High-resolution quantitative MRI of multiple sclerosis spinal cord lesions.

PURPOSE: Validation of quantitative MR measures for myelin imaging in the postmortem multiple sclerosis spinal cord. METHODS: Four fixed spinal cord samples were imaged first with a 3T clinical MR scanner to identify areas of interest for scanning, and then with a 7T small bore scanner using a multicomponent-driven equilibrium single-pulse observation of T1 and T2 protocol to produce apparent proton density, T1 , T2 , myelin water, intracellular water, and free-water fraction maps. After imaging, the cords were sectioned and stained with histological markers (hematoxylin and eosin, myelin basic protein, and neurofilament protein), which were quantitatively compared with the MR maps. RESULTS: Excellent correspondence was found between high-resolution MR parameter maps and histology, particularly for apparent proton density MRI and myelin basic protein staining. CONCLUSION: High-resolution quantitative MRI of the spinal cord provides biologically meaningful measures, and could be beneficial to diagnose and track multiple sclerosis lesions in the spinal cord.

Application of radial GRAPPA techniques to single- and multislice dynamic speech MRI using a 16-channel neurovascular coil.

PURPOSE: To investigate: (1) the feasibility of using through-time radial GeneRalized Autocalibrating Partially Parallel Acquisitions (rGRAPPA) and hybrid radial GRAPPA (h-rGRAPPA) in single- and multislice dynamic speech MRI; (2) whether single-slice dynamic speech MRI at a rate of 15 frames per second (fps) or higher and with adequate image quality can be achieved using these radial GRAPPA techniques. METHODS: Seven healthy adult volunteers were imaged at 3T using a 16-channel neurovascular coil and 2 spoiled gradient echo sequences (radial trajectory, field of view = 192 × 192 mm2 , acquired pixel size = 2.4 × 2.4 mm2 ). One sequence imaged a single slice at 16.8 fps, the other imaged 2 interleaved slices at 7.8 fps per slice. Image sets were reconstructed using rGRAPPA and h-rGRAPPA, and their image quality was compared using the root mean square error, structural similarity index, and visual assessments. RESULTS: Image quality deteriorated when fewer than 170 calibration frames were used in the rGRAPPA reconstruction. rGRAPPA image sets demonstrated: (1) in 97% of cases, a similar image quality to h-rGRAPPA image sets reconstructed using a k-space segment size of 4, (2) in 98% of cases, a better image quality than h-rGRAPPA image sets reconstructed using a k-space segment size of 32. CONCLUSION: This study confirmed: (1) the feasibility of using rGRAPPA and h-rGRAPPA in single- and multislice dynamic speech MRI, (2) that single-slice speech imaging at a frame rate higher than 15 fps and with adequate image quality can be achieved using these radial GRAPPA techniques.

Vocal Tract Images Reveal Neural Representations of Sensorimotor Transformation During Speech Imitation.

Imitating speech necessitates the transformation from sensory targets to vocal tract motor output, yet little is known about the representational basis of this process in the human brain. Here, we address this question by using real-time MR imaging (rtMRI) of the vocal tract and functional MRI (fMRI) of the brain in a speech imitation paradigm. Participants trained on imitating a native vowel and a similar nonnative vowel that required lip rounding. Later, participants imitated these vowels and an untrained vowel pair during separate fMRI and rtMRI runs. Univariate fMRI analyses revealed that regions including left inferior frontal gyrus were more active during sensorimotor transformation (ST) and production of nonnative vowels, compared with native vowels; further, ST for nonnative vowels activated somatomotor cortex bilaterally, compared with ST of native vowels. Using test representational similarity analysis (RSA) models constructed from participants' vocal tract images and from stimulus formant distances, we found that RSA searchlight analyses of fMRI data showed either type of model could be represented in somatomotor, temporal, cerebellar, and hippocampal neural activation patterns during ST. We thus provide the first evidence of widespread and robust cortical and subcortical neural representation of vocal tract and/or formant parameters, during prearticulatory ST.

Functional brain outcomes of L2 speech learning emerge during sensorimotor transformation.

Sensorimotor transformation (ST) may be a critical process in mapping perceived speech input onto non-native (L2) phonemes, in support of subsequent speech production. Yet, little is known concerning the role of ST with respect to L2 speech, particularly where learned L2 phones (e.g., vowels) must be produced in more complex lexical contexts (e.g., multi-syllabic words). Here, we charted the behavioral and neural outcomes of producing trained L2 vowels at word level, using a speech imitation paradigm and functional MRI. We asked whether participants would be able to faithfully imitate trained L2 vowels when they occurred in non-words of varying complexity (one or three syllables). Moreover, we related individual differences in imitation success during training to BOLD activation during ST (i.e., pre-imitation listening), and during later imitation. We predicted that superior temporal and peri-Sylvian speech regions would show increased activation as a function of item complexity and non-nativeness of vowels, during ST. We further anticipated that pre-scan acoustic learning performance would predict BOLD activation for non-native (vs. native) speech during ST and imitation. We found individual differences in imitation success for training on the non-native vowel tokens in isolation; these were preserved in a subsequent task, during imitation of mono- and trisyllabic words containing those vowels. fMRI data revealed a widespread network involved in ST, modulated by both vowel nativeness and utterance complexity: superior temporal activation increased monotonically with complexity, showing greater activation for non-native than native vowels when presented in isolation and in trisyllables, but not in monosyllables. Individual differences analyses showed that learning versus lack of improvement on the non-native vowel during pre-scan training predicted increased ST activation for non-native compared with native items, at insular cortex, pre-SMA/SMA, and cerebellum. Our results hold implications for the importance of ST as a process underlying successful imitation of non-native speech.

Recommendations for real-time speech MRI.

Real-time magnetic resonance imaging (RT-MRI) is being increasingly used for speech and vocal production research studies. Several imaging protocols have emerged based on advances in RT-MRI acquisition, reconstruction, and audio-processing methods. This review summarizes the state-of-the-art, discusses technical considerations, and provides specific guidance for new groups entering this field. We provide recommendations for performing RT-MRI of the upper airway. This is a consensus statement stemming from the ISMRM-endorsed Speech MRI summit held in Los Angeles, February 2014. A major unmet need identified at the summit was the need for consensus on protocols that can be easily adapted by researchers equipped with conventional MRI systems. To this end, we provide a discussion of tradeoffs in RT-MRI in terms of acquisition requirements, a priori assumptions, artifacts, computational load, and performance for different speech tasks. We provide four recommended protocols and identify appropriate acquisition and reconstruction tools. We list pointers to open-source software that facilitate implementation. We conclude by discussing current open challenges in the methodological aspects of RT-MRI of speech.

Impact of orthodontic appliances on the quality of craniofacial anatomical magnetic resonance imaging and real-time speech imaging.

AIMS: The aim of this work was to investigate the effects of commonly used orthodontic appliances on the magnetic resonance (MR) image quality of the craniofacial region, with special interest in the soft palate and velopharyngeal wall using real-time speech imaging sequences and anatomical imaging of the temporomandibular joints (TMJ) and pituitaries. METHODS: Common orthodontic appliances were studied on 1.5 T scanner using standard spin and gradient echo sequences (based on the American Society for Testing and Materials standard test method) and sequences previously applied for high-resolution anatomical and dynamic real-time imaging during speech. Images were evaluated for the presence and size of artefacts. RESULTS: Metallic orthodontic appliances had different effects on image quality. The most extensive individual effects were associated with the presence of stainless steel archwire, particularly if combined with stainless steel brackets and stainless steel molar bands. With those appliances, diagnostic quality of magnetic resonance imaging speech and palate images will be most likely severely degraded, or speech imaging and imaging of pituitaries and TMJ will be not possible. All non-metallic, non-metallic with Ni/Cr reinforcement or Ni/Ti alloys appliances were of little concern. LIMITATIONS: The results in the study are only valid at 1.5 T and for the sequences and devices used and cannot necessarily be extrapolated to all sequences and devices. Furthermore, both geometry and size of some appliances are subject dependent, and consequently, the effects on the image quality can vary between subjects. Therefore, the results presented in this article should be treated as a guide when assessing the risks of image quality degradation rather than an absolute evaluation of possible artefacts. CONCLUSIONS: Appliances manufactured from stainless steel cause extensive artefacts, which may render image non-diagnostic. The presence and type of orthodontic appliances should be always included in the patient's screening, so the risks of artefacts can be assessed prior to imaging. Although the risks to patients with fixed orthodontic appliances at 1.5 T MR scanners are low, their secure attachment should be confirmed prior to the examination.

Comparison between diffusion-weighted MRI (DW-MRI) at 1.5 and 3 tesla: a phantom study.

PURPOSE: To compare DW-MRI between 1.5 and 3 Tesla (T) in terms of image quality, apparent diffusion coefficient (ADC), reproducibility, lesion-to-background contrast and signal-to-noise ratio (SNR), using a test object. MATERIALS AND METHODS: A spherical diffusion phantom was used for qualitatively assessing image quality and performing quantitative measurements between the two field strengths. RESULTS: Distortions and signal losses degraded image quality at 3T even when the protocols were optimized for minimum TE. The ADC, in the majority of the phantom compartments, was significantly different between 1.5T and 3T (P < 0.009), while the average coefficient of variation, excluding the phantom compartments affected by artifacts, was <1.3% at both field strengths. The lesion-to-background contrast was improved at 1.5T for images acquired with b = 1000 s/mm(2) and comparable contrast was achieved at 3T with higher b-values. The SNR gain at 3T could, in theory, be balanced by the increased number of signal excitations one can accommodate at 1.5T to perform DW-MRI within the same acquisition time and possibly improved image quality, when 3T systems with no parallel transmission are used. CONCLUSION: Further phantom and in vivo studies are required to investigate the utility of DW-MRI at 3T, if image quality and acquisition times comparable to the ones from 1.5T are assumed.

Adaptive averaging applied to dynamic imaging of the soft palate.

Achieving sufficient temporal and spatial resolution with adequate signal-to-noise ratio (SNR) in dynamic soft palate imaging is challenging. Triggered acquisitions require repeated reproducible speech samples, and while real-time imaging is more reliable, it may lack SNR. Adaptive averaging was implemented to improve SNR in nongated imaging during repetition of a speech task. Similar images were identified using localized cross-correlation before averaging. Adaptive averaging was applied to the soft palate region of images from six volunteers and one patient acquired with various sequences. In volunteers, soft palate SNR increased by 53 ± 17% with four averages. The additional SNR was used to enable reduced slice thickness in two example subjects. Adaptive averaging was also compared to pseudotriggered images in one example and, in another, it was applied to an unrepeated speech task. In a patient with a repaired cleft palate, the technique was used to demonstrate residual velopharyngeal insufficiency. This initial work demonstrates that increased temporal or spatial resolution may be traded for reduced SNR, which can be recovered using adaptive averaging. This will be a valuable tool in assessing velopharyngeal function, particularly in pediatric patients where cooperation may make gated studies difficult or when their head sizes require increased spatial resolution while maintaining temporal resolution.

A segmentation-informed deep learning framework to register dynamic two-dimensional magnetic resonance images of the vocal tract during speech.

Objective: Dynamic magnetic resonance (MR) imaging enables visualisation of articulators during speech. There is growing interest in quantifying articulator motion in two-dimensional MR images of the vocal tract, to better understand speech production and potentially inform patient management decisions. Image registration is an established way to achieve this quantification. Recently, segmentation-informed deformable registration frameworks have been developed and have achieved state-of-the-art accuracy. This work aims to adapt such a framework and optimise it for estimating displacement fields between dynamic two-dimensional MR images of the vocal tract during speech. Methods: A deep-learning-based registration framework was developed and compared with current state-of-the-art registration methods and frameworks (two traditional methods and three deep-learning-based frameworks, two of which are segmentation informed). The accuracy of the methods and frameworks was evaluated using the Dice coefficient (DSC), average surface distance (ASD) and a metric based on velopharyngeal closure. The metric evaluated if the fields captured a clinically relevant and quantifiable aspect of articulator motion. Results: The segmentation-informed frameworks achieved higher DSCs and lower ASDs and captured more velopharyngeal closures than the traditional methods and the framework that was not segmentation informed. All segmentation-informed frameworks achieved similar DSCs and ASDs. However, the proposed framework captured the most velopharyngeal closures. Conclusions: A framework was successfully developed and found to more accurately estimate articulator motion than five current state-of-the-art methods and frameworks. Significance: The first deep-learning-based framework specifically for registering dynamic two-dimensional MR images of the vocal tract during speech has been developed and evaluated.

Effects of spatial and temporal resolution on cardiovascular magnetic resonance feature tracking measurements using a simple realistic numerical phantom.

OBJECTIVES: To develop a single-slice numerical phantom with known myocardial motion, at several temporal and in-plane spatial resolutions, for testing and comparison of Cardiovascular Magnetic Resonance (CMR) feature tracking (FT) software. METHODS: The phantom was developed based on CMR acquisitions of one volunteer (acquired cine, tagging cine, T1 map, T2 map, proton density weighted image). The numerical MRI simulator JEMRIS was used, and the phantom was generated at several in-plane spatial resolutions (1.4 × 1.4 mm2 to 3.0 × 3.0 mm2) and temporal resolutions (20 to 40 cardiac phases). Two feature tracking software packages were tested: Medical Image Tracking Toolbox (MITT) and two versions of cvi42 (v5.3.8 and v5.13.7). The effect of resolution on strain results was investigated with reference to ground-truth radial and circumferential strain. RESULTS: Peak radial strain was consistently undermeasured more for cvi42 v5.13.7 than for v5.3.8. Increased pixel size produced a trend of increased difference from ground-truth peak strain, with the largest changes for cvi42 obtained using v5.13.7 between 1.4 × 1.4 mm2 and 3.0 × 3.0 mm2, at 9.17 percentage points (radial) and 8.42 percentage points (circumferential). CONCLUSIONS: The results corroborate the presence of intervendor differences in feature tracking results and show the magnitude of strain differences between software versions. ADVANCES IN KNOWLEDGE: This study shows how temporal and in-plane spatial resolution can affect feature tracking with reference to the ground-truth strain of a numerical phantom. Results reaffirm the need for numerical phantom development for the validation and testing of FT software.

Real-time speech MRI datasets with corresponding articulator ground-truth segmentations.

The use of real-time magnetic resonance imaging (rt-MRI) of speech is increasing in clinical practice and speech science research. Analysis of such images often requires segmentation of articulators and the vocal tract, and the community is turning to deep-learning-based methods to perform this segmentation. While there are publicly available rt-MRI datasets of speech, these do not include ground-truth (GT) segmentations, a key requirement for the development of deep-learning-based segmentation methods. To begin to address this barrier, this work presents rt-MRI speech datasets of five healthy adult volunteers with corresponding GT segmentations and velopharyngeal closure patterns. The images were acquired using standard clinical MRI scanners, coils and sequences to facilitate acquisition of similar images in other centres. The datasets include manually created GT segmentations of six anatomical features including the tongue, soft palate and vocal tract. In addition, this work makes code and instructions to implement a current state-of-the-art deep-learning-based method to segment rt-MRI speech datasets publicly available, thus providing the community and others with a starting point for developing such methods.

Effectiveness of semi-quantitative multiphase dynamic contrast-enhanced MRI as a predictor of malignancy in complex adnexal masses: radiological and pathological correlation.

OBJECTIVES: To determine whether threshold criteria using semi-quantitative multiphase-dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) can improve prediction of malignancy in complex adnexal masses. METHODS: MRI features of 70 complex adnexal masses with enhancing components in 63 patients were reviewed and correlated with histopathology (n = 67) or radiological follow-up (n = 3). Masses were categorised as benign (n = 34) or borderline/invasive malignant (n = 36). Borderline lesions (n = 6) were also analysed separately. Using the semi-quantitative breast analysis software, regions of interest were drawn around the most avidly enhancing component of each lesion. Maximum absolute enhancement of signal intensities (SI(max)), maximum relative enhancement (SI(rel)) and wash-in rate (WIR) were recorded. Optimal threshold criteria were established to predict borderline/invasive malignancy. RESULTS: There was a significant difference in mean SI(max) (P < 0.05), SI(rel) (P < 0.01) and WIR (P < 0.001) between benign and borderline/invasive malignant groups. A cut-off WIR ≥ 9.5 l/s had a specificity of 88% and positive predictive value of 86% for predicting malignancy, significantly better than conventional MRI (62%, P < 0.01). WIR <8.2 l/s had a negative predictive value of 94%. CONCLUSION: Threshold criteria using semi-quantitative multiphase DCE-MRI improves specificity in the prediction of malignancy in complex adnexal masses with enhancing components and is complementary to standard qualitative assessment. KEY POINTS: Semi-quantitative DCE-MRI threshold criteria are effective for predicting ovarian malignancy. The surgical approach may be altered depending on DCE-MRI threshold criteria analysis. Borderline tumours demonstrate significant overlap with benign lesions using DCE-MRI threshold criteria.

Individual differences in vocal size exaggeration.

The human voice carries socially relevant information such as how authoritative, dominant, and attractive the speaker sounds. However, some speakers may be able to manipulate listeners by modulating the shape and size of their vocal tract to exaggerate certain characteristics of their voice. We analysed the veridical size of speakers' vocal tracts using real-time magnetic resonance imaging as they volitionally modulated their voice to sound larger or smaller, corresponding changes to the size implied by the acoustics of their voice, and their influence over the perceptions of listeners. Individual differences in this ability were marked, spanning from nearly incapable to nearly perfect vocal modulation, and was consistent across modalities of measurement. Further research is needed to determine whether speakers who are effective at vocal size exaggeration are better able to manipulate their social environment, and whether this variation is an inherited quality of the individual, or the result of life experiences such as vocal training.

Deep-learning-based segmentation of the vocal tract and articulators in real-time magnetic resonance images of speech.

BACKGROUND AND OBJECTIVE: Magnetic resonance (MR) imaging is increasingly used in studies of speech as it enables non-invasive visualisation of the vocal tract and articulators, thus providing information about their shape, size, motion and position. Extraction of this information for quantitative analysis is achieved using segmentation. Methods have been developed to segment the vocal tract, however, none of these also fully segment any articulators. The objective of this work was to develop a method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech, thus overcoming the limitations of existing methods. METHODS: Five speech MR image sets (392 MR images in total), each of a different healthy adult volunteer, were used in this work. A fully convolutional network with an architecture similar to the original U-Net was developed to segment the following six regions in the image sets: the head, soft palate, jaw, tongue, vocal tract and tooth space. A five-fold cross-validation was performed to investigate the segmentation accuracy and generalisability of the network. The segmentation accuracy was assessed using standard overlap-based metrics (Dice coefficient and general Hausdorff distance) and a novel clinically relevant metric based on velopharyngeal closure. RESULTS: The segmentations created by the method had a median Dice coefficient of 0.92 and a median general Hausdorff distance of 5mm. The method segmented the head most accurately (median Dice coefficient of 0.99), and the soft palate and tooth space least accurately (median Dice coefficients of 0.92 and 0.93 respectively). The segmentations created by the method correctly showed 90% (27 out of 30) of the velopharyngeal closures in the MR image sets. CONCLUSIONS: An automatic method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech was successfully developed. The method is intended for use in clinical and non-clinical speech studies which involve quantitative analysis of the shape, size, motion and position of the vocal tract and articulators. In addition, a novel clinically relevant metric for assessing the accuracy of vocal tract and articulator segmentation methods was developed.

Singers show enhanced performance and neural representation of vocal imitation.

Humans have a remarkable capacity to finely control the muscles of the larynx, via distinct patterns of cortical topography and innervation that may underpin our sophisticated vocal capabilities compared with non-human primates. Here, we investigated the behavioural and neural correlates of laryngeal control, and their relationship to vocal expertise, using an imitation task that required adjustments of larynx musculature during speech. Highly trained human singers and non-singer control participants modulated voice pitch and vocal tract length (VTL) to mimic auditory speech targets, while undergoing real-time anatomical scans of the vocal tract and functional scans of brain activity. Multivariate analyses of speech acoustics, larynx movements and brain activation data were used to quantify vocal modulation behaviour and to search for neural representations of the two modulated vocal parameters during the preparation and execution of speech. We found that singers showed more accurate task-relevant modulations of speech pitch and VTL (i.e. larynx height, as measured with vocal tract MRI) during speech imitation; this was accompanied by stronger representation of VTL within a region of the right somatosensory cortex. Our findings suggest a common neural basis for enhanced vocal control in speech and song. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part I)'.

Assessment of Apparent Diffusion Coefficients and SUVs as Predicators of Histological Differentiation in Anal Squamous Cell Carcinoma.

AIM: The study aims to assess minimal apparent diffusion coefficient (ADCmin) and SUVmax as predictors of histological differentiation in patients with anal squamous cell carcinoma (ASCC) and to determine cutoff values for each histopathological tumor grade. PATIENTS AND METHODS: A retrospective study of 41 ASCC patients (14 males, 27 females; mean age, 65 ± 13 years) staged with FDG PET/CT and MRI (mean scan time interval, 21 ± 11 days). SUVmax and ADCmin values were measured and compared with histopathological tumor grading obtained from biopsy. RESULTS: The mean size and tumor volume were 3 ± 2 cm and 16.5 ± 27.3 cm3, respectively. The mean ADCmin values for well-, moderately, and poorly differentiated ASCC were 935 ± 179, 896 ± 123, and 637 ± 114, respectively. The mean SUVmax for well-, moderately, and poorly differentiated ASCC were 6.9 ± 1.8, 11.5 ± 4.1, and 13.4 ± 2.6, respectively. The difference in mean ADCmin values between poorly and moderately/well-differentiated tumors was statistically significant, whereas this was not significant between moderately and well-differentiated tumors. Differences in SUVmax values were statistically significant between poorly/moderately and well-differentiated tumors, whereas there was no statistical significance between poorly and moderately differentiated tumors. By combining the 2 modalities using cutoff values of 675 × 10-6 mm2·s-1 for ADCmin and 8.5 for SUVmax, it was possible to differentiate the tumor categories with a sensitivity, specificity, positive predictive value, and negative predictive value, respectively, of 84.6%, 96.4%, 91.7%, and 93.1% for well-differentiated ASCC, 76.5%, 87.5%, 81.3%, and 84% for moderately, and 90.9%, 89.3%, 76.9%, and 96.2% for poorly differentiated ASCC, respectively. CONCLUSIONS: ADCmin and SUVmax values correlated with the degree of differentiation in ASCC and can be used as predictors of tumor grading and aggressiveness. Combined ADCmin and SUVmax cutoff values can therefore be used for early patient risk stratification and treatment decision making.

Diagnostic Accuracy of FEC-PET/CT, FDG-PET/CT, and Diffusion-Weighted MRI in Detection of Nodal Metastases in Surgically Treated Endometrial and Cervical Carcinoma.

PURPOSE: Preoperative nodal staging is important for planning treatment in cervical cancer and endometrial cancer, but remains challenging. We compare nodal staging accuracy of 18F-ethyl-choline-(FEC)-PET/CT, 18F-fluoro-deoxy-glucose-(FDG)-PET/CT, and diffusion-weighted-MRI (DW-MRI) with conventional morphologic MRI. EXPERIMENTAL DESIGN: A prospective, multicenter observational study of diagnostic accuracy for nodal metastases was undertaken in 5 gyne-oncology centers. FEC-PET/CT, FDG-PET/CT, and DW-MRI were compared with nodal size and morphology on MRI. Reference standard was strictly correlated nodal histology. Eligibility included operable cervical cancer stage ≥ 1B1 or endometrial cancer (grade 3 any stage with myometrial invasion or grade 1-2 stage ≥ II). RESULTS: Among 162 consenting participants, 136 underwent study DW-MRI and FDG-PET/CT and 60 underwent FEC-PET/CT. In 118 patients, 267 nodal regions were strictly correlated at histology (nodal positivity rate, 25%). Sensitivity per patient (n = 118) for nodal size, morphology, DW-MRI, FDG- and FEC-PET/CT was 40%*, 53%, 53%, 63%*, and 67% for all cases (*, P = 0.016); 10%, 10%, 20%, 30%, and 25% in cervical cancer (n = 40); 65%, 75%, 70%, 80% and 88% in endometrial cancer (n = 78). FDG-PET/CT outperformed nodal size (P = 0.006) and size ratio (P = 0.04) for per-region sensitivity. False positive rates were all <10%. CONCLUSIONS: All imaging techniques had low sensitivity for detection of nodal metastases and cannot replace surgical nodal staging. The performance of FEC-PET/CT was not statistically different from other techniques that are more widely available. FDG-PET/CT had higher sensitivity than size in detecting nodal metastases. False positive rates were low across all methods. The low false positive rate demonstrated by FDG-PET/CT may be helpful in arbitration of challenging surgical planning decisions.

Realistic Dynamic Numerical Phantom for MRI of the Upper Vocal Tract.

Dynamic and real-time MRI (rtMRI) of human speech is an active field of research, with interest from both the linguistics and clinical communities. At present, different research groups are investigating a range of rtMRI acquisition and reconstruction approaches to visualise the speech organs. Similar to other moving organs, it is difficult to create a physical phantom of the speech organs to optimise these approaches; therefore, the optimisation requires extensive scanner access and imaging of volunteers. As previously demonstrated in cardiac imaging, realistic numerical phantoms can be useful tools for optimising rtMRI approaches and reduce reliance on scanner access and imaging volunteers. However, currently, no such speech rtMRI phantom exists. In this work, a numerical phantom for optimising speech rtMRI approaches was developed and tested on different reconstruction schemes. The novel phantom comprised a dynamic image series and corresponding k-space data of a single mid-sagittal slice with a temporal resolution of 30 frames per second (fps). The phantom was developed based on images of a volunteer acquired at a frame rate of 10 fps. The creation of the numerical phantom involved the following steps: image acquisition, image enhancement, segmentation, mask optimisation, through-time and spatial interpolation and finally the derived k-space phantom. The phantom was used to: (1) test different k-space sampling schemes (Cartesian, radial and spiral); (2) create lower frame rate acquisitions by simulating segmented k-space acquisitions; (3) simulate parallel imaging reconstructions (SENSE and GRAPPA). This demonstrated how such a numerical phantom could be used to optimise images and test multiple sampling strategies without extensive scanner access.

Improving liver lesion characterisation using retrospective fusion of FDG PET/CT and MRI.

AIM: To compare retrospectively fused FDG PET/CT and MRI (PET/MRI) to FDG PET/CT and MRI for characterisation of indeterminate focal liver lesions as malignant or benign in patients with a known primary malignancy. MATERIALS AND METHOD: A retrospective review of 70 patients (30 females, 40 males; mean age 56 ±â€¯14 years) with 150 indeterminate lesions after FDG PET/CT and MRI (mean scan time interval 21 ±â€¯11 days). HERMES® software was used to fuse PET/CT and MRI scans which were reviewed by 2 readers using the Likert score (scale 1-5) to characterise lesions as benign (1-3) or malignant (4-5). Final diagnosis was determined by histopathology or follow up imaging. Results for fused PET/MRI were compared to PET/CT and MRI alone. RESULTS: For detection, MRI and fused PET/MRI detected all the lesions while PET/CT detected 89.4%. Characterisation of liver lesions as malignant on PET/CT alone yielded sensitivity, specificity, accuracy, PPV and NPV of 55.6%, 83.3%, 66.7%, 83.3%, 55.6% respectively and 67.6%, 92.1%, 80%, 89.3%, 74.5% for MRI, respectively. The sensitivity, specificity, accuracy, PPV and NPV for characterising lesions as malignant increased to 91.9%, 97.4%, 94.7%, 97.1%, 92.5% with PET/MRI fusion. The sensitivity, specificity, accuracy, PPV and NPV of fused PET/MRI for characterising lesions as malignant remained superior to PET/CT and MRI. CONCLUSION: Retrospective fusion of PET with MRI has improved characterisation of indeterminate focal liver lesions compared to MRI or FDG PET/CT alone.