoring (1: excellent, 2: good, 3: moderate, 4: poor, 5: not visible). Friedman and two-tailed Wilcoxon tests were used for statistical analysis (significance level: p < 0.05). RESULTS: Twistflex retainers had the strongest impact on the visibility of all landmarks inside (4.0 ± 1.5) and outside the RA (1.7 ± 1.2). In contrast, artefacts caused by CAD/CAM retainers were limited to the dental area inside the RA (CoCr: 2.2 ± 1.2) or did not impair MRI-based diagnostics in a clinically relevant way (NiTi: 1.0 ± 0.1; Ti5: 1.4 ± 0.6). CONCLUSION: The present study on a single test person demonstrates that conventional stainless steel twistflex retainers can severely impair the diagnostic value in head/neck and dental MRI. By contrast, CoCr CAD/CAM retainers can cause artefacts which only slightly impair dental MRI but not head/neck MRI, whereas NiTi and Ti5 CAD/CAM might be fully compatible with both head/neck and dental MRI.

Quantification and Proximal-to-Distal Distribution Pattern of Tibial Nerve Lesions in Relapsing-Remitting Multiple Sclerosis : Assessment by MR Neurography.

PURPOSE: Recent studies suggest an involvement of the peripheral nervous system (PNS) in multiple sclerosis (MS). Here, we characterize the proximal-to-distal distribution pattern of peripheral nerve lesions in relapsing-remitting MS (RRMS) by quantitative magnetic resonance neurography (MRN). METHODS: A total of 35 patients with RRMS were prospectively included and underwent detailed neurologic and electrophysiologic examinations. Additionally, 30 age- and sex-matched healthy controls were recruited. 3T MRN with anatomical coverage from the proximal thigh down to the tibiotalar joint was conducted using dual-echo 2­dimensional relaxometry sequences with spectral fat saturation. Quantification of PNS involvement was performed by evaluating microstructural (proton spin density (ρ), T2-relaxation time (T2app)), and morphometric (cross-sectional area, CSA) MRN markers in every axial slice. RESULTS: In patients with RRMS, tibial nerve lesions at the thigh and the lower leg were characterized by a decrease in T2app and an increase in ρ compared to controls (T2app thigh: p < 0.0001, T2app lower leg: p = 0.0040; ρ thigh: p < 0.0001; ρ lower leg: p = 0.0098). An additional increase in nerve CSA was only detectable at the thigh, while the semi-quantitative marker T2w-signal was not altered in RRMS in both locations. A slight proximal-to-distal gradient was observed for T2app and T2-signal, but not for ρ. CONCLUSION: PNS involvement in RRMS is characterized by a decrease in T2app and an increase in ρ, occurring with proximal predominance at the thigh and the lower leg. Our results indicate microstructural alterations in the extracellular matrix of peripheral nerves in RRMS and may contribute to a better understanding of the pathophysiologic relevance of PNS involvement.

Endodontic working length measurements of premolars and molars in high-resolution dental MRI: a clinical pilot study for assessment of reliability and accuracy.

OBJECTIVES: To prospectively assess the reliability and accuracy of high-resolution, dental MRI (dMRI) for endodontic working length (WL) measurements of premolars and molars under clinical conditions. MATERIALS AND METHODS: Three-Tesla dMRI was performed in 9 subjects who also had undergone cone-beam computed tomography (CBCT) (mean age: 47 ± 13.5 years). A total of 34 root canals from 12 molars (4/8, upper/lower jaw; 22 root canals) and 11 premolars (2/9 upper/lower jaw; 12 root canals) were included. CBCT and dMRI datasets were reconstructed to visualize the root canal in one single slice. Subsequently, two radiologists measured the root canal lengths in both modalities twice in blinded fashion. Reliability and accuracy for both modalities were assessed using intraclass correlation coefficients (ICCs) and Bland-Altman analysis, respectively. RESULTS: Reliability (intra-rater I/II; inter-rater) of dental MRI measurements was excellent and comparable to CBCT for premolars (0.993/0.900; 0.958 vs. 0.993/0.956; 0.951) and for molars (0.978/0.995; 0.986 vs. 0.992/0.996; 0.989). Bland-Altman analysis revealed a mean underestimation/bias (95% confidence interval) of dMRI measurements of 0.8 (- 1.44/3.05) mm for premolars and 0.4 (- 1.55/2.39) mm for molars. In up to 59% of the cases, the accuracy of dMRI for WL measurements was within the underestimation margin of 0 to 2 mm short of the apical foramen AF. CONCLUSIONS: In vivo demonstration and measurement of WL are feasible using dMRI. The reliability of measurements is high and equivalent to CBCT. Nonetheless, due to lower spatial resolution and longer acquisition time, the accuracy of dMRI is inferior to CBCT, impeding its current use for clinical treatment planning. CLINICAL RELEVANCE: dMRI is a promising radiation-free imaging technique. Its reliability for endodontic working length measurements is high, but its accuracy is not satisfactory enough yet.

Quantitative assessment of contrast-enhancement patterns of the healthy dental pulp by magnetic resonance imaging: A prospective in vivo study.

AIM: This prospective in vivo study aimed to optimize the assessment of pulpal contrast-enhancement (PCE) on dental magnetic resonance imaging (dMRI) and investigate physiological PCE patterns. METHODOLOGY: In 70 study participants, 1585 healthy teeth were examined using 3-Tesla dMRI before and after contrast agent administration. For all teeth, the quotient of post- and pre-contrast pulp signal intensity (Q-PSI) was calculated to quantify PCE. First, pulp chambers were analysed in 10 participants to compare the coefficient of variation of mean versus maximum Q-PSI values (Q-PSImean versus Q-PSImax ). Second, dynamic PCE was evaluated in 10 subjects to optimize the time interval between contrast agent application and image acquisition. Finally, 50 participants (age groups: 20-29, 30-39, 40-49, 50-59 and 60-69 years) were examined to analyse age, gender, tooth types and maxilla versus mandible as independent factors of PCE. Statistical analysis was performed using Wilcoxon signed rank test and linear mixed models. RESULTS: PCE assessment based on Q-PSImax was associated with a significantly smaller coefficient of variation compared with Q-PSImean , with median values of 0.17 versus 0.21 (p = .002). Analysis of dynamic PCE revealed an optimal timing interval for image acquisition 4 min after contrast media application. No significant differences in PCE were observed by comparing age groups, female versus male participants and maxillary versus mandibular teeth (p > .05). Differences between tooth types were small (median Q-PSImax values of 2.52/2.32/2.30/2.20 for molars/premolars/canines/incisors) but significant (p < .05), except for the comparison of canines versus premolars (p = .80). CONCLUSIONS: PCE in dMRI was a stable intra-individual marker with only minor differences between different tooth types, thus forming an important basis for intra-individual controls when assessing teeth with suspected endodontic pathosis. Furthermore, it was demonstrated that PCE is independent of age, gender and jaw type. These findings indicate that dMRI-based PCE analysis could be a valuable diagnostic tool for the identification of various pulp diseases in future patient studies.

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after repeated GBCA administration with yet unknown clinical significance. We aimed to assess the feasibility and diagnostic value of synthetic post-contrast T1-weighted MRI generated from pre-contrast MRI sequences through deep convolutional neural networks (dCNN) for tumour response assessment in neuro-oncology. METHODS: In this multicentre, retrospective cohort study, we used MRI examinations to train and validate a dCNN for synthesising post-contrast T1-weighted sequences from pre-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery sequences. We used MRI scans with availability of these sequences from 775 patients with glioblastoma treated at Heidelberg University Hospital, Heidelberg, Germany (775 MRI examinations); 260 patients who participated in the phase 2 CORE trial (1083 MRI examinations, 59 institutions); and 505 patients who participated in the phase 3 CENTRIC trial (3147 MRI examinations, 149 institutions). Separate training runs to rank the importance of individual sequences and (for a subset) diffusion-weighted imaging were conducted. Independent testing was performed on MRI data from the phase 2 and phase 3 EORTC-26101 trial (521 patients, 1924 MRI examinations, 32 institutions). The similarity between synthetic and true contrast enhancement on post-contrast T1-weighted MRI was quantified using the structural similarity index measure (SSIM). Automated tumour segmentation and volumetric tumour response assessment based on synthetic versus true post-contrast T1-weighted sequences was performed in the EORTC-26101 trial and agreement was assessed with Kaplan-Meier plots. FINDINGS: The median SSIM score for predicting contrast enhancement on synthetic post-contrast T1-weighted sequences in the EORTC-26101 test set was 0·818 (95% CI 0·817-0·820). Segmentation of the contrast-enhancing tumour from synthetic post-contrast T1-weighted sequences yielded a median tumour volume of 6·31 cm3 (5·60 to 7·14), thereby underestimating the true tumour volume by a median of -0·48 cm3 (-0·37 to -0·76) with the concordance correlation coefficient suggesting a strong linear association between tumour volumes derived from synthetic versus true post-contrast T1-weighted sequences (0·782, 0·751-0·807, p<0·0001). Volumetric tumour response assessment in the EORTC-26101 trial showed a median time to progression of 4·2 months (95% CI 4·1-5·2) with synthetic post-contrast T1-weighted and 4·3 months (4·1-5·5) with true post-contrast T1-weighted sequences (p=0·33). The strength of the association between the time to progression as a surrogate endpoint for predicting the patients' overall survival in the EORTC-26101 cohort was similar when derived from synthetic post-contrast T1-weighted sequences (hazard ratio of 1·749, 95% CI 1·282-2·387, p=0·0004) and model C-index (0·667, 0·622-0·708) versus true post-contrast T1-weighted MRI (1·799, 95% CI 1·314-2·464, p=0·0003) and model C-index (0·673, 95% CI 0·626-0·711). INTERPRETATION: Generating synthetic post-contrast T1-weighted MRI from pre-contrast MRI using dCNN is feasible and quantification of the contrast-enhancing tumour burden from synthetic post-contrast T1-weighted MRI allows assessment of the patient's response to treatment with no significant difference by comparison with true post-contrast T1-weighted sequences with administration of GBCAs. This finding could guide the application of dCNN in radiology to potentially reduce the necessity of GBCA administration. FUNDING: Deutsche Forschungsgemeinschaft.

Assessment of Peripheral Nervous System Alterations in Patients with the Fabry Related GLA-Variant p.A143T.

Background The purpose of this study is to examine alterations of the peripheral nervous system (PNS) in oligo-symptomatic patients carrying the Fabry related GLA-gene variant p.A143T by Magnetic Resonance Neurography (MRN) and skin biopsy. This prospective study assessed dorsal root ganglia (DRG) volume L3 to S2, vascular permeability of the DRG L5, S1, and the spinal nerve L5 in five patients carrying p.A143T in comparison to patients with classical Fabry mutations and healthy controls. Moreover, skin punch biopsies above the lateral malleolus of the right foot were obtained in four patients and intraepidermal nerve fiber density (IENFD) was counted individually. Compared to controls, DRG volumes of p.A143T patients were enlarged by 30% (L3, p < 0.05), 35% (L4, p < 0.05), 29% (L5, p = 0.15), 36% (S1, p < 0.01), and 18% (S2, p < 0.05), but less pronounced compared to patients carrying a classical Fabry mutation. Compared to healthy controls, vascular permeability was decreased by 40% (L5 right), 49% (L5 left), 48% (S1 right), and 49% (S1) (p < 0.01-p < 0.001), but non-significant less than patients carrying a classical Fabry mutation. Compared to sex-matched 5% lower normative reference values per decade, IENFD was decreased in three of four patients. MRN and determination of IENFD is able to detect early alteration of the PNS segment in oligo-symptomatic patients with the disease-modifying GLA-variant p.A143T on an individual basis. This procedure might also help in further GLA-variants of uncertain significance for early identification of patients with single major organ manifestation.

Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis.

OBJECTIVES: Patients with multiple sclerosis (MS) regularly undergo MRI for assessment of disease burden. However, interpretation may be time consuming and prone to intra- and interobserver variability. Here, we evaluate the potential of artificial neural networks (ANN) for automated volumetric assessment of MS disease burden and activity on MRI. METHODS: A single-institutional dataset with 334 MS patients (334 MRI exams) was used to develop and train an ANN for automated identification and volumetric segmentation of T2/FLAIR-hyperintense and contrast-enhancing (CE) lesions. Independent testing was performed in a single-institutional longitudinal dataset with 82 patients (266 MRI exams). We evaluated lesion detection performance (F1 scores), lesion segmentation agreement (DICE coefficients), and lesion volume agreement (concordance correlation coefficients [CCC]). Independent evaluation was performed on the public ISBI-2015 challenge dataset. RESULTS: The F1 score was maximized in the training set at a detection threshold of 7 mm3 for T2/FLAIR lesions and 14 mm3 for CE lesions. In the training set, mean F1 scores were 0.867 for T2/FLAIR lesions and 0.636 for CE lesions, as compared to 0.878 for T2/FLAIR lesions and 0.715 for CE lesions in the test set. Using these thresholds, the ANN yielded mean DICE coefficients of 0.834 and 0.878 for segmentation of T2/FLAIR and CE lesions in the training set (fivefold cross-validation). Corresponding DICE coefficients in the test set were 0.846 for T2/FLAIR lesions and 0.908 for CE lesions, and the CCC was ≥ 0.960 in each dataset. CONCLUSIONS: Our results highlight the capability of ANN for quantitative state-of-the-art assessment of volumetric lesion load on MRI and potentially enable a more accurate assessment of disease burden in patients with MS. KEY POINTS: • Artificial neural networks (ANN) can accurately detect and segment both T2/FLAIR and contrast-enhancing MS lesions in MRI data. • Performance of the ANN was consistent in a clinically derived dataset, with patients presenting all possible disease stages in MRI scans acquired from standard clinical routine rather than with high-quality research sequences. • Computer-aided evaluation of MS with ANN could streamline both clinical and research procedures in the volumetric assessment of MS disease burden as well as in lesion detection.

Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.

BACKGROUND: The Response Assessment in Neuro-Oncology (RANO) criteria and requirements for a uniform protocol have been introduced to standardise assessment of MRI scans in both clinical trials and clinical practice. However, these criteria mainly rely on manual two-dimensional measurements of contrast-enhancing (CE) target lesions and thus restrict both reliability and accurate assessment of tumour burden and treatment response. We aimed to develop a framework relying on artificial neural networks (ANNs) for fully automated quantitative analysis of MRI in neuro-oncology to overcome the inherent limitations of manual assessment of tumour burden. METHODS: In this retrospective study, we compiled a single-institution dataset of MRI data from patients with brain tumours being treated at Heidelberg University Hospital (Heidelberg, Germany; Heidelberg training dataset) to develop and train an ANN for automated identification and volumetric segmentation of CE tumours and non-enhancing T2-signal abnormalities (NEs) on MRI. Independent testing and large-scale application of the ANN for tumour segmentation was done in a single-institution longitudinal testing dataset from the Heidelberg University Hospital and in a multi-institutional longitudinal testing dataset from the prospective randomised phase 2 and 3 European Organisation for Research and Treatment of Cancer (EORTC)-26101 trial (NCT01290939), acquired at 38 institutions across Europe. In both longitudinal datasets, spatial and temporal tumour volume dynamics were automatically quantified to calculate time to progression, which was compared with time to progression determined by RANO, both in terms of reliability and as a surrogate endpoint for predicting overall survival. We integrated this approach for fully automated quantitative analysis of MRI in neuro-oncology within an application-ready software infrastructure and applied it in a simulated clinical environment of patients with brain tumours from the Heidelberg University Hospital (Heidelberg simulation dataset). FINDINGS: For training of the ANN, MRI data were collected from 455 patients with brain tumours (one MRI per patient) being treated at Heidelberg hospital between July 29, 2009, and March 17, 2017 (Heidelberg training dataset). For independent testing of the ANN, an independent longitudinal dataset of 40 patients, with data from 239 MRI scans, was collected at Heidelberg University Hospital in parallel with the training dataset (Heidelberg test dataset), and 2034 MRI scans from 532 patients at 34 institutions collected between Oct 26, 2011, and Dec 3, 2015, in the EORTC-26101 study were of sufficient quality to be included in the EORTC-26101 test dataset. The ANN yielded excellent performance for accurate detection and segmentation of CE tumours and NE volumes in both longitudinal test datasets (median DICE coefficient for CE tumours 0·89 [95% CI 0·86-0·90], and for NEs 0·93 [0·92-0·94] in the Heidelberg test dataset; CE tumours 0·91 [0·90-0·92], NEs 0·93 [0·93-0·94] in the EORTC-26101 test dataset). Time to progression from quantitative ANN-based assessment of tumour response was a significantly better surrogate endpoint than central RANO assessment for predicting overall survival in the EORTC-26101 test dataset (hazard ratios ANN 2·59 [95% CI 1·86-3·60] vs central RANO 2·07 [1·46-2·92]; p<0·0001) and also yielded a 36% margin over RANO (p<0·0001) when comparing reliability values (ie, agreement in the quantitative volumetrically defined time to progression [based on radiologist ground truth vs automated assessment with ANN] of 87% [266 of 306 with sufficient data] compared with 51% [155 of 306] with local vs independent central RANO assessment). In the Heidelberg simulation dataset, which comprised 466 patients with brain tumours, with 595 MRI scans obtained between April 27, and Sept 17, 2018, automated on-demand processing of MRI scans and quantitative tumour response assessment within the simulated clinical environment required 10 min of computation time (average per scan). INTERPRETATION: Overall, we found that ANN enabled objective and automated assessment of tumour response in neuro-oncology at high throughput and could ultimately serve as a blueprint for the application of ANN in radiology to improve clinical decision making. Future research should focus on prospective validation within clinical trials and application for automated high-throughput imaging biomarker discovery and extension to other diseases. FUNDING: Medical Faculty Heidelberg Postdoc-Program, Else Kröner-Fresenius Foundation.

Relevance of T2 signal changes in the assessment of progression of glioblastoma according to the Response Assessment in Neurooncology criteria.

BACKGROUND: According to the Response Assessment in Neurooncology (RANO) criteria, significant nonenhancing signal increase in T2-weighted images qualifies for progression in high-grade glioma (T2-progress), even if there is no change in the contrast-enhancing tumor portion. The purpose of this retrospective study was to assess the frequency of isolated T2-progress and its predictive value on subsequent T1-progress, as determined by a T2 signal increase of 15% or 25%, respectively. The frequency of T2-progress was correlated with antiangiogenic therapy. PATIENTS AND METHODS: MRI follow-up examinations (n = 777) of 144 patients with histologically proven glioblastoma were assessed for contrast-enhanced T1 and T2-weighted images. Examinations were classified as T1-progress, T2-progress with 15% or 25% T2-signal increase, stable disease, or partial or complete response. RESULTS: Thirty-five examinations revealed exclusive T2-progress using the 15% criterion, and only 2 examinations qualified for the 25% criterion; 61.8% of the scans presenting T2-progress and 31.5% of the scans presenting stable disease revealed T1-progress in the next follow-up examination. The χ(2) test showed a highly significant correlation (P < .001) between T2-progress, with the 15% criterion and subsequent T1-progress. No correlation between antiangiogenic therapy and T2-progress was shown. CONCLUSION: Tumor progression, as determined by both contrast-enhanced T1 and T2 sequences is more frequently diagnosed than when considering only contrast-enhanced T1 sequences. Definition of T2-progress by a 15% T2-signal increase criterion is superior to a 25% criterion. The missing correlation of T2-progress and antiangiogenic therapy supports the hypothesis of T2-progress as part of the natural course of the tumor disease.

Minocycline and hypothermia for reperfusion injury after focal cerebral ischemia in the rat: effects on BBB breakdown and MMP expression in the acute and subacute phase.

Reperfusion injury is a complication of recanalization therapies after focal cerebral ischemia. The disruption of the blood-brain barrier (BBB) caused by up-regulated metalloproteinases (MMPs) can lead to edema and hemorrhage. Middle cerebral artery occlusion (MCAO=90 min) and reperfusion (R=24 h vs. 5 days) was induced in male Wistar rats. Rats were randomized in four groups: (1) control (C), (2) twice daily minocycline (30 mg/kg bodyweight) every day (M), (3) hypothermia (33 degrees C) for 4 h starting 60 min after occlusion (H), (4) combination of groups 2 and 3 (MH). Serial MRI was performed regarding infarct evolution and BBB disruption, MMP-2 and MMP-9 were assessed by zymography of serum and ischemic brain tissue, and a functional neuroscore was done at 24 h and 5 days. M and H reduced both infarct sizes, volume and signal intensity of BBB breakdown and improved neuroscore at all points in time to the same extent. This was most likely due to inhibition of MMP-2 and MMP-9. The presence of MMP-9 at 24 h or MMP-2 at 5 days in brain tissue correlated with BBB breakdown whereas serum MMP-2- and -9 showed no relationship with BBB breakdown. The combination MH had a small but not significantly additional effect over the single treatments. Minocycline seems to be as neuroprotective as hypothermia in the acute and subacute phase after cerebral ischemia. One essential mechanism is the inhibition of MMPs. The combination therapy is only slightly superior. The net effect of MMPs inhibition up to 5 days after focal cerebral ischemia is still beneficial.