Fast cardiac T1ρ,adiab mapping using slice-selective adiabatic spin-lock preparation pulses.

PURPOSE: Myocardial T1ρ mapping techniques commonly acquire multiple images in one breathhold to calculate a single-slice T1ρ map. Recently, non-selective adiabatic pulses have been used for robust spin-lock preparation (T1ρ,adiab). The objective of this study was to develop a fast multi-slice myocardial T1ρ,adiab mapping approach. METHODS: The proposed-sequence reduces the number of breathholds required for whole-heart 2D T1ρ,adiab mapping by acquiring multiple interleaved slices in each breathhold using slice-selective T1ρ,adiab preparation pulses. The proposed-sequence was implemented with two interleaved slices per breathhold scan and was quantitatively evaluated in phantom experiments and 10 healthy-volunteers against a single-slice T1ρ,adiab mapping sequence. The sequence was demonstrated in two patients with myocardial scar. RESULTS: The phantom experiments showed the proposed-sequence had slice-to-slice variation of 1.62% ± 1.05% and precision of 4.51 ± 0.68 ms. The healthy volunteer cohort subject-wise mean relaxation time was lower for the proposed-sequence than the single-slice sequence (137.7 ± 5.3 ms vs. 148.4 ± 8.3 ms, p < 0.001), and spatial-standard-deviation was better (18.7 ± 1.8 ms vs. 21.8 ± 3.4 ms, p < 0.018). The mean within-subject, coefficient of variation was 5.93% ± 1.57% for the proposed-sequence and 6.31% ± 1.92% for the single-slice sequence (p = 0.35) and the effect of slice variation (0.81 ± 4.87 ms) was not significantly different to zero (p = 0.61). In both patient examples increased T1ρ,adiab (maximum American Heart Association-segment mean = 174 and 197 ms) was measured within the myocardial scar. CONCLUSION: The proposed sequence provides a twofold acceleration for myocardial T1ρ,adiab mapping using a multi-slice approach. It has no significant difference in within-subject variability, and significantly better precision, compared to a 2D T1ρ,adiab mapping sequence based on non-selective adiabatic spin-lock preparations.

Physics-guided self-supervised learning for retrospective T1 and T2 mapping from conventional weighted brain MRI: Technical developments and initial validation in glioblastoma.

PURPOSE: To develop a self-supervised learning method to retrospectively estimate T1 and T2 values from clinical weighted MRI. METHODS: A self-supervised learning approach was constructed to estimate T1, T2, and proton density maps from conventional T1- and T2-weighted images. MR physics models were employed to regenerate the weighted images from the network outputs, and the network was optimized based on loss calculated between the synthesized and input weighted images, alongside additional constraints based on prior information. The method was evaluated on healthy volunteer data, with conventional mapping as references. The reproducibility was examined on two 3.0T scanners. Performance in tumor characterization was inspected by applying the method to a public glioblastoma dataset. RESULTS: For T1 and T2 estimation from three weighted images (T1 MPRAGE, T1 gradient echo sequences, and T2 turbo spin echo), the deep learning method achieved global voxel-wise error ≤9% in brain parenchyma and regional error ≤12.2% in six types of brain tissues. The regional measurements obtained from two scanners showed mean differences ≤2.4% and correlation coefficients >0.98, demonstrating excellent reproducibility. In the 50 glioblastoma patients, the retrospective quantification results were in line with literature reports from prospective methods, and the T2 values were found to be higher in tumor regions, with sensitivity of 0.90 and specificity of 0.92 in a voxel-wise classification task between normal and abnormal regions. CONCLUSION: The self-supervised learning method is promising for retrospective T1 and T2 quantification from clinical MR images, with the potential to improve the availability of quantitative MRI and facilitate brain tumor characterization.

Free-breathing three-dimensional simultaneous myocardial T1 and T2 mapping based on multi-parametric SAturation-recovery and Variable-flip-Angle.

BACKGROUND: Quantitative myocardial tissue characterization with T1 and T2 parametric mapping can provide an accurate and complete assessment of tissue abnormalities across a broad range of cardiomyopathies. However, current clinical T1 and T2 mapping tools rely predominantly on two-dimensional (2D) breath-hold sequences. Clinical adoption of three-dimensional (3D) techniques is limited by long scan duration. The aim of this study is to develop and validate a time-efficient 3D free-breathing simultaneous T1 and T2 mapping sequence using multi-parametric SAturation-recovery and Variable-flip-Angle (mSAVA). METHODS: mSAVA acquires four volumes for simultaneous whole-heart T1 and T2 mapping. We validated mSAVA using simulations, phantoms, and in-vivo experiments at 3T in 11 healthy subjects and 11 patients with diverse cardiomyopathies. T1 and T2 values by mSAVA were compared with modified Look-Locker inversion recovery (MOLLI) and gradient and spin echo (GraSE), respectively. The clinical performance of mSAVA was evaluated against late gadolinium enhancement (LGE) imaging in patients. RESULTS: Phantom T1 and T2 by mSAVA showed a strong correlation to reference sequences (R2 = 0.98 and 0.99). In-vivo imaging with an imaging resolution of 1.5 × 1.5 × 8 mm3 could be achieved. Myocardial T1 and T2 of healthy subjects by mSAVA were 1310 ± 46 and 44.6 ± 2.0 ms, respectively, with T1 standard deviation higher than MOLLI (105 ± 12 vs 60 ± 16 ms) and T2 standard deviation lower than GraSE (4.5 ± 0.8 vs 5.5 ± 1.0 ms). mSAVA T1 and T2 maps presented consistent findings in patients undergoing LGE. Myocardial T1 and T2 of all patients by mSAVA were 1421 ± 79 and 47.2 ± 3.3 ms, respectively. CONCLUSION: mSAVA is a fast 3D technique promising for clinical whole-heart T1 and T2 mapping.

IMU Data-Driven and PCA-Based Approach to Establish Quantifiable and Practically Applicable Measures for V2 Technique Elements in Cross-Country Skiing.

Quantifying movement coordination in cross-country (XC) skiing, specifically the technique with its elemental forms, is challenging. Particularly, this applies when trying to establish a bidirectional transfer between scientific theory and practical experts' knowledge as expressed, for example, in ski instruction curricula. The objective of this study was to translate 14 curricula-informed distinct elements of the V2 ski-skating technique (horizontal and vertical posture, lateral tilt, head position, upper body rotation, arm swing, shoulder abduction, elbow flexion, hand and leg distance, plantar flexion, ski set-down, leg push-off, and gliding phase) into plausible, valid and applicable measures to make the technique training process more quantifiable and scientifically grounded. Inertial measurement unit (IMU) data of 10 highly experienced XC skiers who demonstrated the technique elements by two extreme forms each (e.g., anterior versus posterior positioning for the horizontal posture) were recorded. Element-specific principal component analyses (PCAs)-driven by the variance produced by the technique extremes-resulted in movement components that express quantifiable measures of the underlying technique elements. Ten measures were found to be sensitive in distinguishing between the inputted extreme variations using statistical parametric mapping (SPM), whereas for four elements the SPM did not detect differences (lateral tilt, plantar flexion, ski set-down, and leg push-off). Applicability of the established technique measures was determined based on quantifying individual techniques through them. The study introduces a novel approach to quantitatively assess V2 ski-skating technique, which might help to enhance technique feedback and bridge the communication gap that often exists between practitioners and scientists.

Inter-joint coordination with and without dopaminergic medication in Parkinson's disease: a case-control study.

BACKGROUND: How the joints exactly move and interact and how this reflects PD-related gait abnormalities and the response to dopaminergic treatment is poorly understood. A detailed understanding of these kinematics can inform clinical management and treatment decisions. The aim of the study was to investigate the influence of different gait speeds and medication on/off conditions on inter-joint coordination, as well as kinematic differences throughout the whole gait cycle in well characterized pwPD. METHODS: 29 controls and 29 PD patients during medication on, 8 of them also during medication off walked a straight walking path in slow, preferred and fast walking speeds. Gait data was collected using optical motion capture system. Kinematics of the hip and knee and coordinated hip-knee kinematics were evaluated using Statistical Parametric Mapping (SPM) and cyclograms (angle-angle plots). Values derived from cyclograms were compared using repeated-measures ANOVA for within group, and ttest for between group comparisons. RESULTS: PD gait differed from controls mainly by lower knee range of motion (ROM). Adaptation to gait speed in PD was mainly achieved by increasing hip ROM. Regularity of gait was worse in PD but only during preferred speed. The ratios of different speed cyclograms were smaller in the PD groups. SPM analyses revealed that PD participants had smaller hip and knee angles during the swing phase, and PD participants reached peak hip flexion later than controls. Withdrawal of medication showed an exacerbation of only a few parameters. CONCLUSIONS: Our findings demonstrate the potential of granular kinematic analyses, including > 1 joint, for disease and treatment monitoring in PD. Our approach can be extended to further mobility-limiting conditions and other joint combinations. TRIAL REGISTRATION: The study is registered in the German Clinical Trials Register (DRKS00022998, registered on 04 Sep 2020).

Performance and micro-pacing in sprint cross-country skiing: A comparison of individual time-trial and head-to-head race formats.

This study compared performance strategies and sub-technique selection in cross-country skate skiing sprint races, specifically individual time-trial (ITT) and head-to-head (H2H) formats. Fourteen male cross-country skiers from the Chinese national team participated in the FIS-sanctioned sprint race day. GNSS and heart rate sensors recorded positioning, skiing speeds, heart rate, sub-technique usage, and skiing kinematics. Statistical parametric mapping (SPM) was used to determine the course positions (clusters) where instantaneous skiing speed was significantly associated with section time. One-way analyses of variance were used to examine differences between the ITT and H2H. H2H race speeds were 2.4 ± 0.2% faster than the ITT race (p < 0.05).Variations in sub-technique and skiing kinematics were observed between race formats, indicating different strategies and tactics employed by athletes. SPM identified specific clusters (primarily uphill) where the fastest athlete gained significant time over the slowest. The greatest time gains were associated with higher G3 sub-technique usage and longer G3 cycle length on steep uphill terrain (9-13% gradients). Integrating SPM analyses and sub-technique assessments can help optimise performance and tactics in sprint races. This study enhances our understanding of cross-country skiing dynamics and performance variations among elite competitors.

Neuromuscular fatigue and cognitive constraints independently modify lower extremity landing biomechanics in healthy and chronic ankle instability individuals.

The purpose was to determine the impact of both cognitive constraint and neuromuscular fatigue on landing biomechanics in healthy and chronic ankle instability (CAI) participants. Twenty-three male volunteers (13 Control and 10 CAI) performed a single-leg landing task before and immediately after a fatiguing exercise with and without cognitive constraints. Ground Reaction Force (GRF) and Time to Stabilization (TTS) were determined at landing in vertical, anteroposterior (ap) and mediolateral (ml) axes using a force plate. Three-dimensional movements of the hip, knee and ankle were recorded during landing using a motion capture system. Exercise-induced fatigue decreased ankle plantar flexion and inversion and increased knee flexion. Neuromuscular fatigue decreased vertical GRF and increased ml GRF and ap TTS. Cognitive constraint decreased ankle internal rotation and increased knee and hip flexion during the flight phase of landing. Cognitive constraint increased ml GRF and TTS in all three axes. No interaction between factors (group, fatigue, cognitive) were observed. Fatigue and cognitive constraint induced greater knee and hip flexion, revealing higher proximal control during landing. Ankle kinematic suggests a protective strategy in response to fatigue and cognitive constraints. Finally, these two constraints impair dynamic stability that could increase the risk of ankle sprain.

A Novel Workflow for Electrophysiology Studies in Patients with Brugada Syndrome.

Brugada Syndrome (BrS) is a primary electrical epicardial disease characterized by ST-segment elevation followed by a negative T-wave in the right precordial leads on the surface electrocardiogram (ECG), also known as the 'type 1' ECG pattern. The risk stratification of asymptomatic individuals with spontaneous type 1 ECG pattern remains challenging. Clinical and electrocardiographic prognostic markers are known. As none of these predictors alone is highly reliable in terms of arrhythmic prognosis, several multi-factor risk scores have been proposed for this purpose. This article presents a new workflow for processing endocardial signals acquired with high-density RV electro-anatomical mapping (HDEAM) from BrS patients. The workflow, which relies solely on Matlab software, calculates various electrical parameters and creates multi-parametric maps of the right ventricle. The workflow, but it has already been employed in several research studies involving patients carried out by our group, showing its potential positive impact in clinical studies. Here, we will provide a technical description of its functionalities, along with the results obtained on a BrS patient who underwent an endocardial HDEAM.

Were Frailty Identification Criteria Created Equal? A Comparative Case Study on Continuous Non-Invasively Collected Neurocardiovascular Signals during an Active Standing Test in the Irish Longitudinal Study on Ageing (TILDA).

BACKGROUND: In this observational study, we compared continuous physiological signals during an active standing test in adults aged 50 years and over, characterised as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA). METHODS: This study utilised data from TILDA, an ongoing landmark prospective cohort study of community-dwelling adults aged 50 years or older in Ireland. The initial sampling strategy in TILDA was based on random geodirectory sampling. Four independent groups were identified: those characterised as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterised as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active standing test, including systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, the frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualised across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM). RESULTS: A total of 1124 participants (mean age of 63.5 years; 50.2% women) were included: 23 were characterised as frail only by the FP, 97 by the FI, 38 by the CFS, and 966 by none of these criteria. The SPM analyses revealed that only the group characterised as frail by the FI had significantly different signals (p < 0.001) compared to the non-frail group. Specifically, they exhibited an attenuated gain in HR between 10 and 15 s post-stand and larger deficits in sBP and dBP between 15 and 20 s post-stand. CONCLUSIONS: The FI proved to be more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition. Significant differences were observed in the dynamics of cardiovascular signals among the frail populations identified by different frailty criteria, suggesting that caution should be taken when employing frailty identification tools on physiological signals, particularly the neurocardiovascular signals in an active standing test.

CMR Findings in COVID-19 Recovered Patients: A Review on Parametric Mapping, Feature-Tracking, and LGE.

On March 11, 2020, the World Health Organization raised the coronavirus disease 2019 (COVID-19) status to a pandemic level. The disease caused a global outbreak with devastating consequences, and a fair percentage of patients who have recovered from it continue experiencing persistent sequelae. Hence, identifying the medium and long-term effects of the COVID-19 disease is crucial for its future management. In particular, cardiac complications, from affected function to myocardial injuries, have been reported in these patients. Considering that cardiovascular magnetic resonance (CMR) imaging is the gold standard in diagnosing myocardial involvement and has more advantages than other medical imaging modalities, assessing the outcomes of patients who recovered from COVID-19 with CMR could prove beneficial. This review compiles common findings in CMR in patients from the general population who recovered from COVID-19. The CMR-based techniques comprised parametric mapping for analyzing myocardial composition, feature tracking for studying regional heart deformation, and late gadolinium enhancement for detecting compromised areas in the cardiac muscle. A total of 19 studies were included. The evidence suggests that it is more likely to find signs of myocardial injury in patients who recovered from COVID-19 than in healthy controls, including changes in T1 and T2 mapping relaxation times, affected strain, or the presence of late gadolinium enhancement (LGE) lesions. However, more than two years after the outbreak, there is still a lack of consensus about how these parameters may indicate cardiac involvement in patients who recovered from the disease, as limited and contradictory data is available.

Response of Knee Joint Biomechanics to Landing Under Internal and External Focus of Attention in Female Volleyball Players.

The aim of this study was to examine the effect of attentional focus instructions on the biomechanical variables associated with the risk of anterior cruciate ligament injury of the knee joint during a drop landing task using a time series analysis. Ten female volleyball players (age: 20.4 ± 0.8 years, height: 169.7 ± 7.1 cm, mass: 57.6 ± 3.1 kg, experience: 6.3 ± 0.8 years) performed landings from a 50 cm height under three different attentional focus conditions: (1) external focus (focus on landing as soft as possible), (2) internal focus (focus on bending your knees when you land), and (3) control (no-focus instruction). Statistical parameter mapping in the sagittal plane during the crucial first 30% of landing time showed a significant effect of attentional focus instructions. Despite the similarity in landing performance across foci instructions, adopting an external focus instruction promoted reduced vertical ground reaction force and lower sagittal flexion moment during the first 30% of execution time compared to internal focus, suggesting reduced knee loading. Therefore, adopting an external focus of attention was suggested to reduce most biomechanical risk variables in the sagittal plane associated with anterior cruciate ligament injuries, compared to internal focus and control condition. No significant differences were found in the frontal and horizontal planes between the conditions during this crucial interval.

SwinUNet: a multiscale feature learning approach to cardiovascular magnetic resonance parametric mapping for myocardial tissue characterization.

Objective.Cardiovascular magnetic resonance (CMR) can measure T1 and T2 relaxation times for myocardial tissue characterization. However, the CMR procedure for T1/T2 parametric mapping is time-consuming, making it challenging to scan heart patients routinely in clinical practice. This study aims to accelerate CMR parametric mapping with deep learning.Approach. A deep-learning model, SwinUNet, was developed to accelerate T1/T2 mapping. SwinUNet used a convolutional UNet and a Swin transformer to form a hierarchical 3D computation structure, allowing for analyzing CMR images spatially and temporally with multiscale feature learning. A comparative study was conducted between SwinUNet and an existing deep-learning model, MyoMapNet, which only used temporal analysis for parametric mapping. The T1/T2 mapping performance was evaluated globally using mean absolute error (MAE) and structural similarity index measure (SSIM). The clinical T1/T2 indices for characterizing the left-ventricle myocardial walls were also calculated and evaluated using correlation and Bland-Altman analysis.Main results. We performed accelerated T1 mapping with ≤4 heartbeats and T2 mapping with 2 heartbeats in reference to the clinical standard, which required 11 heartbeats for T1 mapping and 3 heartbeats for T2 mapping. SwinUNet performed well in all the experiments (MAE < 50 ms, SSIM > 0.8, correlation > 0.75, and Bland-Altman agreement limits < 100 ms for T1 mapping; MAE < 1 ms, SSIM > 0.9, correlation > 0.95, and Bland-Altman agreement limits < 1.5 ms for T2 mapping). When the maximal acceleration was used (2 heartbeats), SwinUNet outperformed MyoMapNet and gave measurement accuracy similar to the clinical standard.Significance. SwinUNet offers an optimal solution to CMR parametric mapping for assessing myocardial diseases quantitatively in clinical cardiology.

Interlimb kinetic asymmetries during the tuck jump assessment are more exposed following kinetic stabilization.

OBJECTIVE: To analyse interlimb kinetics and asymmetries during the tuck jump assessment (TJA), before and after kinetic stabilization, to identify injury risk in healthy female athletes. DESIGN: Cross-sectional study. SETTING: Laboratory. PARTICIPANTS: Twenty-five healthy females (age 21.0 ± 1.83 yrs; height 1.68 ± 0.06 m; body mass 69.4 ± 10.7 kg). MAIN OUTCOME MEASURES: Kinetics were measured during 10-s trials of the TJA and absolute asymmetries compared, before and after kinetic stabilization using paired sample t-tests. Statistical parametric mapping (SPM) compared vertical ground reaction force (VGRF) data for each limb during the jumping cycles before and after stabilization. RESULTS: Small to moderate increases in interlimb asymmetries were observed after stabilization for VGRF, relative vertical leg stiffness, average loading rate, total and propulsive impulse, peak braking and propulsive force (p < 0.05). SPM revealed significant interlimb differences between 77-98% and 83-99% of ground contact for the jumping cycles pre- and post-stabilization respectively. CONCLUSIONS: Larger asymmetries were evident after kinetic stabilization, with increased VGRF in the non-dominant limb. We speculate that participants sacrificed interlimb landing symmetry to achieve kinetic stability, which may reflect a primal landing strategy that forgoes movement quality. Assessing lower limb biomechanics using the TJA should involve examining kinetic stability and interlimb kinetic asymmetries.

Differences between lower extremity joint running kinetics captured by marker-based and markerless systems were speed dependent.

BACKGROUND: The development of computer vision technology has enabled the use of markerless movement tracking for biomechanical analysis. Recent research has reported the feasibility of markerless systems in motion analysis but has yet to fully explore their utility for capturing faster movements, such as running. Applied studies using markerless systems in clinical and sports settings are still lacking. Thus, the present study compared running biomechanics estimated by marker-based and markerless systems. Given running speed not only affects sports performance but is also associated with clinical injury prevention, diagnosis, and rehabilitation, we aimed to investigate the effects of speed on the comparison of estimated lower extremity joint moments and powers between markerless and marker-based technologies during treadmill running as a concurrent validating study. METHODS: Kinematic data from marker-based/markerless technologies were collected, along with ground reaction force data, from 16 young adults running on an instrumented treadmill at 3 speeds: 2.24 m/s, 2.91 m/s, and 3.58 m/s (5.0 miles/h, 6.5 miles/h, and 8.0 miles/h). Sagittal plane moments and powers of the hip, knee, and ankle were calculated by inverse dynamic methods. Time series analysis and statistical parametric mapping were used to determine system differences. RESULTS: Compared to the marker-based system, the markerless system estimated increased lower extremity joint kinetics with faster speed during the swing phase in most cases. CONCLUSION: Despite the promising application of markerless technology in clinical settings, systematic markerless overestimation requires focused attention. Based on segment pose estimations, the centers of mass estimated by markerless technologies were farther away from the relevant distal joint centers, which led to greater joint moments and powers estimates by markerless vs. marker-based systems. The differences were amplified by running speed.

Effects of a Total Motion Release (TMR®) Protocol for the Single Leg Squat on Asymmetrical Movement Patterns.

BACKGROUND: Improving single leg squat (SLS) movement symmetry may benefit rehabilitation protocols. The Total Motion Release® (TMR®) protocol has been theorized to evaluate and improve patient-perceived movement asymmetries. HYPOTHESIS/PURPOSE: The purpose of this study was to evaluate whether perceived asymmetries identified by a TMR® scoring protocol were related to biomechanical asymmetries and whether improving perceived asymmetries influenced movement mechanics. It was hypothesized that participants with perceived asymmetries would also present with biomechanical asymmetries. A secondary hypothesis was that participants would reduce their perceived asymmetries after performing the TMR® protocol and subsequently have greater biomechanical symmetry. STUDY DESIGN: Descriptive Cohort (Laboratory Study). METHODS: Twenty participants (10 female, 10 male) with self-identified bilateral differences of 10 points or greater on the TMR® scoring scale were recruited for the study. The non-preferred side was defined as the side that scored higher. 3Dimensional motion capture was used to bilaterally assess baseline SLS depth as well as hip, knee, and ankle kinematics and kinetics. For the TMR® protocol, sets of 10 SLSs were performed on the preferred leg until their perceived asymmetries were resolved (i.e., both sides scored equally), or four sets had been completed. Kinematics and kinetics were collected immediately after the intervention and after a 10-minute rest period. RESULTS: Participants had biomechanical asymmetries at baseline for knee flexion, ankle flexion, and knee moments. Following the intervention, participants had reduced TMR® scores on the non-preferred leg, and this coincided with increased knee joint moments on that side. Although perceived asymmetries were resolved after the intervention, kinematic and kinetic asymmetries at the knee and ankle were still present. CONCLUSIONS: A TMR® intervention could benefit rehabilitation protocols by reducing factors of dysfunction and increasing the ability of patients to load the non-preferred knee. Further investigations are necessary to elucidate the importance of asymmetrical movement patterns. LEVEL OF EVIDENCE: 3b.

Additional value of CMR parametric mapping in tissue characterization of common benign pediatric cardiac tumors.

BACKGROUND: Cardiac Magnetic Resonance (CMR) parametric mapping is underexplored in cardiac tumors. OBJECTIVES: To evaluate the contribution of mapping sequences on the characterization of pediatric tumors. METHODS: All pediatric patients referred for cardiac tumors at Bambino Gesù Children's Hospital from June 2017 to November 2023, who underwent CMR with mapping sequences, were included. The diagnosis of tumor type was performed according to signal characteristics on different sequences. Mass parametric mapping for each subtype and interobserver variability was assessed. RESULTS: Sixteen patients were enrolled. The mean age at CMR was 7 ± 5 years. "Traditional" mass-type assessment diagnosed hemangioma (Group A) in 3 patients (19%), fibroma (Group B) in 4 patients (25%), rhabdomyoma (Group C) in 6 patients (37%), and lipoma (Group D) in 3 patients (19%). The ANOVA analysis revealed significant differences in mass native T1 and mass extracellular volume (ECV) values among the four subgroups (p<0.001 for both comparisons). The mean native T1 and ECV values were respectively 1465 ± 158 msec and 54 ± 4% for Group A, 860 ± 118 msec and 93 ± 4% for Group B, 1007 ± 57 msec and 23 ± 5% for Group C, and 215 ± 13 msec and 0 ± 0% for Group D. CONCLUSIONS: Mass mapping analysis is feasible and reproducible in children. ECV values provide the most accurate differentiation. Mass ECV consistently resembles normal myocardium in rhabdomyoma, is extremely high (approaching 100%) in fibroma, equals to zero in lipoma, and matches blood pool ECV (1-Hct) in hemangioma.

Automated extraction of the arterial input function from brain images for parametric PET studies.

BACKGROUND: Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. RESULTS: Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07. CONCLUSIONS: Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.

Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence.

Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due to diverse challenges. Effective methods for augmenting data in developing ML models, specifically in the human posture domain, are scarce. Therefore, this study explored the feasibility of leveraging generative artificial intelligence (AI) to produce realistic synthetic posture data by utilizing three-dimensional posture data. Methods: Data were collected from 338 subjects through surface topography. A Variational Autoencoder (VAE) architecture was employed to generate and evaluate synthetic posture data, examining its distinguishability from real data by domain experts, ML classifiers, and Statistical Parametric Mapping (SPM). The benefits of incorporating augmented posture data into the learning process were exemplified by a deep autoencoder (AE) for automated feature representation. Results: Our findings highlight the challenge of differentiating synthetic data from real data for both experts and ML classifiers, underscoring the quality of synthetic data. This observation was also confirmed by SPM. By integrating synthetic data into AE training, the reconstruction error can be reduced compared to using only real data samples. Moreover, this study demonstrates the potential for reduced latent dimensions, while maintaining a reconstruction accuracy comparable to AEs trained exclusively on real data samples. Conclusion: This study emphasizes the prospects of harnessing generative AI to enhance ML tasks in the biomechanics domain.

The impact of pediatric obesity on biomechanical differences across the gait cycle at three walking speeds.

BACKGROUND: Obesity impacts a child's ability to walk with resulting biomechanical adaptations; however, existing research has not comprehensively compared differences across the gait cycle. We examined differences in lower extremity biomechanics across the gait cycle between children with and without obesity at three walking speeds. METHODS: Full gait cycles of age-matched children with obesity (N = 10; BMI: 25.7 ± 4.2 kg/m2) and without obesity (N = 10; BMI: 17.0 ± 1.9 kg/m2) were analyzed at slow, normal, and fast walking speeds. Main and interaction effects of group and speed across hip, knee, and ankle joint angles and moments in sagittal, frontal, and transverse planes were analyzed using one-dimensional statistical parametric mapping. FINDINGS: Compared to children without obesity, children with obesity had greater hip adduction during mid-stance, while also producing greater hip extensor moments during early stance phase, abductor moments throughout most of stance, and hip external rotator moments during late stance. Children with obesity recorded greater knee flexor, knee extensor and knee internal rotator moments during early stance, and knee external rotator moments in late stance than children without obesity; children with obesity also demonstrated greater ankle plantarflexor moments throughout mid and late stance. Interaction effects existed within joint kinetics data; children with obesity produced greater hip extensor moments at initial contact and toe-off when walking at fast compared to normal walking speed. INTERPRETATION: While few kinematic differences existed between the two groups, children with obesity exhibited greater moments at the hip, knee, and ankle during critical periods of controlling and stabilizing mass.

Trial- vs. cycle-level detrending in the analysis of cyclical biomechanical data.

Biomechanical time series may contain low-frequency trends due to factors like electromechanical drift, attentional drift and fatigue. Existing detrending procedures are predominantly conducted at the trial level, removing trends that exist over finite, adjacent time windows, but this fails to consider what we term 'cycle-level trends': trends that occur in cyclical movements like gait and that vary across the movement cycle, for example: positive and negative drifts in early and late gait phases, respectively. The purposes of this study were to describe cycle-level detrending and to investigate the frequencies with which cycle-level trends (i) exist, and (ii) statistically affect results. Anterioposterior ground reaction forces (GRF) from the 41-subject, 8-speed, open treadmill walking dataset of Fukuchi (2018) were analyzed. Of a total of 552 analyzed trials, significant cycle-level trends were found approximately three times more frequently (21.1%) than significant trial-level trends (7.4%). In statistical comparisons of adjacent walking speeds (i.e., speed 1 vs. 2, 2 vs. 3, etc.) just 3.3% of trials exhibited cycle-level trends that changed the null hypothesis rejection decision. However 17.6% of trials exhibited cycle-level trends that qualitatively changed the stance phase regions identified as significant. Although these results are preliminary and derived from just one dataset, results suggest that cycle-level trends can contribute to analysis bias, and therefore that cycle-level trends should be considered and/or removed where possible. Software implementing the proposed cycle-level detrending is available at https://github.com/0todd0000/detrend1d.