Reconstruction of high-resolution tongue volumes from MRI

A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients

BACKGROUND

High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these images require long acquisition times, so routinely acquired follow-up images after treatment often consist of 2D low-resolution (LR) images (with thick slices in multiple planes).

PURPOSE

In this work, we present a super-resolution convolutional neural network, based on previous single-image MRI super-resolution work, that can reconstruct a HR image from 2D LR slices in multiple planes in order to facilitate the extraction of structural biomarkers from routine scans.

METHODS

A multilevel densely connected super-resolution convolutional neural network (mDCSRN) was adapted to take two perpendicular LR scans (e.g., coronal and axial) as tensors and reconstruct a 3D HR image. A training set of 90 HR T1 pediatric head scans from the Adolescent Brain Cognitive Development (ABCD) study was used, with 2D LR images simulated through a downsampling pipeline that introduces motion artifacts, blurring, and registration errors to make the LR scans more realistic to routinely acquired ones. The outputs of the model were compared against simple interpolation in two steps. First, the quality of the reconstructed HR images was assessed using the peak signal-to-noise ratio and structural similarity index compared to baseline. Second, the precision of structure segmentation (using the autocontouring software Limbus AI) in the reconstructed versus the baseline HR images was assessed using mean distance-to-agreement (mDTA) and 95% Hausdorff distance. Three datasets were used: 10 new ABCD images (dataset 1), 18 images from the Children's Brain Tumor Network (CBTN) study (dataset 2) and 6 "real-world" follow-up images of a pediatric head and neck cancer patient (dataset 3).

RESULTS

The proposed mDCSRN outperformed simple interpolation in terms of visual quality. Similarly, structure segmentations were closer to baseline images after 3D reconstruction. The mDTA improved to, on average (95% confidence interval), 0.7 (0.4-1.0) and 0.8 (0.7-0.9) mm for datasets 1 and 3 respectively, from the interpolation performance of 6.5 (3.6-9.5) and 1.2 (1.0-1.3) mm.

CONCLUSIONS

We demonstrate that deep learning methods can successfully reconstruct 3D HR images from 2D LR ones, potentially unlocking datasets for retrospective study and advancing research in the long-term effects of pediatric cancer. Our model outperforms standard interpolation, both in perceptual quality and for autocontouring. Further work is needed to validate it for additional structural analysis tasks.

Cross-Modality Reference and Feature Mutual-Projection for 3D Brain MRI Image Super-Resolution

High-resolution (HR) magnetic resonance imaging (MRI) can reveal rich anatomical structures for clinical diagnoses. However, due to hardware and signal-to-noise ratio limitations, MRI images are often collected with low resolution (LR) which is not conducive to diagnosing and analyzing clinical diseases. Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance. In this paper, we propose a cross-modality reference and feature mutual-projection (CRFM) method to enhance the spatial resolution of brain MRI images. Specifically, we feed the gradients of HR MRI images from referenced imaging modality into the SR network to transform true clear textures to LR feature maps. Meanwhile, we design a plug-in feature mutual-projection (FMP) method to capture the cross-scale dependency and cross-modality similarity details of MRI images. Finally, we fuse all feature maps with parallel attentions to produce and refine the HR features adaptively. Extensive experiments on MRI images in the image domain and k-space show that our CRFM method outperforms existing state-of-the-art MRI SR methods.

Investigating muscle coordination patterns with Granger causality analysis in protrusive motion from tagged and diffusion MRI

The human tongue exhibits an orchestrated arrangement of internal muscles, working in sequential order to execute tongue movements. Understanding the muscle coordination patterns involved in tongue protrusive motion is crucial for advancing knowledge of tongue structure and function. To achieve this, this work focuses on five muscles known to contribute to protrusive motion. Tagged and diffusion MRI data are collected for analysis of muscle fiber geometry and motion patterns. Lagrangian strain measurements are derived, and Granger causal analysis is carried out to assess predictive information among the muscles. Experimental results suggest sequential muscle coordination of protrusive motion among distinct muscle groups.

Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction

This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body.

A state-of-the-art review of image motion deblurring techniques in precision agriculture

Image motion deblurring is a crucial technology in computer vision that has gained significant attention attracted by its outstanding ability for accurate acquisition of motion image information, processing and intelligent decision making, etc. Motion blur has recently been considered as one of the major challenges for applications of computer vision in precision agriculture. Motion blurred images seriously affect the accuracy of information acquisition in precision agriculture scene image such as testing, tracking, and behavior analysis of animals, recognition of plant phenotype, critical characteristics of pests and diseases, etc. On the other hand, the fast motion and irregular deformation of agriculture livings, and motion of image capture device all introduce great challenges for image motion deblurring. Hence, the demand of more efficient image motion deblurring method is rapidly increasing and developing in the applications with dynamic scene. Up till now, some studies have been carried out to address this challenge, e.g., spatial motion blur, multi-scale blur and other types of blur. This paper starts with categorization of causes of image blur in precision agriculture. Then, it gives detail introduction of general-purpose motion deblurring methods and their the strengthen and weakness. Furthermore, these methods are compared for the specific applications in precision agriculture e.g., detection and tracking of livestock animal, harvest sorting and grading, and plant disease detection and phenotyping identification etc. Finally, future research directions are discussed to push forward the research and application of advancing in precision agriculture image motion deblurring field.

Analysis of Tongue Muscle Strain During Speech From Multimodal Magnetic Resonance Imaging

PURPOSE

Muscle groups within the tongue in healthy and diseased populations show different behaviors during speech. Visualizing and quantifying strain patterns of these muscle groups during tongue motion can provide insights into tongue motor control and adaptive behaviors of a patient.

METHOD

We present a pipeline to estimate the strain along the muscle fiber directions in the deforming tongue during speech production. A deep convolutional network estimates the crossing muscle fiber directions in the tongue using diffusion-weighted magnetic resonance imaging (MRI) data acquired at rest. A phase-based registration algorithm is used to estimate motion of the tongue muscles from tagged MRI acquired during speech. After transforming both muscle fiber directions and motion fields into a common atlas space, strain tensors are computed and projected onto the muscle fiber directions, forming so-called strains in the line of actions (SLAs) throughout the tongue. SLAs are then averaged over individual muscles that have been manually labeled in the atlas space using high-resolution T2-weighted MRI. Data were acquired, and this pipeline was run on a cohort of eight healthy controls and two glossectomy patients.

RESULTS

The crossing muscle fibers reconstructed by the deep network show orthogonal patterns. The strain analysis results demonstrate consistency of muscle behaviors among some healthy controls during speech production. The patients show irregular muscle patterns, and their tongue muscles tend to show more extension than the healthy controls.

CONCLUSIONS

The study showed visual evidence of correlation between two muscle groups during speech production. Patients tend to have different strain patterns compared to the controls. Analysis of variations in muscle strains can potentially help develop treatment strategies in oral diseases.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.21957011.

Measuring Strain in Diffusion-Weighted Data Using Tagged Magnetic Resonance Imaging

Accurate strain measurement in a deforming organ has been essential in motion analysis using medical images. In recent years, internal tissue's in vivo motion and strain computation has been mostly achieved through dynamic magnetic resonance (MR) imaging. However, such data lack information on tissue's intrinsic fiber directions, preventing computed strain tensors from being projected onto a direction of interest. Although diffusion-weighted MR imaging excels at providing fiber tractography, it yields static images unmatched with dynamic MR data. This work reports an algorithm workflow that estimates strain values in the diffusion MR space by matching corresponding tagged dynamic MR images. We focus on processing a dataset of various human tongue deformations in speech. The geometry of tongue muscle fibers is provided by diffusion tractography, while spatiotemporal motion fields are provided by tagged MR analysis. The tongue's deforming shapes are determined by segmenting a synthetic cine dynamic MR sequence generated from tagged data using a deep neural network. Estimated motion fields are transformed into the diffusion MR space using diffeomorphic registration, eventually leading to strain values computed in the direction of muscle fibers. The method was tested on 78 time volumes acquired during three sets of specific tongue deformations including both speech and protrusion motion. Strain in the line of action of seven internal tongue muscles was extracted and compared both intra- and inter-subject. Resulting compression and stretching patterns of individual muscles revealed the unique behavior of individual muscles and their potential activation pattern.

Unsupervised arterial spin labeling image super-resolution via multi-scale generative adversarial network

PURPOSE

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is an advanced non-invasive imaging technology that can measure cerebral blood flow (CBF) quantitatively without a contrast agent injection or radiation exposure. However, because of the weak labeling, conventional ASL images usually suffer from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. Therefore, a method that can simultaneously improve the spatial resolution and SNR is needed.

METHODS

In this work, we proposed an unsupervised super-resolution (SR) method to improve ASL image resolution based on a pyramid of generative adversarial networks (GAN). Through layer-by-layer training, the generators can learn features from the coarsest to the finest. The last layer's generator which contains fine details and textures was used to generate the final SR ASL images. In our proposed framework, the corresponding T1-weighted MR image was supplied as a second-channel input of the generators to provide high-resolution prior information. In addition, a low-pass-filter loss term was included to suppress the noise of the original ASL images. To evaluate the performance of the proposed framework, a simulation study and two real-patient experiments based on the in vivo datasets obtained from 3 healthy subjects on a 3T MR scanner were conducted, regarding the low-resolution (LR) to normal-resolution (NR) and the NR-to-SR tasks. The proposed method was compared to the nearest neighbor interpolation, trilinear interpolation, 3rd order B-splines interpolation methods, and deep image prior (DIP) with the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) as the quantification metrics. The averaged ASL images acquired with 44 min acquisition time were used as the ground truth for real-patient LR-to-NR study. The ablation studies of low-pass-filter loss term and T1-weighted MR image were performed based on simulation data.

RESULTS

For the simulation study, results show that the proposed method achieved significantly higher PSNR (p-value < 0.05) and SSIM (p-value < 0.05) than the nearest neighbor interpolation, trilinear interpolation, 3rd order B-splines interpolation, and DIP methods. For the real-patient LR-to-NR experiment, results show that the proposed method can generate high-quality SR ASL images with clearer structure boundaries and low noise levels, and has the highest mean PSNR and SSIM. For real-patient NR-to-SR tasks, the structure of the results using the proposed method is sharper and clearer, which are the most similar to the structure of the reference 44 min acquisition image than other methods. The proposed method also shows the ability to remove artifacts in the NR image while super-resolution. The ablation study verified that the low-pass-filter loss term and T1-weighted MR image are necessary for the proposed method.

CONCLUSIONS

The proposed unsupervised multi-scale GAN framework can simultaneously improve spatial resolution and reduce image noise. Experiment results from simulation data and 3 healthy subjects show that the proposed method achieves better performance than the nearest neighbor interpolation, the trilinear interpolation, the 3rd order B-splines interpolation, and DIP methods. This article is protected by copyright. All rights reserved.

Task-Coupling Elastic Learning for Physical Sign-based Medical Image Classification

Physical signs of patients indicate crucial evidence for diagnosing both location and nature of the disease, where there is a sequential relationship between the two tasks. Thus their joint learning can utilize intrinsic association by transferring related knowledge across relevant tasks. Choosing the right time to transfer is a critical problem for joint learning. However, how to dynamically adjust when tasks interact to capture the right time for transferring related knowledge is still an open issue. To this end, we propose a Task-Coupling Elastic Learning (TCEL) framework to model the task relatedness for classifying disease-location and disease-nature based on physical sign images. The main idea is to dynamically transfer relevant knowledge by progressively shifting task-coupling from loose to tight during the multi-stage training. In the early stage of training, we relax the constraints of modeling relations to focus more in learning the generic task-common features. In the later stage, the semantic guidance will be strengthened to learn the task-specific features. Specifically, a dynamic sequential module (DSM) is proposed to explicitly model the sequential relationship and enable multi-stage training. Moreover, to address the side effect of DSM, a new loss regularization is proposed. The extensive experiments on these two clinical datasets show the superiority of the proposed method over the baselines, and demonstrate the effectiveness of the proposed task-coupling elastic mechanism.

Fully-channel regional attention network for disease-location recognition with tongue images

OBJECTIVE

Using the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model.

METHODS

To enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism.

RESULTS

The tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition.

CONCLUSION

In this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency.

SIGNIFICANCE

The applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications.

A deep joint sparse non-negative matrix factorization framework for identifying the common and subject-specific functional units of tongue motion during speech

Intelligible speech is produced by creating varying internal local muscle groupings-i.e., functional units-that are generated in a systematic and coordinated manner. There are two major challenges in characterizing and analyzing functional units. First, due to the complex and convoluted nature of tongue structure and function, it is of great importance to develop a method that can accurately decode complex muscle coordination patterns during speech. Second, it is challenging to keep identified functional units across subjects comparable due to their substantial variability. In this work, to address these challenges, we develop a new deep learning framework to identify common and subject-specific functional units of tongue motion during speech. Our framework hinges on joint deep graph-regularized sparse non-negative matrix factorization (NMF) using motion quantities derived from displacements by tagged Magnetic Resonance Imaging. More specifically, we transform NMF with sparse and graph regularizations into modular architectures akin to deep neural networks by means of unfolding the Iterative Shrinkage-Thresholding Algorithm to learn interpretable building blocks and associated weighting map. We then apply spectral clustering to common and subject-specific weighting maps from which we jointly determine the common and subject-specific functional units. Experiments carried out with simulated datasets show that the proposed method achieved on par or better clustering performance over the comparison methods.Experiments carried out with in vivo tongue motion data show that the proposed method can determine the common and subject-specific functional units with increased interpretability and decreased size variability.

Reliability of Sonoelastography Measurement of Tongue Muscles and Its Application on Obstructive Sleep Apnea

Few studies have explored the feasibility of shear-wave ultrasound elastography (SWUE) for evaluating the upper airways of patients with obstructive sleep apnea (OSA). This study aimed to establish a reliable SWUE protocol for evaluating tongue muscle elasticity and its feasibility and utility in differentiating patients with OSA. Inter-rater and intra-rater reliability of SWUE measurements were tested using the intraclass correlation coefficients. Submental ultrasound was used to measure tongue thickness and stiffness. Association between the ultrasound measurements and presence of OSA was analyzed using multivariate logistic regression. One-way analysis of variance was used to examine if the values of the ultrasound parameters varied among patients with different severities of OSA. Overall, 37 healthy subjects and 32 patients with OSA were recruited. The intraclass correlation coefficients of intra‐ and inter-rater reliability for SWUE for tongue stiffness ranged from 0.84 to 0.90. After adjusting for age, sex, neck circumference, and body mass index, the risk for OSA was positively associated with tongue thickness [odds ratio 1.16 (95% confidence interval 1.01–1.32)] and negatively associated with coronal imaging of tongue muscle stiffness [odds ratio 0.72 (95% confidence interval 0.54–0.95)]. There were no significant differences in tongue stiffness among OSA patients with varying disease severity. SWUE provided a reliable evaluation of tongue muscle stiffness, which appeared to be softer in patients with OSA. Future longitudinal studies are necessary to investigate the relationship between tongue softening and OSA, as well as response to treatment.

Applications of a deep learning method for anti-aliasing and super-resolution in MRI

Magnetic resonance (MR) images with both high resolutions and high signal-to-noise ratios (SNRs) are desired in many clinical and research applications. However, acquiring such images takes a long time, which is both costly and susceptible to motion artifacts. Acquiring MR images with good in-plane resolution and poor through-plane resolution is a common strategy that saves imaging time, preserves SNR, and provides one viewpoint with good resolution in two directions. Unfortunately, this strategy also creates orthogonal viewpoints that have poor resolution in one direction and, for 2D MR acquisition protocols, also creates aliasing artifacts. A deep learning approach called SMORE that carries out both anti-aliasing and super-resolution on these types of acquisitions using no external atlas or exemplars has been previously reported but not extensively validated. This paper reviews the SMORE algorithm and then demonstrates its performance in four applications with the goal to demonstrate its potential for use in both research and clinical scenarios. It is first shown to improve the visualization of brain white matter lesions in FLAIR images acquired from multiple sclerosis patients. Then it is shown to improve the visualization of scarring in cardiac left ventricular remodeling after myocardial infarction. Third, its performance on multi-view images of the tongue is demonstrated and finally it is shown to improve performance in parcellation of the brain ventricular system. Both visual and selected quantitative metrics of resolution enhancement are demonstrated.

Super-resolution for upper abdominal MRI: Acquisition and post-processing protocol optimization using brain MRI control data and expert reader validation

PURPOSE

Magnetic resonance (MR) cholangiopancreatography (MRCP) is an established specialist method for imaging the upper abdomen and biliary/pancreatic ducts. Due to limitations of either MR image contrast or low through-plane resolution, patients may require further evaluation with contrast-enhanced computed tomography (CT) images. However, CT fails to offer the high tissue-ductal-vessel contrast-to-noise ratio available on T2-weighted MR imaging.

METHODS

MR super-resolution reconstruction (SRR) frameworks have the potential to provide high-resolution visualizations from multiple low through-plane resolution single-shot T2-weighted (SST2W) images as currently used during MRCP studies. Here, we (i) optimize the source image acquisition protocols by establishing the ideal number and orientation of SST2W series for MRCP SRR generation, (ii) optimize post-processing protocols for two motion correction candidate frameworks for MRCP SRR, and (iii) perform an extensive validation of the overall potential of upper abdominal SRR, using four expert readers with subspeciality interest in hepato-pancreatico-biliary imaging.

RESULTS

Obtained SRRs show demonstrable advantages over traditional SST2W MRCP data in terms of anatomical clarity and subjective radiologists' preference scores for a range of anatomical regions that are especially critical for the management of cancer patients.

CONCLUSIONS

Our results underline the potential of using SRR alongside traditional MRCP data for improved clinical diagnosis.

Differentiating post-cancer from healthy tongue muscle coordination patterns during speech using deep learning

The ability to differentiate post-cancer from healthy tongue muscle coordination patterns is necessary for the advancement of speech motor control theories and for the development of therapeutic and rehabilitative strategies. A deep learning approach is presented to classify two groups using muscle coordination patterns from magnetic resonance imaging (MRI). The proposed method uses tagged-MRI to track the tongue's internal tissue points and atlas-driven non-negative matrix factorization to reduce the dimensionality of the deformation fields. A convolutional neural network is applied to the classification task yielding an accuracy of 96.90%, offering the potential to the development of therapeutic or rehabilitative strategies in speech-related disorders.

Sagittal Measurement of Tongue Movement During Respiration: Comparison Between Ultrasonography and Magnetic Resonance Imaging

The tongue makes up the anterior pharyngeal wall and is critical for airway patency. Magnetic resonance imaging (MRI) is commonly used to study pharyngeal muscle function in pharyngeal disorders such as obstructive sleep apnoea. Tagged MRI and ultrasound studies have separately revealed ∼1 mm of anterior tongue movement during inspiration in healthy patients, but these modalities have not been directly compared. In the study described here, agreement between ultrasound and MRI in measuring regional tongue displacement in 21 healthy patients and 21 patients with obstructive sleep apnoea was evaluated. We found good consistency and agreement between the two techniques, with an intra-class correlation coefficient of 0.79 (95% confidence interval: 0.75-0.82) for anteroposterior tongue motion during inspiration. Ultrasound measurements of posterior tongue displacement were 0.24 ± 0.64 mm greater than MRI measurements (95% limits of agreement: 1.03 to -1.49). This may reflect the higher spatial and temporal resolution of the ultrasound technique. This study confirms that ultrasound is a suitable method for quantifying inspiratory tongue movement.

A Deep Learning Based Anti-aliasing Self Super-resolution Algorithm for MRI

High resolution magnetic resonance (MR) images are desired in many clinical applications, yet acquiring such data with an adequate signal-to-noise ratio requires a long time, making them costly and susceptible to motion artifacts. A common way to partly achieve this goal is to acquire MR images with good in-plane resolution and poor through-plane resolution (i.e., large slice thickness). For such 2D imaging protocols, aliasing is also introduced in the through-plane direction, and these high-frequency artifacts cannot be removed by conventional interpolation. Super-resolution (SR) algorithms which can reduce aliasing artifacts and improve spatial resolution have previously been reported. State-of-the-art SR methods are mostly learning-based and require external training data consisting of paired low resolution (LR) and high resolution (HR) MR images. However, due to scanner limitations, such training data are often unavailable. This paper presents an anti-aliasing (AA) and self super-resolution (SSR) algorithm that needs no external training data. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. Our algorithm consists of three steps: 1) We build a self AA (SAA) deep network followed by 2) an SSR deep network, both of which can be applied along different orientations within the original images, and 3) recombine the multiple orientations output from Steps 1 and 2 using Fourier burst accumulation. We perform our SAA+SSR algorithm on a diverse collection of MR data without modification or preprocessing other than N4 inhomogeneity correction, and demonstrate significant improvement compared to competing SSR methods.

Magnetic resonance imaging based anatomical assessment of tongue impairment due to amyotrophic lateral sclerosis: A preliminary study

Amyotrophic Lateral Sclerosis (ALS) is a neurological disorder, which impairs tongue function for speech and swallowing. A widely used Diffusion Tensor Imaging (DTI) analysis pipeline is employed for quantifying differences in tongue fiber myoarchitecture between controls and ALS patients. This pipeline uses both high-resolution magnetic resonance imaging (hMRI) and DTI. hMRI is used to delineate tongue muscles, while DTI provides indices to reveal fiber connectivity within and between muscles. The preliminary results using five controls and two patients show quantitative differences between the groups. This work has the potential to provide insights into the detrimental effects of ALS on speech and swallowing.

Speech Map: A Statistical Multimodal Atlas of 4D Tongue Motion During Speech from Tagged and Cine MR Images

Quantitative measurement of functional and anatomical traits of 4D tongue motion in the course of speech or other lingual behaviors remains a major challenge in scientific research and clinical applications. Here, we introduce a statistical multimodal atlas of 4D tongue motion using healthy subjects, which enables a combined quantitative characterization of tongue motion in a reference anatomical configuration. This atlas framework, termed Speech Map, combines cine- and tagged-MRI in order to provide both the anatomic reference and motion information during speech. Our approach involves a series of steps including (1) construction of a common reference anatomical configuration from cine-MRI, (2) motion estimation from tagged-MRI, (3) transformation of the motion estimations to the reference anatomical configuration, and (4) computation of motion quantities such as Lagrangian strain. Using this framework, the anatomic configuration of the tongue appears motionless, while the motion fields and associated strain measurements change over the time course of speech. In addition, to form a succinct representation of the high-dimensional and complex motion fields, principal component analysis is carried out to characterize the central tendencies and variations of motion fields of our speech tasks. Our proposed method provides a platform to quantitatively and objectively explain the differences and variability of tongue motion by illuminating internal motion and strain that have so far been intractable. The findings are used to understand how tongue function for speech is limited by abnormal internal motion and strain in glossectomy patients.

Automatic quantification of bone marrow edema on MRI of the wrist in patients with early arthritis: A feasibility study

Purpose

To investigate the feasibility of automatic quantification of bone marrow edema (BME) on MRI of the wrist in patients with early arthritis.

Methods

For 485 early arthritis patients (clinically confirmed arthritis of one or more joints, symptoms for less than 2 years), MR scans of the wrist were processed in three automatic stages. First, super‐resolution reconstruction was applied to fuse coronal and axial scans into a single high‐resolution 3D image. Next, the carpal bones were located and delineated using atlas‐based segmentation. Finally, the extent of BME within each bone was quantified by identifying image intensity values characteristic of BME by fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within each bone. Correlation with visual BME scores was assessed through Pearson correlation coefficient.

Results

Pearson correlation between quantitative and visual BME scores across 485 patients was r=0.83, P<0.001.

Conclusions

Quantitative measurement of BME on MRI of the wrist has the potential to provide a feasible alternative to visual scoring. Complete automation requires automatic detection and compensation of acquisition artifacts. Magn Reson Med 79:1127–1134, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans

In magnetic resonance (MR), hardware limitations, scan time constraints, and patient movement often result in the acquisition of anisotropic 3-D MR images with limited spatial resolution in the out-of-plane views. Our goal is to construct an isotropic high-resolution (HR) 3-D MR image through upsampling and fusion of orthogonal anisotropic input scans. We propose a multiframe super-resolution (SR) reconstruction technique based on sparse representation of MR images. Our proposed algorithm exploits the correspondence between the HR slices and the low-resolution (LR) sections of the orthogonal input scans as well as the self-similarity of each input scan to train pairs of overcomplete dictionaries that are used in a sparse-land local model to upsample the input scans. The upsampled images are then combined using wavelet fusion and error backprojection to reconstruct an image. Features are learned from the data and no extra training set is needed. Qualitative and quantitative analyses were conducted to evaluate the proposed algorithm using simulated and clinical MR scans. Experimental results show that the proposed algorithm achieves promising results in terms of peak signal-to-noise ratio, structural similarity image index, intensity profiles, and visualization of small structures obscured in the LR imaging process due to partial volume effects. Our novel SR algorithm outperforms the nonlocal means (NLM) method using self-similarity, NLM method using self-similarity and image prior, self-training dictionary learning-based SR method, averaging of upsampled scans, and the wavelet fusion method. Our SR algorithm can reduce through-plane partial volume artifact by combining multiple orthogonal MR scans, and thus can potentially improve medical image analysis, research, and clinical diagnosis.

Variability in muscle activation of simple speech motions: A biomechanical modeling approach

Biomechanical models of the oropharynx facilitate the study of speech function by providing information that cannot be directly derived from imaging data, such as internal muscle forces and muscle activation patterns. Such models, when constructed and simulated based on anatomy and motion captured from individual speakers, enable the exploration of inter-subject variability of speech biomechanics. These models also allow one to answer questions, such as whether speakers produce similar sounds using essentially the same motor patterns with subtle differences, or vastly different motor equivalent patterns. Following this direction, this study uses speaker-specific modeling tools to investigate the muscle activation variability in two simple speech tasks that move the tongue forward (/ə-ɡis/) vs backward (/ə-suk/). Three dimensional tagged magnetic resonance imaging data were used to inversely drive the biomechanical models in four English speakers. Results show that the genioglossus is the workhorse muscle of the tongue, with activity levels of 10% in different subdivisions at different times. Jaw and hyoid positioners (inferior pterygoid and digastric) also show high activation during specific phonemes. Other muscles may be more involved in fine tuning the shapes. For example, slightly more activation of the anterior portion of the transverse is found during apical than laminal /s/, which would protrude the tongue tip to a greater extent for the apical /s/.

Deformable Registration-Based Super-resolution for Isotropic Reconstruction of 4-D MRI Volumes

Multi-plane super-resolution (SR) has been widely employed for resolution improvement of MR images. However, this has mostly been limited to MRI acquisitions with rigid motion. In cases of non-rigid motion, volumes are usually pre-registered using deformable registration methods before SR reconstruction. The pre-registered images are then used as input for the SR reconstruction. Since deformable registration involves smoothening of the inputs, using pre-registered inputs could lead to loss in information in SR reconstructions. Additionally, any registration errors present in pre-registered inputs could propagate throughout SR reconstructions leading to error accumulation. To address these limitations, in this study, we propose a deformable registration-based super-resolution reconstruction (DIRSR) reconstruction, which handles deformable registration as part of super-resolution. This approach has been demonstrated using 12 synthetic 4-D MRI lung datasets created using single plane (coronal) datasets of six patients and multi-plane (coronal and axial) 4-D lung MRI dataset of one patient. From our evaluation, DIRSR reconstructions are sharper and well aligned compared to reconstructions using SR of pre-registered inputs and rigid-registration SR. MSE, SNR and SSIM evaluations also indicate better reconstruction quality from DIRSR compared to reconstructions from SR of pre-registered inputs (p-value less than 0.0001). In conclusion, we found superior isotropic reconstructions of 4-D MR datasets from DIRSR reconstructions, which could benefit volumetric MR analyses.

A Four-dimensional Motion Field Atlas of the Tongue from Tagged and Cine Magnetic Resonance Imaging

Representation of human tongue motion using three-dimensional vector fields over time can be used to better understand tongue function during speech, swallowing, and other lingual behaviors. To characterize the inter-subject variability of the tongue's shape and motion of a population carrying out one of these functions it is desirable to build a statistical model of the four-dimensional (4D) tongue. In this paper, we propose a method to construct a spatio-temporal atlas of tongue motion using magnetic resonance (MR) images acquired from fourteen healthy human subjects. First, cine MR images revealing the anatomical features of the tongue are used to construct a 4D intensity image atlas. Second, tagged MR images acquired to capture internal motion are used to compute a dense motion field at each time frame using a phase-based motion tracking method. Third, motion fields from each subject are pulled back to the cine atlas space using the deformation fields computed during the cine atlas construction. Finally, a spatio-temporal motion field atlas is created to show a sequence of mean motion fields and their inter-subject variation. The quality of the atlas was evaluated by deforming cine images in the atlas space. Comparison between deformed and original cine images showed high correspondence. The proposed method provides a quantitative representation to observe the commonality and variability of the tongue motion field for the first time, and shows potential in evaluation of common properties such as strains and other tensors based on motion fields.

Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI

PURPOSE

To compare and evaluate the use of super-resolution reconstruction (SRR), in frequency, image, and wavelet domains, to reduce through-plane partial voluming effects in magnetic resonance imaging.

METHODS

The reconstruction of an isotropic high-resolution image from multiple thick-slice scans has been investigated through techniques in frequency, image, and wavelet domains. Experiments were carried out with thick-slice T2-weighted fast spin echo sequence on the Academic College of Radiology MRI phantom, where the reconstructed images were compared to a reference high-resolution scan using peak signal-to-noise ratio (PSNR), structural similarity image metric (SSIM), mutual information (MI), and the mean absolute error (MAE) of image intensity profiles. The application of super-resolution reconstruction was then examined in retrospective processing of clinical neuroimages of ten pediatric patients with tuberous sclerosis complex (TSC) to reduce through-plane partial voluming for improved 3D delineation and visualization of thin radial bands of white matter abnormalities.

RESULTS

Quantitative evaluation results show improvements in all evaluation metrics through super-resolution reconstruction in the frequency, image, and wavelet domains, with the highest values obtained from SRR in the image domain. The metric values for image-domain SRR versus the original axial, coronal, and sagittal images were PSNR = 32.26 vs 32.22, 32.16, 30.65; SSIM = 0.931 vs 0.922, 0.924, 0.918; MI = 0.871 vs 0.842, 0.844, 0.831; and MAE = 5.38 vs 7.34, 7.06, 6.19. All similarity metrics showed high correlations with expert ranking of image resolution with MI showing the highest correlation at 0.943. Qualitative assessment of the neuroimages of ten TSC patients through in-plane and out-of-plane visualization of structures showed the extent of partial voluming effect in a real clinical scenario and its reduction using SRR. Blinded expert evaluation of image resolution in resampled out-of-plane views consistently showed the superiority of SRR compared to original axial and coronal image acquisitions.

CONCLUSIONS

Thick-slice 2D T2-weighted MRI scans are part of many routine clinical protocols due to their high signal-to-noise ratio, but are often severely affected by through-plane partial voluming effects. This study shows that while radiologic assessment is performed in 2D on thick-slice scans, super-resolution MRI reconstruction techniques can be used to fuse those scans to generate a high-resolution image with reduced partial voluming for improved postacquisition processing. Qualitative and quantitative evaluation showed the efficacy of all SRR techniques with the best results obtained from SRR in the image domain. The limitations of SRR techniques are uncertainties in modeling the slice profile, density compensation, quantization in resampling, and uncompensated motion between scans.

Registration and Modeling From Spaced and Misaligned Image Volumes

We address the problem of object modeling from 3D and 3D+T data made up of images, which contain different parts of an object of interest, are separated by large spaces, and are misaligned with respect to each other. These images have only a limited number of intersections, hence making their registration particularly challenging. Furthermore, such data may result from various medical imaging modalities and can, therefore, present very diverse spatial configurations. Previous methods perform registration and object modeling (segmentation and interpolation) sequentially. However, sequential registration is ill-suited for the case of images with few intersections. We propose a new methodology, which, regardless of the spatial configuration of the data, performs the three stages of registration, segmentation, and shape interpolation from spaced and misaligned images simultaneously. We integrate these three processes in a level set framework, in order to benefit from their synergistic interactions. We also propose a new registration method that exploits segmentation information rather than pixel intensities, and that accounts for the global shape of the object of interest, for increased robustness and accuracy. The accuracy of registration is compared against traditional mutual information based methods, and the total modeling framework is assessed against traditional sequential processing and validated on artificial, CT, and MRI data.

Analysis of 3-D Tongue Motion From Tagged and Cine Magnetic Resonance Images

PURPOSE

Measuring tongue deformation and internal muscle motion during speech has been a challenging task because the tongue deforms in 3 dimensions, contains interdigitated muscles, and is largely hidden within the vocal tract. In this article, a new method is proposed to analyze tagged and cine magnetic resonance images of the tongue during speech in order to estimate 3-dimensional tissue displacement and deformation over time.

METHOD

The method involves computing 2-dimensional motion components using a standard tag-processing method called harmonic phase, constructing superresolution tongue volumes using cine magnetic resonance images, segmenting the tongue region using a random-walker algorithm, and estimating 3-dimensional tongue motion using an incompressible deformation estimation algorithm.

RESULTS

Evaluation of the method is presented with a control group and a group of people who had received a glossectomy carrying out a speech task. A 2-step principal-components analysis is then used to reveal the unique motion patterns of the subjects. Azimuth motion angles and motion on the mirrored hemi-tongues are analyzed.

CONCLUSION

Tests of the method with a various collection of subjects show its capability of capturing patient motion patterns and indicate its potential value in future speech studies.

A Spatio-Temporal Atlas and Statistical Model of the Tongue During Speech from Cine-MRI

Statistical modeling of tongue motion during speech using cine magnetic resonance imaging (MRI) provides key information about the relationship between structure and motion of the tongue. In order to study the variability of tongue shape and motion in populations, a consistent integration and characterization of inter-subject variability is needed. In this paper, a method to construct a spatio-temporal atlas comprising a mean motion model and statistical modes of variation during speech is presented. The model is based on the cine-MRI from twenty two normal speakers and consists of several steps involving both spatial and temporal alignment problems independently. First, all images are registered into a common reference space, which is taken to be a neutral resting position of the tongue. Second, the tongue shapes of each individual relative to this reference space are produced. Third, a time warping approach (several are evaluated) is used to align the time frames of each subject to a common time series of initial mean images. Finally, the spatio-temporal atlas is created by time-warping each subject, generating new mean images at each time, and producing shape statistics around these mean images using principal component analysis at each reference time frame. Experimental results consist of comparison of various parameters and methods in creation of the atlas and a demonstration of the final modes of variations at various key time frames in a sample phrase.

Structure and variability in human tongue muscle anatomy

The human tongue has a complex architecture, consistent with its complex roles in eating, speaking and breathing. Tongue muscle architecture has been depicted in drawings and photographs, but not quantified volumetrically. This paper aims to fill that gap by measuring the muscle architecture of the tongue for 14 people captured in high-resolution 3D MRI volumes. The results show the structure, relationships and variability among the muscles, as well as the effects of age, gender and weight on muscle volume. Since the tongue consists of partially interdigitated muscles, we consider the muscle volumes in two ways. The functional muscle volume encompasses the region of the tongue served by the muscle. The structural volume halves the volume of the muscle in regions where it interdigitates with other muscles. Results show similarity of scaling across subjects, and speculate on functional effects of the anatomical structure.

Construction of An Unbiased Spatio-Temporal Atlas of the Tongue During Speech

Quantitative characterization and comparison of tongue motion during speech and swallowing present fundamental challenges because of striking variations in tongue structure and motion across subjects. A reliable and objective description of the dynamics tongue motion requires the consistent integration of inter-subject variability to detect the subtle changes in populations. To this end, in this work, we present an approach to constructing an unbiased spatio-temporal atlas of the tongue during speech for the first time, based on cine-MRI from twenty two normal subjects. First, we create a common spatial space using images from the reference time frame, a neutral position, in which the unbiased spatio-temporal atlas can be created. Second, we transport images from all time frames of all subjects into this common space via the single transformation. Third, we construct atlases for each time frame via groupwise diffeomorphic registration, which serves as the initial spatio-temporal atlas. Fourth, we update the spatio-temporal atlas by realigning each time sequence based on the Lipschitz norm on diffeomorphisms between each subject and the initial atlas. We evaluate and compare different configurations such as similarity measures to build the atlas. Our proposed method permits to accurately and objectively explain the main pattern of tongue surface motion.

Subject-Specific Biomechanical Modelling of the Oropharynx: Towards Speech Production

Biomechanical models of the oropharynx are beneficial to treatment planning of speech impediments by providing valuable insight into the speech function such as motor control. In this paper, we develop a subject-specific model of the oropharynx and investigate its utility in speech production. Our approach adapts a generic tongue-jaw-hyoid model (Stavness et al. 2011) to fit and track dynamic volumetric MRI data of a normal speaker, subsequently coupled to a source-filter based acoustic synthesizer. We demonstrate our model's ability to track tongue tissue motion, simulate plausible muscle activation patterns, as well as generate acoustic results that have comparable spectral features to the associated recorded audio. Finally, we propose a method to adjust the spatial resolution of our subject-specific tongue model to match the fidelity level of our MRI data and speech synthesizer. Our findings suggest that a higher resolution tongue model - using similar muscle fibre definition - does not show a significant improvement in acoustic performance, for our speech utterance and at this level of fidelity; however we believe that our approach enables further refinements of the muscle fibres suitable for studying longer speech sequences and finer muscle innervation using higher resolution dynamic data.

Relating Speech Production to Tongue Muscle Compressions Using Tagged and High-resolution Magnetic Resonance Imaging

The human tongue is composed of multiple internal muscles that work collaboratively during the production of speech. Assessment of muscle mechanics can help understand the creation of tongue motion, interpret clinical observations, and predict surgical outcomes. Although various methods have been proposed for computing the tongue's motion, associating motion with muscle activity in an interdigitated fiber framework has not been studied. In this work, we aim to develop a method that reveals different tongue muscles' activities in different time phases during speech. We use four-dimensional tagged magnetic resonance (MR) images and static high-resolution MR images to obtain tongue motion and muscle anatomy, respectively. Then we compute strain tensors and local tissue compression along the muscle fiber directions in order to reveal their shortening pattern. This process relies on the support from multiple image analysis methods, including super-resolution volume reconstruction from MR image slices, segmentation of internal muscles, tracking the incompressible motion of tissue points using tagged images, propagation of muscle fiber directions over time, and calculation of strain in the line of action, etc. We evaluated the method on a control subject and two post-glossectomy patients in a controlled speech task. The normal subject's tongue muscle activity shows high correspondence with the production of speech in different time instants, while both patients' muscle activities show different patterns from the control due to their resected tongues. This method shows potential for relating overall tongue motion to particular muscle activity, which may provide novel information for future clinical and scientific studies.

Effects of higher order propagation modes in vocal tract like geometries

In this paper, a multimodal theory accounting for higher order acoustical propagation modes is presented as an extension to the classical plane wave theory. This theoretical development is validated against experiments on vocal tract replicas, obtained using a 3D printer and finite element simulations. Simplified vocal tract geometries of increasing complexity are used to investigate the influence of some geometrical parameters on the acoustical properties of the vocal tract. It is shown that the higher order modes can produce additional resonances and anti-resonances and can also strongly affect the radiated sound. These effects appear to be dependent on the eccentricity and the cross-sectional shape of the geometries. Finally, the comparison between the simulations and the experiments points out the importance of taking visco-thermal losses into account to increase the accuracy of the resonance bandwidths prediction.

Segmentation of tongue muscles from super-resolution magnetic resonance images

Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.

Semi-automatic segmentation for 3D motion analysis of the tongue with dynamic MRI

Dynamic MRI has been widely used to track the motion of the tongue and measure its internal deformation during speech and swallowing. Accurate segmentation of the tongue is a prerequisite step to define the target boundary and constrain the tracking to tissue points within the tongue. Segmentation of 2D slices or 3D volumes is challenging because of the large number of slices and time frames involved in the segmentation, as well as the incorporation of numerous local deformations that occur throughout the tongue during motion. In this paper, we propose a semi-automatic approach to segment 3D dynamic MRI of the tongue. The algorithm steps include seeding a few slices at one time frame, propagating seeds to the same slices at different time frames using deformable registration, and random walker segmentation based on these seed positions. This method was validated on the tongue of five normal subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of a total of 130 volumes showed an average dice similarity coefficient (DSC) score of 0.92 with less segmented volume variability between time frames than in manual segmentations.

A High-resolution Atlas and Statistical Model of the Vocal Tract from Structural MRI

Magnetic resonance imaging (MRI) is an essential tool in the study of muscle anatomy and functional activity in the tongue. Objective assessment of similarities and differences in tongue structure and function has been performed using unnormalized data, but this is biased by the differences in size, shape, and orientation of the structures. To remedy this, we propose a methodology to build a 3D vocal tract atlas based on structural MRI volumes from twenty normal subjects. We first constructed high-resolution volumes from three orthogonal stacks. We then removed extraneous data so that all 3D volumes contained the same anatomy. We used an unbiased diffeomorphic groupwise registration using a cross-correlation similarity metric. Principal component analysis was applied to the deformation fields to create a statistical model from the atlas. Various evaluations and applications were carried out to show the behaviour and utility of the atlas.

Speech MRI: morphology and function

Magnetic Resonance Imaging (MRI) plays an increasing role in the study of speech. This article reviews the MRI literature of anatomical imaging, imaging for acoustic modelling and dynamic imaging. It describes existing imaging techniques attempting to meet the challenges of imaging the upper airway during speech and examines the remaining hurdles and future research directions.

Tissue-point motion tracking in the tongue from cine MRI and tagged MRI

PURPOSE Accurate tissue motion tracking within the tongue can help professionals diagnose and treat vocal tract-related disorders, evaluate speech quality before and after surgery, and conduct various scientific studies. The authors compared tissue tracking results from 4 widely used deformable registration (DR) methods applied to cine magnetic resonance imaging (MRI) with harmonic phase (HARP)-based tracking applied to tagged MRI. METHOD Ten subjects repeated the phrase "a geese" multiple times while sagittal images of the head were collected at 26 Hz, first in a tagged MRI data set and then in a cine MRI data set. HARP tracked the motion of 8 specified tissue points in the tagged data set. Four DR methods including diffeomorphic demons and free-form deformations based on cubic B-spline with 3 different similarity measures were used to track the same 8 points in the cine MRI data set. Individual points were tracked and length changes of several muscles were calculated using the DR- and HARP-based tracking methods. RESULTS The results showed that the DR tracking errors were nonsystematic and varied in direction, amount, and timing across speakers and within speakers. Comparison of HARP and DR tracking with manual tracking showed better tracking results for HARP except at the tongue surface, where mistracking caused greater errors in HARP than DR. CONCLUSIONS Tissue point tracking using DR tracking methods contains nonsystematic tracking errors within and across subjects, making it less successful than tagged MRI tracking within the tongue. However, HARP sometimes mistracks points at the tongue surface of tagged MRI because of its limited bandpass filter and tag pattern fading, so that DR has better success measuring surface tissue points on cine MRI than HARP does. Therefore, a hybrid method is being explored.

3D tongue motion from tagged and cine MR images

Understanding the deformation of the tongue during human speech is important for head and neck surgeons and speech and language scientists. Tagged magnetic resonance (MR) imaging can be used to image 2D motion, and data from multiple image planes can be combined via post-processing to yield estimates of 3D motion. However, lacking boundary information, this approach suffers from inaccurate estimates near the tongue surface. This paper describes a method that combines two sources of information to yield improved estimation of 3D tongue motion. The method uses the harmonic phase (HARP) algorithm to extract motion from tags and diffeomorphic demons to provide surface deformation. It then uses an incompressible deformation estimation algorithm to incorporate both sources of displacement information to form an estimate of the 3D whole tongue motion. Experimental results show that use of combined information improves motion estimation near the tongue surface, a problem that has previously been reported as problematic in HARP analysis, while preserving accurate internal motion estimates. Results on both normal and abnormal tongue motions are shown.

Isotropic reconstruction of a 4-D MRI thoracic sequence using super-resolution

PURPOSE

Four-dimensional (4D) thoracic magnetic resonance imaging (MRI) sequences have been shown to successfully monitor both tumor and lungs anatomy. However, a high temporal resolution is required to avoid motion artifacts, which leads to volumes with poor spatial resolution. This article proposes to reconstruct an isotropic 4D MRI thoracic sequence with minimum modifications to the acquisition protocols. This could be an important step toward the use of 4D MRI for thoracic radiotherapy applications.

METHODS

In a postacquisition step, three orthogonal 4D anisotropic acquisitions are combined using super-resolution to reconstruct a series of isotropic volumes. A new phantom that simulates lung tumor motion is developed to evaluate the performance of the algorithm. The proposed framework is also applied to real data of a lung cancer patient.

RESULTS

Subjective and objective evaluations show clear resolution enhancement and partial volume effect diminution. The isotropic reconstruction of patient data significantly improves both the visualization and segmentation of thoracic structures.

CONCLUSIONS

The results presented here are encouraging and suggest that super-resolution can be regarded as an efficient method to improve the resolution of 4D MRI sequences. It produces an isotropic 4D sequence that would be impossible to acquire in practice. Further investigations will be required to evaluate its reproducibility in various clinical applications.

Integrated segmentation and interpolation of sparse data

We address the two inherently related problems of segmentation and interpolation of 3D and 4D sparse data and propose a new method to integrate these stages in a level set framework. The interpolation process uses segmentation information rather than pixel intensities for increased robustness and accuracy. The method supports any spatial configurations of sets of 2D slices having arbitrary positions and orientations. We achieve this by introducing a new level set scheme based on the interpolation of the level set function by radial basis functions. The proposed method is validated quantitatively and/or subjectively on artificial data and MRI and CT scans and is compared against the traditional sequential approach, which interpolates the images first, using a state-of-the-art image interpolation method, and then segments the interpolated volume in 3D or 4D. In our experiments, the proposed framework yielded similar segmentation results to the sequential approach but provided a more robust and accurate interpolation. In particular, the interpolation was more satisfactory in cases of large gaps, due to the method taking into account the global shape of the object, and it recovered better topologies at the extremities of the shapes where the objects disappear from the image slices. As a result, the complete integrated framework provided more satisfactory shape reconstructions than the sequential approach.

Comparison of super resolution reconstruction acquisition geometries for use in mouse phenotyping

3D isotropic imaging at high spatial resolution (30-100 microns) is important for comparing mouse phenotypes. 3D imaging at high spatial resolutions is limited by long acquisition times and is not possible in many in vivo settings. Super resolution reconstruction (SRR) is a postprocessing technique that has been proposed to improve spatial resolution in the slice-select direction using multiple 2D multislice acquisitions. Any 2D multislice acquisition can be used for SRR. In this study, the effects of using three different low-resolution acquisition geometries (orthogonal, rotational, and shifted) on SRR images were evaluated and compared to a known standard. Iterative back projection was used for the reconstruction of all three acquisition geometries. The results of the study indicate that super resolution reconstructed images based on orthogonally acquired low-resolution images resulted in reconstructed images with higher SNR and CNR in less acquisition time than those based on rotational and shifted acquisition geometries. However, interpolation artifacts were observed in SRR images based on orthogonal acquisition geometry, particularly when the slice thickness was greater than six times the inplane voxel size. Reconstructions based on rotational geometry appeared smoother than those based on orthogonal geometry, but they required two times longer to acquire than the orthogonal LR images.

Improved vocal tract reconstruction and modeling using an image super-resolution technique

Magnetic resonance imaging has been widely used in speech production research. Often only one image stack (sagittal, axial, or coronal) is used for vocal tract modeling. As a result, complementary information from other available stacks is not utilized. To overcome this, a recently developed super-resolution technique was applied to integrate three orthogonal low-resolution stacks into one isotropic volume. The results on vowels show that the super-resolution volume produces better vocal tract visualization than any of the low-resolution stacks. Its derived area functions generally produce formant predictions closer to the ground truth, particularly for those formants sensitive to area perturbations at constrictions.

SEMI-AUTOMATIC SEGMENTATION OF THE TONGUE FOR 3D MOTION ANALYSIS WITH DYNAMIC MRI

Accurate segmentation is an important preprocessing step for measuring the internal deformation of the tongue during speech and swallowing using 3D dynamic MRI. In an MRI stack, manual segmentation of every 2D slice and time frame is time-consuming due to the large number of volumes captured over the entire task cycle. In this paper, we propose a semi-automatic segmentation workflow for processing 3D dynamic MRI of the tongue. The steps comprise seeding a few slices, seed propagation by deformable registration, random walker segmentation of the temporal stack of images and 3D super-resolution volumes. This method was validated on the tongue of two subjects carrying out the same speech task with multi-slice 2D dynamic cine-MR images obtained at three orthogonal orientations and 26 time frames. The resulting semi-automatic segmentations of 52 volumes showed an average dice similarity coefficient (DSC) score of 0.9 with reduced segmented volume variability compared to manual segmentations.