Comparison of prospective and retrospective motion correction in 3D-encoded neuroanatomical MRI.

PURPOSE: To compare prospective motion correction (PMC) and retrospective motion correction (RMC) in Cartesian 3D-encoded MPRAGE scans and to investigate the effects of correction frequency and parallel imaging on the performance of RMC. METHODS: Head motion was estimated using a markerless tracking system and sent to a modified MPRAGE sequence, which can continuously update the imaging FOV to perform PMC. The prospective correction was applied either before each echo train (before-ET) or at every sixth readout within the ET (within-ET). RMC was applied during image reconstruction by adjusting k-space trajectories according to the measured motion. The motion correction frequency was retrospectively increased with RMC or decreased with reverse RMC. Phantom and in vivo experiments were used to compare PMC and RMC, as well as to compare within-ET and before-ET correction frequency during continuous motion. The correction quality was quantitatively evaluated using the structural similarity index measure with a reference image without motion correction and without intentional motion. RESULTS: PMC resulted in superior image quality compared to RMC both visually and quantitatively. Increasing the correction frequency from before-ET to within-ET reduced the motion artifacts in RMC. A hybrid PMC and RMC correction, that is, retrospectively increasing the correction frequency of before-ET PMC to within-ET, also reduced motion artifacts. Inferior performance of RMC compared to PMC was shown with GRAPPA calibration data without intentional motion and without any GRAPPA acceleration. CONCLUSION: Reductions in local Nyquist violations with PMC resulted in superior image quality compared to RMC. Increasing the motion correction frequency to within-ET reduced the motion artifacts in both RMC and PMC.

In Vivo Imaging of the Buccal Mucosa Shows Loss of the Endothelial Glycocalyx and Perivascular Hemorrhages in Pediatric Plasmodium falciparum Malaria.

Severe malaria is mostly caused by Plasmodium falciparum, resulting in considerable, systemic inflammation and pronounced endothelial activation. The endothelium forms an interface between blood and tissue, and vasculopathy has previously been linked with malaria severity. We studied the extent to which the endothelial glycocalyx that normally maintains endothelial function is involved in falciparum malaria pathogenesis by using incident dark-field imaging in the buccal mucosa. This enabled calculation of the perfused boundary region, which indicates to what extent erythrocytes can permeate the endothelial glycocalyx. The perfused boundary region was significantly increased in severe malaria patients and mirrored by an increase of soluble glycocalyx components in plasma. This is suggestive of a substantial endothelial glycocalyx loss. Patients with severe malaria had significantly higher plasma levels of sulfated glycosaminoglycans than patients with uncomplicated malaria, whereas other measured glycocalyx markers were raised to a comparable extent in both groups. In severe malaria, the plasma level of the glycosaminoglycan hyaluronic acid was positively correlated with the perfused boundary region in the buccal cavity. Plasma hyaluronic acid and heparan sulfate were particularly high in severe malaria patients with a low Blantyre coma score, suggesting involvement in its pathogenesis. In vivo imaging also detected perivascular hemorrhages and sequestering late-stage parasites. In line with this, plasma angiopoietin-1 was decreased while angiopoietin-2 was increased, suggesting vascular instability. The density of hemorrhages correlated negatively with plasma levels of angiopoietin-1. Our findings indicate that as with experimental malaria, the loss of endothelial glycocalyx is associated with vascular dysfunction in human malaria and is related to severity.

Quantifying the Financial Savings of Motion Correction in Brain MRI: A Model-Based Estimate of the Costs Arising From Patient Head Motion and Potential Savings From Implementation of Motion Correction.

BACKGROUND: Patient head motion is a major concern in clinical brain MRI, as it reduces the diagnostic image quality and may increase examination time and cost. PURPOSE: To investigate the prevalence of MR images with significant motion artifacts on a given clinical scanner and to estimate the potential financial cost savings of applying motion correction to clinical brain MRI examinations. STUDY TYPE: Retrospective. SUBJECTS: In all, 173 patients undergoing a PET/MRI dementia protocol and 55 pediatric patients undergoing a PET/MRI brain tumor protocol. The total scan time of the two protocols were 17 and 40 minutes, respectively. FIELD STRENGTH/SEQUENCES: 3 T, Siemens mMR Biograph, MPRAGE, DWI, T1 and T2 -weighted FLAIR, T2 -weighted 2D-FLASH, T2 -weighted TSE. ASSESSMENT: A retrospective review of image sequences from a given clinical MRI scanner was conducted to investigate the prevalence of motion-corrupted images. The review was performed by three radiologists with different levels of experience using a three-step semiquantitative scale to classify the quality of the images. A total of 1013 sequences distributed on 228 MRI examinations were reviewed. The potential cost savings of motion correction were estimated by a cost estimation for our country with assumptions. STATISTICAL TEST: The cost estimation was conducted with a 20% lower and upper bound on the model assumptions to include the uncertainty of the assumptions. RESULTS: 7.9% of the sequences had motion artifacts that decreased the interpretability, while 2.0% of the sequences had motion artifacts causing the images to be nondiagnostic. The estimated annual cost to the clinic/hospital due to patient head motion per scanner was $45,066 without pediatric examinations and $364,242 with pediatric examinations. DATA CONCLUSION: The prevalence of a motion-corrupted image was found in 2.0% of the reviewed sequences. Based on the model, repayment periods are presented as a function of the price for applying motion correction and its performance. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 6 J. Magn. Reson. Imaging 2020;52:731-738.

Measuring movements in adolescents with psychosis using the Microsoft Kinect sensor: a pilot study exploring a new tool for assessing aspects of antipsychotic-induced parkinsonism.

BACKGROUND: The assessment of motor disturbances in antipsychotic-treated adolescent patients is often limited to the use of observer-based rating scales with interobserver variability. The objectives of this pilot study were to measure movement patterns associated with antipsychotic-induced parkinsonism in young patients with psychosis and initiating/treated with antipsychotics, using a computer application connected with the Microsoft Kinect sensor (Motorgame). METHOD: All participants were assessed by neurological examination, clinical side effect rating scales (Udvalg for Kliniske Undersøgelser Side Effect Rating Scale, Barnes Akathisia Rating Scale, Simpson Angus Scale (SAS), and Abnormal Involuntary Movement Scale), and the Motorgame. Furthermore, speed of information processing and motor speed with subtests from the Brief Assessment of Cognition in Schizophrenia test battery was assessed. RESULTS: We included 21 adolescents with first-episode psychosis (62% treated with antipsychotics; males 38%; mean age 16 ± 1.4 years) and 69 healthy controls (males 36%; mean age 16 ± 1.5 years). Prolonged time of motor performance (TOMP) in the Motorgame was associated with higher SAS scores for arm dropping (p = .009). A consistent practice effect was detected (p < .001). We found no significant associations between TOMP and age, height, body weight, sex, antipsychotic dosage, or information processing speed. CONCLUSIONS: We found an uncorrected significant association between prolonged TOMP and shoulder bradykinesia. The Motorgame was found useful in assessing parkinsonian symptoms in early-onset psychosis and accepted by participants. Future studies of larger cohorts, including patients with high scores in clinical motor side effect scales, are required to establish solid validity of the novel test.

3D facial landmarks: Inter-operator variability of manual annotation.

BACKGROUND: Manual annotation of landmarks is a known source of variance, which exist in all fields of medical imaging, influencing the accuracy and interpretation of the results. However, the variability of human facial landmarks is only sparsely addressed in the current literature as opposed to e.g. the research fields of orthodontics and cephalometrics. We present a full facial 3D annotation procedure and a sparse set of manually annotated landmarks, in effort to reduce operator time and minimize the variance. METHOD: Facial scans from 36 voluntary unrelated blood donors from the Danish Blood Donor Study was randomly chosen. Six operators twice manually annotated 73 anatomical and pseudo-landmarks, using a three-step scheme producing a dense point correspondence map. We analyzed both the intra- and inter-operator variability, using mixed-model ANOVA. We then compared four sparse sets of landmarks in order to construct a dense correspondence map of the 3D scans with a minimum point variance. RESULTS: The anatomical landmarks of the eye were associated with the lowest variance, particularly the center of the pupils. Whereas points of the jaw and eyebrows have the highest variation. We see marginal variability in regards to intra-operator and portraits. Using a sparse set of landmarks (n=14), that capture the whole face, the dense point mean variance was reduced from 1.92 to 0.54 mm. CONCLUSION: The inter-operator variability was primarily associated with particular landmarks, where more leniently landmarks had the highest variability. The variables embedded in the portray and the reliability of a trained operator did only have marginal influence on the variability. Further, using 14 of the annotated landmarks we were able to reduced the variability and create a dense correspondences mesh to capture all facial features.

Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images.

Detection of abnormalities within the inner ear is a challenging task even for experienced clinicians. In this study, we propose an automated method for automatic abnormality detection to provide support for the diagnosis and clinical management of various otological disorders. We propose a framework for inner ear abnormality detection based on deep reinforcement learning for landmark detection which is trained uniquely in normative data. In our approach, we derive two abnormality measurements: Dimage and Uimage. The first measurement, Dimage, is based on the variability of the predicted configuration of a well-defined set of landmarks in a subspace formed by the point distribution model of the location of those landmarks in normative data. We create this subspace using Procrustes shape alignment and Principal Component Analysis projection. The second measurement, Uimage, represents the degree of hesitation of the agents when approaching the final location of the landmarks and is based on the distribution of the predicted Q-values of the model for the last ten states. Finally, we unify these measurements in a combined anomaly measurement called Cimage. We compare our method's performance with a 3D convolutional autoencoder technique for abnormality detection using the patch-based mean squared error between the original and the generated image as a basis for classifying abnormal versus normal anatomies. We compare both approaches and show that our method, based on deep reinforcement learning, shows better detection performance for abnormal anatomies on both an artificial and a real clinical CT dataset of various inner ear malformations with an increase of 11.2% of the area under the ROC curve. Our method also shows more robustness against the heterogeneous quality of the images in our dataset.

Multi-modal deep learning for joint prediction of otitis media and diagnostic difficulty.

Objectives: In this study, we propose a diagnostic model for automatic detection of otitis media based on combined input of otoscopy images and wideband tympanometry measurements. Methods: We present a neural network-based model for the joint prediction of otitis media and diagnostic difficulty. We use the subclassifications acute otitis media and otitis media with effusion. The proposed approach is based on deep metric learning, and we compare this with the performance of a standard multi-task network. Results: The proposed deep metric approach shows good performance on both tasks, and we show that the multi-modal input increases the performance for both classification and difficulty estimation compared to the models trained on the modalities separately. An accuracy of 86.5% is achieved for the classification task, and a Kendall rank correlation coefficient of 0.45 is achieved for difficulty estimation, corresponding to a correct ranking of 72.6% of the cases. Conclusion: This study demonstrates the strengths of a multi-modal diagnostic tool using both otoscopy images and wideband tympanometry measurements for the diagnosis of otitis media. Furthermore, we show that deep metric learning improves the performance of the models.

A Deep Learning Approach for Detecting Otitis Media From Wideband Tympanometry Measurements.

OBJECTIVE: In this study, wepropose an automatic diagnostic algorithm for detecting otitis media based on wideband tympanometry measurements. METHODS: We develop a convolutional neural network for classification of otitis media based on the analysis of the wideband tympanogram. Saliency maps are computed to gain insight into the decision process of the convolutional neural network. Finally, we attempt to distinguish between otitis media with effusion and acute otitis media, a clinical subclassification important for the choice of treatment. RESULTS: The approach shows high performance on the overall otitis media detection with an accuracy of 92.6%. However, the approach is not able to distinguish between specific types of otitis media. CONCLUSION: Out approach can detect otitis media with high accuracy and the wideband tympanogram holds more diagnostic information than the commonly used techniques wideband absorbance measurements and simple tympanograms. SIGNIFICANCE: This study shows how advanced deep learning methods enable automatic diagnosis of otitis media based on wideband tympanometry measurements, which could become a valuable diagnostic tool.

Inter-rater reliability of the diagnosis of otitis media based on otoscopic images and wideband tympanometry measurements.

OBJECTIVES: This study aims to investigate the inter-rater reliability and agreement of the diagnosis of otitis media with effusion, acute otitis media, and no effusion cases based on an otoscopy image and in some cases an additional wideband tympanometry measurement of the patient. METHODS: 1409 cases were examined and diagnosed by an otolaryngologist in the clinic, and otoscopy examination and wideband tympanometry (WBT) measurement were conducted. Afterwards, four otolaryngologists (Ear, Nose, and Throat doctors, ENTs), who did not perform the acute examination of the patients, evaluated the otoscopy images and WBT measurements results for diagnosis (acute otitis media, otitis media with effusion, or no effusion). They also specified their diagnostic certainty for each case, and reported whether they used the image, wideband tympanometry, or both, for diagnosis. RESULTS: All four ENTs agreed on the diagnosis in 57% of the cases, with a pairwise agreement of 74%, and a Light's Kappa of 0.58. There are, however, large differences in agreement and certainty between the three diagnoses. Acute otitis media yields the highest agreement (77% between all four ENTs) and certainty (0.90), while no effusion shows much lower agreement and certainty (34% and 0.58, respectively). There is a positive correlation between certainty and agreement between the ENTs across all cases, and both certainty and agreement increase for cases where a WBT measurement is shown in addition to the otoscopy image. CONCLUSIONS: The inter-rater reliability between four ENTs was high when diagnosing acute otitis media and lower when diagnosing otitis media with effusion. However, WBT can add valuable information to get closer to the ground-truth diagnosis without myringotomy. Furthermore, the diagnostic certainty increases when the WBT is examined together with the otoscopy image.

Deep metric learning for otitis media classification.

In this study, we propose an automatic diagnostic algorithm for detecting otitis media based on otoscopy images of the tympanic membrane. A total of 1336 images were assessed by a medical specialist into three diagnostic groups: acute otitis media, otitis media with effusion, and no effusion. To provide proper treatment and care and limit the use of unnecessary antibiotics, it is crucial to correctly detect tympanic membrane abnormalities, and to distinguish between acute otitis media and otitis media with effusion. The proposed approach for this classification task is based on deep metric learning, and this study compares the performance of different distance-based metric loss functions. Contrastive loss, triplet loss and multi-class N-pair loss are employed, and compared with the performance of standard cross-entropy and class-weighted cross-entropy classification networks. Triplet loss achieves high precision on a highly imbalanced data set, and the deep metric methods provide useful insight into the decision making of a neural network. The results are comparable to the best clinical experts and paves the way for more accurate and operator-independent diagnosis of otitis media.

Deep Learning Framework for Real-Time Estimation of in-silico Thrombotic Risk Indices in the Left Atrial Appendage.

Patient-specific computational fluid dynamics (CFD) simulations can provide invaluable insight into the interaction of left atrial appendage (LAA) morphology, hemodynamics, and the formation of thrombi in atrial fibrillation (AF) patients. Nonetheless, CFD solvers are notoriously time-consuming and computationally demanding, which has sparked an ever-growing body of literature aiming to develop surrogate models of fluid simulations based on neural networks. The present study aims at developing a deep learning (DL) framework capable of predicting the endothelial cell activation potential (ECAP), an in-silico index linked to the risk of thrombosis, typically derived from CFD simulations, solely from the patient-specific LAA morphology. To this end, a set of popular DL approaches were evaluated, including fully connected networks (FCN), convolutional neural networks (CNN), and geometric deep learning. While the latter directly operated over non-Euclidean domains, the FCN and CNN approaches required previous registration or 2D mapping of the input LAA mesh. First, the superior performance of the graph-based DL model was demonstrated in a dataset consisting of 256 synthetic and real LAA, where CFD simulations with simplified boundary conditions were run. Subsequently, the adaptability of the geometric DL model was further proven in a more realistic dataset of 114 cases, which included the complete patient-specific LA and CFD simulations with more complex boundary conditions. The resulting DL framework successfully predicted the overall distribution of the ECAP in both datasets, based solely on anatomical features, while reducing computational times by orders of magnitude compared to conventional CFD solvers.

Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans.

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Technology Analytics and Genomics in Sleep (STAGES) across 11 US and Canadian sleep clinics and analyzed 3D craniofacial scans with the goal of predicting AHI, as measured using gold standard nocturnal polysomnography (PSG). First, the algorithm detects pre-specified landmarks on mesh objects and aligns scans in 3D space. Subsequently, 2D images and depth maps are generated by rendering and rotating scans by 45-degree increments. Resulting images were stacked as channels and used as input to multi-view convolutional neural networks, which were trained and validated in a supervised manner to predict AHI values derived from PSGs. The proposed model achieved a mean absolute error of 11.38 events/hour, a Pearson correlation coefficient of 0.4, and accuracy for predicting OSA of 67% using 10-fold cross-validation. The model improved further by adding patient demographics and variables from questionnaires. We also show that the model performed at the level of three sleep medicine specialists, who used clinical experience to predict AHI based on 3D scan displays. Finally, we created topographic displays of the most important facial features used by the model to predict AHI, showing importance of the neck and chin area. The proposed algorithm has potential to serve as an inexpensive and efficient screening tool for individuals with suspected OSA.

Markov random field surface reconstruction.

A method for implicit surface reconstruction is proposed. The novelty in this paper is the adaptation of Markov Random Field regularization of a distance field. The Markov Random Field formulation allows us to integrate both knowledge about the type of surface we wish to reconstruct (the prior) and knowledge about data (the observation model) in an orthogonal fashion. Local models that account for both scene-specific knowledge and physical properties of the scanning device are described. Furthermore, how the optimal distance field can be computed is demonstrated using conjugate gradients, sparse Cholesky factorization, and a multiscale iterative optimization scheme. The method is demonstrated on a set of scanned human heads and, both in terms of accuracy and the ability to close holes, the proposed method is shown to have similar or superior performance when compared to current state-of-the-art algorithms.

Prediction of Patient Demographics using 3D Craniofacial Scans and Multi-view CNNs.

3D data is becoming increasingly popular and accessible for computer vision tasks. A popular format for 3D data is the mesh format, which can depict a 3D surface accurately and cost-effectively by connecting points in the (x, y, z) plane, known as vertices, into triangles that can be combined to approximate geometrical surfaces. However, mesh objects are not suitable for standard deep learning techniques due to their non-euclidean structure. We present an algorithm which predicts the sex, age, and body mass index of a subject based on a 3D scan of their face and neck. This algorithm relies on an automatic pre-processing technique, which renders and captures the 3D scan from eight different angles around the x-axis in the form of 2D images and depth maps. Subsequently, the generated data is used to train three convolutional neural networks, each with a ResNet18 architecture, to learn a mapping between the set of 16 images per subject (eight 2D images and eight depth maps from different angles) and their demographics. For age and body mass index, we achieved a mean absolute error of 7.77 years and 4.04 kg/m2 on the respective test sets, while Pearson correlation coefficients of 0.76 and 0.80 were obtained, respectively. The prediction of sex yielded an accuracy of 93%. The developed framework serves as a proof of concept for prediction of more clinically relevant variables based on 3D craniofacial scans stored in mesh objects.

Exploring Movement Impairments in Patients With Parkinson's Disease Using the Microsoft Kinect Sensor: A Feasibility Study.

Background: Current assessments of motor symptoms in Parkinson's disease are often limited to clinical rating scales. Objectives: To develop a computer application using the Microsoft Kinect sensor to assess performance-related bradykinesia. Methods: The developed application (Motorgame) was tested in patients with Parkinson's disease and healthy controls. Participants were assessed with the Movement Disorder Society Unified Parkinson's disease Rating Scale (MDS-UPDRS) and standardized clinical side effect rating scales, i.e., UKU Side Effect Rating Scale and Simpson-Angus Scale. Additionally, tests of information processing (Symbol Coding Task) and motor speed (Token Motor Task), together with a questionnaire, were applied. Results: Thirty patients with Parkinson's disease and 33 healthy controls were assessed. In the patient group, there was a statistically significant (p < 0.05) association between prolonged time of motor performance in the Motorgame and upper body rigidity and bradykinesia (MDS-UPDRS) with the strongest effects in the right hand (p < 0.001). In the entire group, prolonged time of motor performance was significantly associated with higher Simson-Angus scale rigidity score and higher UKU hypokinesia scores (p < 0.05). A shortened time of motor performance was significantly associated with higher scores on information processing (p < 0.05). Time of motor performance was not significantly associated with Token Motor Task, duration of illness, or hours of daily physical activity. The Motorgame was well-accepted. Conclusions: In the present feasibility study the Motorgame was able to detect common motor symptoms in Parkinson's disease in a statistically significant and clinically meaningful way, making it applicable for further testing in larger samples.

Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI.

OBJECTIVE: We demonstrate and evaluate the first markerless motion tracker compatible with PET, MRI, and simultaneous PET/MRI systems for motion correction (MC) of brain imaging. METHODS: PET and MRI compatibility is achieved by careful positioning of in-bore vision extenders and by placing all electronic components out-of-bore. The motion tracker is demonstrated in a clinical setup during a pediatric PET/MRI study including 94 pediatric patient scans. PET MC is presented for two of these scans using a customized version of the Multiple Acquisition Frame method. Prospective MC of MRI acquisition of two healthy subjects is demonstrated using a motion-aware MRI sequence. Real-time motion estimates are accompanied with a tracking validity parameter to improve tracking reliability. RESULTS: For both modalities, MC shows that motion induced artifacts are noticeably reduced and that motion estimates are sufficiently accurate to capture motion ranging from small respiratory motion to large intentional motion. In the PET/MRI study, a time-activity curve analysis shows image improvements for a patient performing head movements corresponding to a tumor motion of ±5-10 mm with a 19% maximal difference in standardized uptake value before and after MC. CONCLUSION: The first markerless motion tracker is successfully demonstrated for prospective MC in MRI and MC in PET with good tracking validity. SIGNIFICANCE: As simultaneous PET/MRI systems have become available for clinical use, an increasing demand for accurate motion tracking and MC in PET/MRI scans has emerged. The presented markerless motion tracker facilitate this demand.

Patient-specific estimation of detailed cochlear shape from clinical CT images.

PURPOSE: A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images. METHODS: From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape. RESULTS: We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores. CONCLUSIONS: The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.

A multiscale imaging and modelling dataset of the human inner ear.

Understanding the human inner ear anatomy and its internal structures is paramount to advance hearing implant technology. While the emergence of imaging devices allowed researchers to improve understanding of intracochlear structures, the difficulties to collect appropriate data has resulted in studies conducted with few samples. To assist the cochlear research community, a large collection of human temporal bone images is being made available. This data descriptor, therefore, describes a rich set of image volumes acquired using cone beam computed tomography and micro-CT modalities, accompanied by manual delineations of the cochlea and sub-compartments, a statistical shape model encoding its anatomical variability, and data for electrode insertion and electrical simulations. This data makes an important asset for future studies in need of high-resolution data and related statistical data objects of the cochlea used to leverage scientific hypotheses. It is of relevance to anatomists, audiologists, computer scientists in the different domains of image analysis, computer simulations, imaging formation, and for biomedical engineers designing new strategies for cochlear implantations, electrode design, and others.

A framework for geometry acquisition, 3-D printing, simulation, and measurement of head-related transfer functions with a focus on hearing-assistive devices.

Individual head-related transfer functions (HRTFs) are essential in applications like fitting hearing-assistive devices (HADs) for providing accurate sound localization performance. Individual HRTFs are usually obtained through intricate acoustic measurements. This paper investigates the use of a three-dimensional (3D) head model for acquisition of individual HRTFs. Two aspects were investigated; whether a 3D-printed model can replace measurements on a human listener and whether numerical simulations can replace acoustic measurements. For this purpose, HRTFs were acoustically measured for four human listeners and for a 3D printed head model of one of these listeners. Further, HRTFs were simulated by applying the finite element method to the 3D head model. The monaural spectral features and spectral distortions were very similar between re-measurements and between human and printed measurements, however larger deviations were observed between measurement and simulation. The binaural cues were in agreement among all HRTFs of the same listener, indicating that the 3D model is able to provide localization cues potentially accessible to HAD users. Hence, the pipeline of geometry acquisition, printing, and acoustic measurements or simulations, seems to be a promising step forward towards in-silico design of HADs.

Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation.

Recent developments in computational modeling of cochlear implantation are promising to study in silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from high-resolution anatomical µCT images. Then, by fitting the statistical model to a patient's CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model. This model can then be used to study in silico the effects of the electrical stimulation of the cochlear implant. Results are shown on a total of 25 models of patients. In all cases, a final mesh suitable for finite element simulations was obtained, in an average time of 94 s. The framework has proven to be fast and robust, and is promising for a detailed prognosis of the cochlear implantation surgery.