Flatness of the Meckel cave may cause primary trigeminal neuralgia: a radiomics-based study

Background Neurovascular contact (NVC) is the main cause of primary trigeminal neuralgia (PTN); however, cases of PTN without NVC are still observed. In this study, the Meckel cave (MC) morphology in PTN were analyzed by radiomics and compared to healthy controls (HCs) to explore the cause of PTN. Methods We studied the 3.0T MRI data of 115 patients with PTN and 46 HCs. Bilateral MC was modeled using the 3D Slicer software, and the morphological characteristics of MC were analyzed using the radiomics method. Results The right side incidence rate in the PTN group was higher than the left side incidence. By analyzing the flatness feature of MC, we observed that the affected side of the PTN was lower than that of the unaffected side, the right MC of the PTN and HC was lower than that of the left MC, the MC of the affected side of the left and right PTN without bilateral NVC was lower than that of the unaffected side. Conclusions By providing a method to analyze the morphology of the MC, we found that there is an asymmetry in the morphology of bilateral MC in the PTN and HC groups. It can be inferred that the flatness of the MC may be a cause of PTN.

Flatness of the Meckel Cave May Cause Primary Trigeminal Neuralgia: a Radiomics-based Study

Background: Neurovascular contact (NVC) is the main cause of primary trigeminal neuralgia (PTN); however, cases of PTN without NVC are still observed. In this study, the Meckel cave (MC) morphology in PTN were analyzed by radiomics and compared to healthy controls (HCs) to explore the cause of PTN.Methods: We studied the 3.0T MRI data of 115 patients with PTN and 46 HCs. Bilateral MC was modeled using the 3Dslicer software, and the morphological characteristics of MC were analyzed using the radiomics method.Results: The right side incidence rate in the PTN group was higher than the left side incidence. By analyzing the flatness feature of MC, we observed that the affected side of the PTN was lower than that of the unaffected side, the right MC of the PTN and HC was lower than that of the left MC, the MC of the affected side of the left and right PTN without bilateral NVC was lower than that of the unaffected side.Conclusions: By providing a method to analyze the morphology of MC, we found that there is asymmetry in the morphology of bilateral MC in the PTN and HC groups. It can be inferred that the flatness of MC may be a cause of PTN.

A Comparison of the Distortion in the Same Field MRI and MR-Linac System With a 3D Printed Phantom

PurposeA 3D printed geometric phantom was developed that can be scanned with computed tomography (CT) and magnetic resonance imaging (MRI) to measure the geometric distortion and determine the relevant dose changes.Materials and MethodsA self-designed 3D printed photosensitive resin phantom was used, which adopts grid-like structures and has 822 1 cm2 squares. The scanning plan was delivered by three MRI scanners: the Elekta Unity MR-Linac 1.5T, GE Signa HDe 1.5T, and GE Discovery-sim 750 3.0T. The geometric distortion comparison was concentrated on two 1.5T MRI systems, whereas the 3.0T MRI was used as a supplemental experiment. The most central transverse images in each dataset were selected to demonstrate the plane distortion. Some mark points were selected to analyze the distortion in the 3D direction based on the plane geometric distortion. A treatment plan was created with the off-line Monaco system.ResultsThe distortion increases gradually from the center to the outside. The distortion range is 0.79 ± 0.40 mm for the Unity, 1.31 ± 0.56 mm for the GE Signa HDe, and 2.82 ± 1.48 mm for the GE Discovery-sim 750. Additionally, the geometric distortion slightly affects the actual planning dose of the radiotherapy.ConclusionGeometric distortion increases gradually from the center to the outside. The distortion values of the Unity were smaller than those of the GE Signa HDe, and the Unity has the smallest geometric distortion. Finally, the Unity’s dose variation best matched with the standard treatment plan.

Whole brain 3D MR fingerprinting in multiple sclerosis: a pilot study

Background MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis. Methods Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in isotropic voxels (1 × 1 × 1 mm3) and a total acquisition time of 4 min 38 s. Data were collected on 18 subjects paired with 18 controls. Regions of interest were drawn over MRF-derived T1 relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T1 and T2 relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms. Partial least squares discriminant analysis was performed to discriminate NAWM and Splenium in MS compared with controls. Results Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65 % (p = 0.21) and approached 90 % (p < 0.01) for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p = 0.015), minimum T1 (p = 0.03) and negative correlation with splenium uniformity (p = 0.04). Perfect discrimination (AUC = 1) was achieved between selected features from MS lesions and F-NAWM. Conclusions 3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.

POS1391 MEASURING SUBCLINICAL INFLAMMATION IN HAND AND FOREFOOT IN PATIENTS WITH ARTHRALGIA USING 1.5T OR 3.0T MRI: DOES FIELD STRENGTH MATTER?

Background:Magnetic resonance imaging (MRI) of small joints sensitively detects inflammation. MRI-detected subclinical inflammation, and tenosynovitis in particular, has been shown predictive for RA development in patients with arthralgia. These scientific data are mostly acquired on 1.0T-1.5T MRI scanners. However, 3.0T MRI is nowadays increasingly used in practice. Evidence on the comparability of these field strengths is scarce and it has never been studied in arthralgia where subclinical inflammation is subtle. Moreover, comparisons never included tenosynovitis, which is, of all imaging features, the strongest predictor for progression to RA.Objectives:To determine if there is a difference between 1.5T and 3.0T MRI in detecting subclinical inflammation in arthralgia patients.Methods:2968 locations (joints, bones or tendon sheaths) in hands and forefeet of 28 arthralgia patients were imaged on both 1.5T and 3.0T MRI. Two independent readers scored for erosions, osteitis, synovitis (according to RAMRIS) and tenosynovitis (as described by Haavaardsholm et al.). Scores were also summed as total inflammation (osteitis, synovitis and tenosynovitis) and total RAMRIS (erosions, osteitis, synovitis and tenosynovitis) scores. Interreader reliability (comparing both readers) and field strength agreement (comparing 1.5T and 3.0T) was assessed with interclass correlation coefficients (ICCs). Next, field strength agreement was assessed after dichotomization into presence or absence of inflammation. Analyses were performed on patient- and location-level.Results:ICCs between readers were excellent (>0.90). Comparing 1.5 and 3.0T revealed excellent ICCs of 0.90 (95% confidence interval 0.78-0.95) for the total inflammation score and 0.90 (0.78-0.95) for the total RAMRIS score. ICCs for individual inflammation features were: tenosynovitis: 0.87 (0.74-0.94), synovitis 0.65 (0.24-0.84) and osteitis 0.96 (0.91-0.98). The field strength agreement on dichotomized scores was 83% for the total inflammation score and 89% for the total RAMRIS score. Of the individual features, agreement for tenosynovitis was the highest (89%). Analyses on location- level showed similar results.Conclusion:Agreement of subclinical inflammation scores on 1.5T and 3.0T were good to excellent, in particular for tenosynovitis. This suggests that scientific evidence on predictive power of MRI in arthralgia patients, obtained on 1.5T, can be generalized to 3.0T when this field strength would be used for diagnostic purposes in daily practice.Disclosure of Interests:None declared

Whole Brain 3D MR Fingerprinting in Multiple Sclerosis: A Pilot Investigation

Background: MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis.Methods: Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in isotropic voxels (1x1x1mm3) and a total acquisition time of 4min 38s. Data were collected on 18 subjects paired with 18 controls. Regions of interested were drawn over MRF-derived T1 relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T1 and T2 relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms (T-SNE). Partial least squares discriminant analysis (PLS-DA) was performed to discriminate NAWM and Splenium in MS compared with controls. Results: Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65% and approached 90% for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p=0.015), minimum T1 (p=0.03) and negative correlation with splenium uniformity (p=0.04). Perfect discrimination (AUC=1) was achieved between selected features from MS lesions and F-NAWM.Conclusions: 3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.