Practical challenges of continuous real-time functional magnetic resonance imaging neurofeedback with multiband accelerated echo-planar imaging and short repetition times.

Continuous real-time functional magnetic resonance imaging (fMRI) neurofeedback is gaining increasing scientific attention in clinical neuroscience and may benefit from the short repetition times of modern multiband echoplanar imaging sequences. However, minimizing feedback delay can result in technical challenges. Here, we report a technical problem we experienced during continuous fMRI neurofeedback with multiband echoplanar imaging and short repetition times. We identify the possible origins of this problem, describe our current interim solution and provide openly available workflows and code to other researchers in case they wish to use a similar approach.

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.

PETRA, MSVAT-SPACE and SEMAC sequences for metal artefact reduction in dental MR imaging.

OBJECTIVES: Dental MRI is often impaired by artefacts due to metallic dental materials. Several sequences were developed to reduce susceptibility artefacts. Here, we evaluated a set of sequences for artefact reduction for dental MRI for the first time. METHODS: Artefact volume, signal-to-noise ratio (SNR) and image quality were assessed on a 3-T MRI for pointwise encoding time reduction with radial acquisition (PETRA), multiple-slab acquisition with view angle tilting gradient, based on a sampling perfection with application-optimised contrasts using different flip angle evolution (SPACE) sequence (MSVAT-SPACE), slice-encoding for metal-artefact correction (SEMAC) and compared to a standard SPACE and a standard turbo-spin-echo (TSE) sequence. Field-of-view and acquisition times were chosen to enable in vivo application. Two implant-supported prostheses were tested (porcelain fused to metal non-precious alloy and monolithic zirconia). RESULTS: Smallest artefact was measured for TSE sequences with no difference between the standard TSE and the SEMAC. MSVAT-SPACE reduced artefacts about 56% compared to the standard SPACE. Effect of the PETRA was dependent on sample used. Image quality and SNR were comparable for all sequences except PETRA, which yielded poor results. CONCLUSION: There is no benefit in terms of artefact reduction for SEMAC compared to standard TSE. Usage of MSVAT-SPACE is advantageous since artefacts are reduced and higher resolution is achieved. KEY POINTS: • SEMAC is not superior to TSE in terms of artefact reduction. • MSVAT-SPACE reduces susceptibility artefacts while maintaining comparable image quality. • PETRA reduces susceptibility artefacts depending on material but offers poor image quality.

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences.

As the number of magnetic resonance imaging (MRI) scanners and patients with medical implants is constantly growing, radiologists increasingly encounter metallic implant-related artifacts in MRI, resulting in reduced image quality. Therefore, the MRI suitability of implants in terms of artifact volume, as well as the development of pulse sequences to reduce image artifacts, are becoming more and more important. Here, we present a comprehensive protocol which allows for a standardized evaluation of the artifact volume of implants on MRI. Furthermore, this protocol can be used to analyze the vulnerability of different pulse sequences to artifacts. The proposed protocol can be applied to T1- and T2-weighted images with or without fat-suppression and all passive implants. Furthermore, the procedure enables the separate and three-dimensional identification of signal loss and pile-up artifacts. As previous investigations differed greatly in evaluation methods, the comparability of their results was limited. Thus, standardized measurements of MRI artifact volumes are necessary to provide better comparability. This may improve the development of the MRI suitability of implants and better pulse sequences to finally improve patient care.

Determination of the palatal masticatory mucosa thickness by dental MRI: a prospective study analysing age and gender effects.

OBJECTIVES: The aim of this prospective study was to evaluate age and gender effects on the palatal masticatory mucosa thickness by using non-invasive and non-ionizing MRI. METHODS: 40 periodontally healthy participants of five gender-balanced age groups (20-29, 30-39, 40-49, 50-59 and 60-69 years, respectively) underwent dental MRI at 3 Tesla using a contrast-enhanced, high-resolution 3D-sequence. The palatal masticatory mucosa was measured at 40 standard measurement points by two independent observers. Statistical analysis was performed by using intraclass correlation coefficient (ICC), Shapiro-Wilk test, two-way analysis of variance and post-hoc Tukey HSD test. RESULTS: Measurements of the palatal masticatory mucosa thickness were highly reliable with a mean intraobserver ICC of 0.989 and a mean interobserver ICC of 0.987. Mean palatal masticatory mucosa thickness increased with the distance from the gingival margin in all tooth regions. Molars showed a considerably lower average palatal masticatory mucosa thickness at intermediate heights in comparison to canines and premolars. Average palatal masticatory mucosa thickness continuously increased with age and significantly differed between age groups (p < 0.01). A significant increase was observed between the age groups of 30-39 years and 40-49 years (p = 0.04). Gender had no significant effect on average palatal mucosa thickness (p = 0.19). However, there was a tendency towards thicker mucosa in males (mean ± SD, 3.36 mm ± 0.47) compared to females (3.23 mm ± 0.44). CONCLUSIONS: In the present study, dental MRI allowed for a highly reliable determination of the palatal masticatory mucosa thickness. Considerable intra- and interindividual variations in palatal masticatory mucosa thickness were observed. Average palatal masticatory mucosa thickness was dependent on age but not on gender.

Artefacts of implant-supported single crowns - Impact of material composition on artefact volume on dental MRI.

PURPOSE: MRI allows radiation-free imaging of the head and neck area. However, implant-supported prostheses may severely impair image quality due to artefacts. Therefore, identification of preferable material compositions for implants and supported prostheses with little impact on MR image quality is mandatory. MATERIALS AND METHODS: Overall, one zirconia and four titanium dental implants were provided with different single crown materials: porcelain-fused-to-metal precious alloy (GP-T), porcelain-fused-to-metal non-precious alloy (CCT-T), porcelain-fused-to-zirconia (ZC-T) and monolithic zirconia (Z-T, Z-Z). Three-dimensional artefact volume was determined on a 3 Tesla MRI, applying two standard sequence types (SPACE and TSE). Two-way ANOVA and pair-wise post-hoc Turkey test were performed for comparison of artefact size. RESULTS: Fewest MR artefacts were observed with zirconia implant combined with monolithic zirconia crown. A titanium implant combined with a single crown framework out of the non-precious alloys was unfavourable in terms of artefact volume. Smaller and comparable artefact volumes were noted for titanium implants with the remaining three crown materials (GP-T, ZC-T and Z-T). CONCLUSIONS: Material composition of dental implants provided with single crowns has a profound impact on artefact volume. In comparison with crowns containing cobalt, chromium and tungsten, the MRI artefacts are reduced in precious alloy- and zirconia-based crowns. Further studies are needed to assess whether residual artefacts allow sufficient diagnostic imaging with these crowns. Conflict-of-interest statement: The authors have nothing to disclose.

Dental MRI using a dedicated RF-coil at 3 Tesla.

PURPOSE: To assess the benefit of a dedicated surface coil to visualize dental structures in comparison to standard head/neck coil. METHODS: Measurements were performed using the standard head/neck coil and a dedicated array coil for dental MRI at 3 T. As MRI methods, we used a T1-weighted spin-echo sequence with and without spectral fat saturation, a T2-weighted turbo-spin-echo sequence and a 3-dimensional T2-weighted SPACE sequence. Measurements were performed in a phantom to examine sensitivity profiles. Then the signal gain in dental structures was examined in volunteers and in a patient. RESULTS: As expected for a surface coil, the signal gain of the dental coil was highest at the surface of the phantom and decreased with increasing distance to the coil; it was >120% even at a depth of 30 mm, measured from the centre of the coil. The signal gain within the pulp of the volunteers ranged between 236 and 413%. CONCLUSION: The dedicated array coil offers a significantly higher signal within the region of interest for dental MR imaging thus allowing for better depiction of pathologies within the periodontium and for delineation and tracking of the branches of the maxillary and mandibular nerves.