Methodological considerations in assessing countermovement jumps with handheld accentuated eccentric loading.

This study aimed to compare the agreement between three-dimensional motion capture and vertical ground reaction force (vGRF) in identifying the point of dumbbell (DB) release during a countermovement jump with accentuated eccentric loading (CMJAEL), and to examine the influence of the vGRF analysis method on the reliability and magnitude of CMJAEL variables. Twenty participants (10 male, 10 female) completed five maximal effort CMJAEL at 20% and 30% of body mass (CMJAEL20 and CMJAEL30, respectively) using DBs. There was large variability between methods in both loading conditions, as indicated by the wide limits of agreement (CMJAEL20 = -0.22 to 0.07 s; CMJAEL30 = -0.29 to 0.14 s). Variables were calculated from the vGRF data, and compared between four methods (forward integration (FI), backward integration (BI), FI adjusted at bottom position (BP), FI adjusted at DB release point (DR)). Greater absolute reliability was observed for variables from DR (CV% ≤ 7.28) compared to BP (CV% ≤ 13.74), although relative reliability was superior following the BP method (ICC ≥ 0.781 vs ≥ 0.606, respectively). The vGRF method shows promise in pinpointing the DB release point when only force platforms are accessible, and a combination of FI and BI analyses is advised to understand CMJAEL dynamics.

[A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].

OBJECTIVE: We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning. METHODS: A blur encoder was used to extract motion-related degradation features to model the degradation process caused by motion, and the obtained motion degradation features were imported in the artifact correction module for artifact removal. The artifact correction module adopts a joint learning framework for image blur removal and image blur simulation for treatment of spatially varying and random motion patterns. Comparative experiments were conducted to verify the effectiveness of the proposed method using both simulated motion data sets and clinical data sets. RESULTS: The experimental results with the simulated dataset showed that compared with the existing methods, the PSNR of the proposed method increased by 2.88%, the SSIM increased by 0.89%, and the RMSE decreased by 10.58%. The results with the clinical dataset showed that the proposed method achieved the highest expert level with a subjective image quality score of 4.417 (in a 5-point scale), significantly higher than those of the comparison methods. CONCLUSION: The proposed DMBL algorithm with a deep blur joint learning network structure can effectively reduce motion artifacts in dental CBCT images and achieve high-quality image restoration.

Liner orientation change of dual mobility cup determined via 3D ultrasound imaging and motion analysis: A cadaver study.

BACKGROUND: A mobile polyethylene liner enables the dual mobility cup (DMC) to contribute to restoring hip joint range-of-motion, decreasing wear and increasing implant stability. However, more data is required on how liner orientation changes with hip joint movement. As a first step towards better understanding liner orientation change in vivo, this cadaver study focuses on quantifying DMC liner orientation change after different hip passive movements, using ultrasound imaging and motion analysis. HYPOTHESIS: The liner does not always go back to its initial orientation and its final orientation depends mainly on hip movement amplitude. METHODS: 3D ultrasound imaging and motion analysis were used to define liner and hip movements for four fresh post-mortem human subjects with six implanted DMC. Abduction and anteversion angles of the liner plane relative to the pelvis were measured before and after hip flexion, internal rotation, external rotation, abduction, adduction. RESULTS: Liner orientation changes were generally defined by angle variation smaller than 5°, with the liner nearly going back to its initial orientation. However, hip flexion caused liner abduction and anteversion angle variations greater than 15°. Except for hip adduction, only weak or no correlation was found between the final angle of the liner and the maximal hip joint movement amplitude. DISCUSSION: This study is the first attempt to quantify liner orientation change for implanted DMC via ultrasound imaging and constitutes a step forward in the understanding of liner orientation change and its relationship with hip joint movement. The hypothesis that the final liner abduction and anteversion angles depend mainly on hip movement amplitude was not confirmed, even if hip flexion was the movement generating the most liner orientation changes over 15°. This approach should be extended to in vivo clinical investigations, as measured liner angle variation could provide important support for the wear and stability claims made for DMC. LEVEL OF EVIDENCE: IV; cadaveric study.

Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.

BACKGROUND: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) - a locally adaptive image filter - allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. METHODS: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the "optimal" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. RESULTS: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (-3.9 ± 5.2)% and δ SUV max BF , STD = (-4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. CONCLUSIONS: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Development of full-body rhythmic synchronization in middle childhood.

Rhythmic entrainment is a fundamental aspect of musical behavior, but the skills required to accurately synchronize movement to the beat seem to develop over many years. Motion capture studies of corporeal synchronization have shown immature abilities to lock in to the beat in children before age 5, and reliable synchronization ability in adults without musical training; yet there is a lack of data on full-body synchronization skills between early childhood and adulthood. To document typical rhythmic synchronization during middle childhood, we used a wireless motion capture device to measure period- and phase-locking of full body movement to rhythm and metronome stimuli in 6 to 11 year-old children in comparison with adult data. Results show a gradual improvement with age; however children's performance did not reach adult levels by age 12, suggesting that these skills continue to develop during adolescence. Our results suggest that in the absence of specific music training, full-body rhythmic entrainment skills improve gradually during middle childhood, and provide metrics for examining the continued maturation of these skills during adolescence.

Radiological and Clinical Significance of Cervical Dynamic Magnetic Resonance Imaging for Cervical Spondylotic Myelopathy.

OBJECTIVE: The study compared the morphometric changes of the cervical spinal cord using dynamic magnetic resonance imaging (MRI) in patients with cervical spondylotic myelopathy (CSM) and assessed the correlation with kinematic changes, cord cross-sectional area (CSA), and high signal intensity (SI) on T2-weighted imaging (T2WI). METHODS: Patients with CSM were evaluated through dynamic MRI for sagittal and axial CSA changes of the cervical cord, cerebrospinal fluid (CSF) reserve ratio, degree of cord impingement, cord compression rate, range of motion (ROM), and severity of SI on T2WI. The degree of cord impingement was evaluated using the Muhle grading system. Clinical outcomes were assessed using Japanese Orthopaedic Association scoring and Nurick grade. RESULTS: The study included 191 patients (113 males) with a mean age of 55.34 ± 12.09 years. The lowest sagittal CSF reserve ratio and cord occupation rate were observed during extension. Cord impingement and SI change were more prevalent in extension-positioned MRI. There was no difference between ROM on dynamic radiographs and dynamic MRI. Preoperative cervical ROM was greater in patients with intensely high SI change. CONCLUSION: Dynamic MRI is useful for evaluating neck movement. Patients with high SI had greater ROM before surgery but worse outcomes after. Neck extension exacerbated cervical stenosis and cord compression compared to flexion, and cervical spinal motion contributed to the severity of CSM. Cervical spinal motion should be carefully evaluated, particularly in hyperextension, to prevent worsening of CSM.

Promoting fairness in activity recognition algorithms for patient's monitoring and evaluation systems in healthcare.

Researchers face the challenge of defining subject selection criteria when training algorithms for human activity recognition tasks. The ongoing uncertainty revolves around which characteristics should be considered to ensure algorithmic robustness across diverse populations. This study aims to address this challenge by conducting an analysis of heterogeneity in the training data to assess the impact of physical characteristics and soft-biometric attributes on activity recognition performance. The performance of various state-of-the-art deep neural network architectures (tCNN, hybrid-LSTM, Transformer model) processing time-series data using the IntelliRehab (IRDS) dataset was evaluated. By intentionally introducing bias into the training data based on human characteristics, the objective is to identify the characteristics that influence algorithms in motion analysis. Experimental findings reveal that the CNN-LSTM model achieved the highest accuracy, reaching 88%. Moreover, models trained on heterogeneous distributions of disability attributes exhibited notably higher accuracy, reaching 51%, compared to those not considering such factors, which scored an average of 33%. These evaluations underscore the significant influence of subjects' characteristics on activity recognition performance, providing valuable insights into the algorithm's robustness across diverse populations. This study represents a significant step forward in promoting fairness and trustworthiness in artificial intelligence by quantifying representation bias in multi-channel time-series activity recognition data within the healthcare domain.

Curved Surfaces Induce Metachronal Motion of Microscopic Magnetic Cilia.

Cilia are hair-like organelles present on cell surfaces. They often exhibit a collective wave-like motion that can enhance fluid or particle transportation function, known as metachronal motion. Inspired by nature, researchers have developed artificial cilia capable of inducing metachronal motion, especially magnetic actuation. However, current methods remain intricate, requiring either control of the magnetic or geometrical properties of individual cilia or the generation of a complex magnetic field. In this paper, we present a novel elegant method that eliminates these complexities and induces metachronal motion of arrays of identical microscopic magnetic artificial cilia by applying a simple rotating uniform magnetic field. The key idea of our method is to place arrays of cilia on surfaces with a specially designed curvature. This results in consecutive cilia experiencing different magnetic field directions at each point in time, inducing a phase lag in their motion, thereby causing collective wave-like motion. Moreover, by tuning the surface curvature profile, we can achieve diverse metachronal patterns analogous to symplectic and antiplectic metachronal motion observed in nature, and we can even devise novel combinations thereof. Furthermore, we characterize the local flow patterns generated by the motion of the cilia, revealing the formation of vortical patterns. Our novel approach simplifies the realization of miniaturized metachronal motion in microfluidic systems and opens the possibility of controlling flow pattern generation and transportation, opening avenues for applications such as lab-on-a-chip technologies, organ-on-a-chip platforms, and microscopic object propulsion.

Optimizing coronary CT angiography quality with motion-compensated reconstruction for second-generation dual-layer spectral detector CT.

OBJECTIVES: To investigate the effect of motion-compensated reconstruction (MCR) algorithm on improving the image quality of coronary computed tomography angiography (CCTA) using second-generation dual-layer spectral detector computed tomography (DLCT), and to evaluate the influence of heart rate (HR) on the motion-correction efficacy of this algorithm. MATERIALS AND METHODS: We retrospectively enrolled 127 patients who underwent CCTA for suspected coronary artery disease using second-generation DLCT. We divided the patients into two subgroups according to their average HR during scanning: the "HR < 75 bpm" group and the "HR ≥ 75 bpm" group. All images were reconstructed by the standard (STD) algorithm and MCR algorithm. Subjective image quality (4-point Likert scale), interpretability, and objective image quality between the STD and MCR in the whole population and within each subgroup were compared. RESULTS: MCR showed significantly higher Likert scores and interpretability than STD on the per-segment (3.58 ± 0.69 vs. 2.82 ± 0.93, 98.4% vs. 91.9%), per-vessel (3.12 ± 0.81 vs. 2.12 ± 0.74, 96.3% vs. 78.7%) and per-patient (2.57 ± 0.76 vs. 1.62 ± 0.55, 90.6% vs. 59.1%) levels (all p < 0.001). In the analysis of HR subgroups on a per-vessel basis of interpretability, significant differences were observed only in the right coronary artery in the low HR group, whereas significant differences were noted in three major coronary arteries in the high HR group. For objective image quality assessment, MCR significantly improved the SNR (13.22 ± 4.06 vs. 12.72 ± 4.06) and the contrast-to-noise ratio (15.84 ± 4.82 vs. 15.39 ± 4.38) compared to STD (both p < 0.001). CONCLUSION: MCR significantly improves the subjective image quality, interpretability, and objective image quality of CCTA, especially in patients with higher HRs. CLINICAL RELEVANCE STATEMENT: The motion-compensated reconstruction algorithm of the second-generation dual-layer spectral detector computed tomography is helpful in improving the image quality of coronary computed tomography angiography in clinical practice, especially in patients with higher heart rates. KEY POINTS: Motion artifacts from cardiac movement affect the quality and interpretability of coronary computed tomography angiography (CCTA). This motion-compensated reconstruction (MCR) algorithm significantly improves the image quality of CCTA in clinical practice. Image quality improvement by using MCR was more significant in the high heart rate group.

Inhibitory control of gait initiation in humans: An electroencephalography study.

Response inhibition is a crucial component of executive control. Although mainly studied in upper limb tasks, it is fully implicated in gait initiation. Here, we assessed the influence of proactive and reactive inhibitory control during gait initiation in healthy adult participants. For this purpose, we measured kinematics and electroencephalography (EEG) activity (event-related potential [ERP] and time-frequency data) during a modified Go/NoGo gait initiation task in 23 healthy adults. The task comprised Go-certain, Go-uncertain, and NoGo conditions. Each trial included preparatory and imperative stimuli. Our results showed that go-uncertainty resulted in delayed reaction time, without any difference for the other parameters of gait initiation. Proactive inhibition, that is, Go uncertain versus Go certain conditions, influenced EEG activity as soon as the preparatory stimulus. Moreover, both proactive and reactive inhibition influenced the amplitude of the ERPs (central P1, occipito-parietal N1, and N2/P3) and theta and alpha/low beta band activities in response to the imperative-Go-uncertain versus Go-certain and NoGo versus Go-uncertain-stimuli. These findings demonstrate that the uncertainty context; induced proactive inhibition, as reflected in delayed gait initiation. Proactive and reactive inhibition elicited extended and overlapping modulations of ERP and time-frequency activities. This study shows the protracted influence of inhibitory control in gait initiation.

Potential thresholds of critically increased cardiac-related spinal cord motion in degenerative cervical myelopathy.

Introduction: New diagnostic techniques are a substantial research focus in degenerative cervical myelopathy (DCM). This cross-sectional study determined the significance of cardiac-related spinal cord motion and the extent of spinal stenosis as indicators of mechanical strain on the cord. Methods: Eighty-four DCM patients underwent MRI/clinical assessments and were classified as MRI+ [T2-weighted (T2w) hyperintense lesion in MRI] or MRI- (no T2w-hyperintense lesion). Cord motion (displacement assessed by phase-contrast MRI) and spinal stenosis [adapted spinal canal occupation ratio (aSCOR)] were related to neurological (sensory/motor) and neurophysiological readouts [contact heat evoked potentials (CHEPs)] by receiver operating characteristic (ROC) analysis. Results: MRI+ patients (N = 31; 36.9%) were more impaired compared to MRI- patients (N = 53; 63.1%) based on the modified Japanese Orthopedic Association (mJOA) subscores for upper {MRI+ [median (Interquartile range)]: 4 (4-5); MRI-: 5 (5-5); p < 0.01} and lower extremity [MRI+: 6 (6-7); MRI-: 7 (6-7); p = 0.03] motor dysfunction and the monofilament score [MRI+: 21 (18-23); MRI-: 24 (22-24); p < 0.01]. Both patient groups showed similar extent of cord motion and stenosis. Only in the MRI- group displacement identified patients with pathologic assessments [trunk/lower extremity pin prick score (T/LEPP): AUC = 0.67, p = 0.03; CHEPs: AUC = 0.73, p = 0.01]. Cord motion thresholds: T/LEPP: 1.67 mm (sensitivity 84.6%, specificity 52.5%); CHEPs: 1.96 mm (sensitivity 83.3%, specificity 65.6%). The aSCOR failed to show any relation to the clinical assessments. Discussion: These findings affirm cord motion measurements as a promising additional biomarker to improve the clinical workup and to enable timely surgical treatment particularly in MRI- DCM patients. Clinical trial registration: www.clinicaltrials.gov, NCT02170155.

Protocol to record and analyze primate leaping in three-dimensional in the wild.

Several studies comparing primate locomotion under lab versus field conditions have shown the importance of implementing both types of studies, as each has their advantages and disadvantages. However, three-dimensional (3D) motion capture of primates has been challenging under natural conditions. In this study, we provide a detailed protocol on how to collect 3D biomechanical data on primate leaping in their natural habitat that can be widely implemented. To record primate locomotion in the dense forest we use modified GoPro Hero Black cameras with zoom lenses that can easily be carried around and set up on tripods. We outline details on how to obtain camera calibrations at greater heights and how to process the collected data using the MATLAB camera calibration app and the motion tracking software DLTdv8a. We further developed a new MATLAB application "WildLeap3D" to generate biomechanical performance metrics from the derived x, y, z coordinates of the leaps. We provide details on how to collect data on support diameter, compliance, and orientation, and combine these with the jumps to study locomotor performance in an ecological context. We successfully reconstructed leaps of wild primates in the 3D space under natural conditions and provided data on four representative leaps. We provide exemplar data on primate velocity and acceleration during a leap and show how our protocol can be used to analyze segmental kinematics. This study will help to make motion capture of freely moving animals more accessible and help further our knowledge about animal locomotion and movement.

A Convoy of Magnetic Millirobots Transports Endoscopic Instruments for Minimally-Invasive Surgery.

Small-scale robots offer significant potential in minimally invasive medical procedures. Due to the nature of soft biological tissues, however, robots are exposed to complex environments with various challenges in locomotion, which is essential to overcome for useful medical tasks. A single mini-robot often provides insufficient force on slippery biological surfaces to carry medical instruments, such as a fluid catheter or an electrical wire. Here, for the first time, a team of millirobots (TrainBot) is reported to generate around two times higher actuating force than a TrainBot unit by forming a convoy to collaboratively carry long and heavy cargos. The feet of each unit are optimized to increase the propulsive force around three times so that it can effectively crawl on slippery biological surfaces. A human-scale permanent magnetic set-up is developed to wirelessly actuate and control the TrainBot to transport heavy and lengthy loads through narrow biological lumens, such as the intestine and the bile duct. The first electrocauterization performed by the TrainBot is demonstrated to relieve a biliary obstruction and open a tunnel for fluid drainage and drug delivery. The developed technology sheds light on the collaborative strategy of small-scale robots for future minimally invasive surgical procedures.

Spinal loads during dynamic full flexion and return to standing posture in different age and sex groups: A musculoskeletal model study.

During forward flexion, spine motion varies due to age and sex differences. Previous studies showed that lumbar/pelvis range of flexion (RoF) and lumbo-pelvic ratio (L/P) are age/sex dependent. How variation of these parameters affects lumbar loading in a normal population requires further assessment. We aimed to estimate lumbar loads during dynamic flexion-return cycle and the differences in peak loads (compression) and corresponding trunk inclinations due to variation in lumbar/pelvis RoF and L/P. Based on in vivo L/P (0.11-3.44), temporal phases of flexion (early, middle, and later), the lumbar (45-55°) and hip (60-79°) RoF; full flexion-return cycles of six seconds were reconstructed for three age groups (20-35, 36-50 and 50+ yrs.) in both sexes. Six inverse dynamic analyses were performed with a 50th percentile model, and differences in peak loads and corresponding trunk inclinations were calculated. Peak loads at L4-L5 were 179 N higher in younger males versus females, but 228 N and 210 N lower in middle-aged and older males, respectively, compared to females. Females exhibited higher trunk inclinations (6°-20°) than males across all age groups. Age related differences in L4-L5 peak loads and corresponding trunk inclinations were found up to 415 N and 19° in males and 152 N and 13° in females. With aging, peak loads were reduced in males but were found non-monotonic in females, whereas trunk inclinations at peak loads were reduced in both sexes from young to middle/old age groups. In conclusion, lumbar loading and corresponding trunk inclinations varied notably due to age/sex differences. Such data may help distinguishing normal or pathological condition of the lumbar spine.

Origami-Enhanced Mechanical Properties for Worm-Like Robot.

In recent years, the exploration of worm-like robots has garnered much attention for their adaptability in confined environments. However, current designs face challenges in fully utilizing the mechanical properties of structures/materials to replicate the superior performance of real worms. In this article, we propose an approach to address this limitation based on the stacked Miura origami structure, achieving the seamless integration of structural design, mechanical properties, and robotic functionalities, that is, the mechanical properties originate from the geometric design of the origami structure and at the same time serve the locomotion capability of the robot. Three major advantages of our design are: the implementation of origami technology facilitates a more accessible and convenient fabrication process for segmented robotic skin with periodicity and flexibility, as well as robotic bristles with anchoring effect; the utilization of the Poisson's ratio effect for deformation amplification; and the incorporation of localized folding motion for continuous peristaltic locomotion. Utilizing the high geometric designability inherent in origami, our robot demonstrates customizable morphing and quantifiable mechanical properties. Based on the origami worm-like robot prototype, we experimentally verified the effectiveness of the proposed design in realizing the deformation amplification effect and localized folding motion. By comparing this to a conventional worm-like robot with discontinuous deformation, we highlight the merits of these mechanical properties in enhancing the robot's mobility. To sum up, this article showcases a bottom-up approach to robot development, including geometric design, mechanical characterization, and functionality realization, presenting a unique perspective for advancing the development of bioinspired soft robots.

Accuracy and repeatability of 3D Photogrammetry to digitally reconstruct bones.

Advances in computer hardware and software permit the reconstruction of physical objects digitally from digital camera images. Given the varying shapes and sizes of human bones, a comprehensive assessment is required to establish the accuracy of digital bone reconstructions from three-dimensional (3D) photogrammetry. Five human bones (femur, radius, scapula, vertebra, patella) were marked with pencil, to establish between 9 and 29 landmarks. The distances between landmarks were measured from the physical bones and digitized from 3D reconstructions. Images used for reconstructions were taken on two separate days, allowing for repeatability to be established. In comparison to physical measurements, the mean (±standard deviation) absolute differences were between 0.2±0.1mm and 0.4±0.2mm. The mean (±standard deviation) absolute differences between reconstructions were between 0.3±<0.1mm and 0.4±0.4mm. The 3D photogrammetry procedures described are accurate and repeatable, permitting quantitative analyses to be conducted from digital reconstructions. Moreover, 3D photogrammetry may be used to capture and preserve anatomical materials for anatomy education.

Combining Google Earth historical imagery and UAV photogrammetry for urban development analysis.

Rural-urban migration often triggers additional demand for housing and infrastructural development to cater for the growing population in urban areas. Consequently, town planners and urban development authorities need to understand the urban development trend to make sustainable urban planning decisions. Yet, methods to analyse changes and trends in urban spatial development are often complex and require costly data collection. This article thus presents a simplified method to analyse the urban development trend in an area. The method integrates Google Earth (GE) historical imagery (baseline data) and unmanned aerial vehicle (UAV) photogrammetry (recent data) to quantify the changes over time. This approach can be applied to study the urban development trends in low-income countries with budget constraints. The method is discussed under four main headings: (1) background, (2) method details, (3) limitations, and (4) conclusion.•Google Earth historical image can be extracted with its associated world file.•The population of an area can be estimated by using average household size data and the number of residential buildings in the area.•The building height ratio can be used to ascertain if the land is being used parsimoniously.

Mapping and quantifying medium-term soil loss rates in mountain olive groves using unmanned aerial vehicle technology.

Olive groves are one of the main agroecosystems in the Mediterranean region, but water erosion, aggravated by inappropriate soil management, is compromising the environmental sustainability of these crops. National and international public organisations, including the European Union via its Common Agricultural Policy, have acknowledged the problem and recognise the need to quantify the effects of this process. However, the variability of currently available short-term soil erosion measurements, together with limited understanding of the underlying processes, mean there is considerable uncertainty about the long-term effects of soil erosion. This paper presents an innovative procedure called SERHOLIVE4.0 designed to measure and model long-term soil erosion rates in olive groves, by means of structure-from-motion (SfM) techniques by which image information is obtained from unmanned aerial vehicles (UAVs). For the present study, the procedure was evaluated in mountain olive groves, where the erosion rate was calculated from historical surface reconstructions. Overall, this approach was found to be practical and effective. The method includes the following steps: [1] measure the current relief using UAV technology; [2] reconstruct the historical relief from field measurements; [3] calculate soil truncation (h) and obtain a soil erosion rate map; [4] determine the erosive dynamics of the slope and establish the relation between tree truncation, slope and mounds. The method we describe presents the following advantages:•it quantifies soil losses by reference to existing tree mounds;•it is straightforward to apply;•its application enhances the calibration of erosion models.

Diagnostic efficiency of intravoxel incoherent motion-based virtual magnetic resonance elastography in pulmonary neoplasms.

BACKGROUND: The aim of the study were as below. (1) To investigate the feasibility of intravoxel incoherent motion (IVIM)-based virtual magnetic resonance elastography (vMRE) to provide quantitative estimates of tissue stiffness in pulmonary neoplasms. (2) To verify the diagnostic performance of shifted apparent diffusion coefficient (sADC) and reconstructed virtual stiffness values in distinguishing neoplasm nature. METHODS: This study enrolled 59 patients (37 males, 22 females) with one pulmonary neoplasm who underwent computed tomography-guided percutaneous transthoracic needle biopsy (PTNB) with pathological diagnosis (26 adenocarcinoma, 10 squamous cell carcinoma, 3 small cell carcinoma, 4 tuberculosis and 16 non-specific benign; mean age, 60.81 ± 9.80 years). IVIM was performed on a 3 T magnetic resonance imaging scanner before biopsy. sADC and virtual shear stiffness maps reflecting lesion stiffness were reconstructed. sADC and virtual stiffness values of neoplasm were extracted, and the diagnostic performance of vMRE in distinguishing benign and malignant and detailed pathological type were explored. RESULTS: Compared to benign neoplasms, malignant ones had a significantly lower sADC and a higher virtual stiffness value (P < 0.001). Subsequent subtype analyses showed that the sADC values of adenocarcinoma and squamous cell carcinoma groups were significantly lower than non-specific benign group (P = 0.013 and 0.001, respectively). Additionally, virtual stiffness values of the adenocarcinoma and squamous cell carcinoma subtypes were significantly higher than non-specific benign group (P = 0.008 and 0.001, respectively). However, no significant correlation was found among other subtype groups. CONCLUSIONS: Non-invasive vMRE demonstrated diagnostic efficiency in differentiating the nature of pulmonary neoplasm. vMRE is promising as a new method for clinical diagnosis.

Predictors of lung cancer subtypes and lymph node status in non-small-cell lung cancer: intravoxel incoherent motion parameters and extracellular volume fraction.

OBJECTIVE: To determine the performance of intravoxel incoherent motion (IVIM) parameters and the extracellular volume fraction (ECV) in distinguishing between different subtypes of lung cancer and predicting lymph node metastasis (LNM) status in patients with non-small-cell lung cancer (NSCLC). METHODS: One hundred sixteen patients with lung cancer were prospectively recruited. IVIM, native, and postcontrast T1 mapping examinations were performed, and the T1 values were measured to calculate the ECV. The differences in IVIM parameters and ECV were compared between NSCLC and small-cell lung cancer (SCLC), adenocarcinoma (Adeno-Ca) and squamous cell carcinoma (SCC), and NSCLC without and with LNM. The assessment of each parameter's diagnostic performance was based on the area under the receiver operating characteristic curve (AUC). RESULTS: The apparent diffusion coefficient (ADC), true diffusion coefficient (D), and ECV values in SCLC were considerably lower compared with NSCLC (all p < 0.001, AUC > 0.887). The D value in SCC was substantially lower compared with Adeno-Ca (p < 0.001, AUC = 0.735). The perfusion fraction (f) and ECV values in LNM patients were markedly higher compared with those without LNM patients (p < 0.01, < 0.001, AUC > 0.708). CONCLUSION: IVIM parameters and ECV can serve as non-invasive biomarkers for assisting in the pathological classification and LNM status assessment of lung cancer patients. CRITICAL RELEVANCE STATEMENT: IVIM parameters and ECV demonstrated remarkable potential in distinguishing pulmonary carcinoma subtypes and predicting LNM status in NSCLC. KEY POINTS: Lung cancer is prevalent and differentiating subtype and invasiveness determine the treatment course. True diffusion coefficient and ECV showed promise for subtyping and determining lymph node status. These parameters could serve as non-invasive biomarkers to help determine personalized treatment strategies.