Acquisition Parameters Research Articles

Adaptive control for downhole nuclear magnetic resonance excitation

Nuclear magnetic resonance (NMR) measurements are performed with the pulse sequence and acquisition parameters set by the operator, which cannot be adjusted in real time according to sample characteristics. In one acquisition cycle, usually thousands of high-power pulses are transmitted and thousands of echo points are acquired. The power consumption cause the RF amplifier to overheat, and large amounts of acquired data may be invalid. Therefore, the optimization of excitation and acquisition processes is necessary to improve measurement efficiency. We explore a scheme for the real-time measurement of the samples by adaptively regulating the pulse sequence, which adapts the variable TE pulse sequence as the reconnaissance mode. The appropriate pulse sequence and reasonable parameters (NE, TE) can be selected according to the relaxation characteristics of the samples.This adaptive control strategy has great significance in guiding both dynamic and static measurements, and it is especially suitable for occasions where low magnetic field gradients and diffusion terms can be ignored. We also design a test circuit for adaptive control, which has the function of automatic parameter adjustment. By adjusting parameters such as the number of refocusing pulses, echo spacing, etc., the effective measurement of the samples can be achieved in practice.

Quality control procedures and metrics for resting-state functional MRI.

The monitoring and assessment of data quality is an essential step in the acquisition and analysis of functional MRI (fMRI) data. Ideally data quality monitoring is performed while the data are being acquired and the subject is still in the MRI scanner so that any errors can be caught early and addressed. It is also important to perform data quality assessments at multiple points in the processing pipeline. This is particularly true when analyzing datasets with large numbers of subjects, coming from multiple investigators and/or institutions. These quality control procedures should monitor not only the quality of the original and processed data, but also the accuracy and consistency of acquisition parameters. Between-site differences in acquisition parameters can guide the choice of certain processing steps (e.g., resampling from oblique orientations, spatial smoothing). Various quality control metrics can determine what subjects to exclude from the group analyses, and can also guide additional processing steps that may be necessary. This paper describes a combination of qualitative and quantitative assessments to determine the quality of fMRI data. Processing is performed using the AFNI data analysis package. Qualitative assessments include visual inspection of the structural T1-weighted and fMRI echo-planar images, functional connectivity maps, functional connectivity strength, and temporal signal-to-noise maps concatenated from all subjects into a movie format. Quantitative metrics include the acquisition parameters, statistics about the level of subject motion, temporal signal-to-noise ratio, smoothness of the data, and the average functional connectivity strength. These measures are evaluated at different steps in the processing pipeline to catch gross abnormalities in the data, and to determine deviations in acquisition parameters, the alignment to template space, the level of head motion, and other sources of noise. We also evaluate the effect of different quantitative QC cutoffs, specifically the motion censoring threshold, and the impact of bandpass filtering. These qualitative and quantitative metrics can then provide information about what subjects to exclude and what subjects to examine more closely in the analysis of large datasets.

Comparison of GEDI LiDAR Data Capability for Forest Canopy Height Estimation over Broadleaf and Needleleaf Forests

The GEDI LiDAR system was specifically designed to detect vegetation structure and has proven to be a suitable tool for estimating forest biophysical parameters, especially canopy height, at a global scale. This study compares the GEDI relative height metric (RH100) over different forest types, especially deciduous broadleaf and evergreen coniferous located in Thuringia, Germany, to understand how the forest structural differences affect the GEDI height estimation. A canopy height model that was produced using digital terrain and surface models (DTM and DSM) derived from airborne laser scanning data is used as the reference data. Based on the result, GEDI canopy height over needleleaf forest is slightly more accurate (RMSE = 6.61 m) than that over broadleaf (RMSE = 8.30 m) and mixed (RMSE = 7.94 m) forest. Evaluation of the GEDI acquisition parameters shows that differences in beam type, sensitivity, and acquisition time do not significantly affect the accuracy of canopy heights, especially over needleleaf forests. Considering foliage condition impacts on canopy height estimation, the contrasting result is observed in the broadleaf and needleleaf forests. The GEDI dataset acquired during the winter when deciduous species shed their leaves (the so-called leaf-off dataset), outperforms the leaf-on dataset in the broadleaf forest but shows less accurate results for the needleleaf forest. Considering the effect of the plant area index (PAI) on the accuracy of the GEDI canopy height, the GEDI dataset is divided into two sets with low and high PAI values with a threshold of median PAI = 2. The results show that the low PAI dataset (median PAI < 2) corresponds to the non-growing season (autumn and winter) in the broadleaf forest. The slightly better performance of GEDI using the non-growing dataset (RMSE = 7.40 m) compared to the growing dataset (RMSE = 8.44 m) in the deciduous broadleaf forest and vice versa, the slightly better result using the growing dataset (RMSE = 6.38 m) compared to the non-growing dataset (RMSE = 7.24 m) in the evergreen needleleaf forest is in line with the results using the leaf-off/leaf-on season dataset. Although a slight improvement in canopy height estimation was observed using either the leaf-off or non-growing season dataset for broadleaf forest, and either the leaf-on or growing season dataset for needleleaf forest, the approach of filtering GEDI data based on such seasonal acquisition time is recommended when retrieving canopy height over pure stands of broadleaf or needleleaf species, and the sufficient dataset is available.

Digitization, Visualization and Accessibility of Globe Virtual Collection: Case Study Jüttner’s Globe

The aim of the article is to prepare a model for making available metadata and digital objects of the new Globe Virtual Collection for the Map Collection of the Faculty of Science of Charles University. The globes are special cartographic documents; therefore, they are also described in a special way. The article deals with the digitization, visualization and accessibility of an old globe by Josef Jüttner from 1839, which comes from the depository of one of the most important central European collections. A simple model for a new virtual processing of the globe collection at Charles University is presented. SfM-MVS photogrammetry was chosen for digitization of the globe. The basic elements of the copperplate were set as basic parameters for image acquisition. Contrast, density, black line, line, dash and dot patterns and their complex use were observed for a good graphic design of the globe. Other parameters included a closer determination of the users for whom the resulting product is intended, as well as the profile of the users’ behavior on the site so far. New metadata were extracted from the bibliographic description. The virtual 3D globe was integrated into the database using the Cesium JavaScript library. Metadata and a 3D model of the globe were linked together and made available to the general public on the Globe page of the Map Collection of the Faculty of Science of Charles University. A comparison of web browsers was performed focusing on the loading time of the 3D model on the website. New graphic elements were identified with the new processing. It was possible to read the factual information written on the globe. Different possibilities and limitations of metadata description, photogrammetric methods and web presentation are described. This good practice can be applied by other virtual map collections.

Understanding and Optimizing Capacitance Performance in Reduced Graphene-Oxide Based Supercapacitors.

Reduced graphene-oxide (RGO)-based electrodes in supercapacitors deliver high energy/power capacities compared to typical nanoporous carbon materials. However, extensive critical analysis of literature reveals enormous discrepancies (up to 250 F g-1 ) in the reported capacitance (variation of 100-350 F g-1 ) of RGO materials synthesized under seemingly similar methods, inhibiting an understanding of capacitance variation. Here, the key factors that control the capacitance performance of RGO electrodes are demonstrated by analyzing and optimizing various types of commonly applied electrode fabrication methods. Beyond usual data acquisition parameters and oxidation/reduction properties of RGO, a substantial difference of more than 100% in capacitance values (with change from 190 ± 20 to 340 ± 10 F g-1 ) is found depending on the electrode preparation method. For this demonstration, ≈40 RGO-based electrodes are fabricated from numerous distinctly different RGO materials via typically applied methods of solution (aqueous and organic) casting and compressed powders. The influence of data acquisition conditions and capacitance estimation practices are also discussed. Furthermore, by optimizing electrode processing method, a direct surface area governed capacitance relationship for RGO structures is revealed.

Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics.

Data-Dependent and Data-Independent Acquisition modes (DDA and DIA, respectively) are both widely used to acquire MS2 spectra in untargeted liquid chromatography tandem mass spectrometry (LC-MS/MS) metabolomics analyses. Despite their wide use, little work has been attempted to systematically compare their MS/MS spectral annotation performance in untargeted settings due to the lack of ground truth and the costs involved in running a large number of acquisitions. Here, we present a systematic in silico comparison of these two acquisition methods in untargeted metabolomics by extending our Virtual Metabolomics Mass Spectrometer (ViMMS) framework with a DIA module. Our results show that the performance of these methods varies with the average number of co-eluting ions as the most important factor. At low numbers, DIA outperforms DDA, but at higher numbers, DDA has an advantage as DIA can no longer deal with the large amount of overlapping ion chromatograms. Results from simulation were further validated on an actual mass spectrometer, demonstrating that using ViMMS we can draw conclusions from simulation that translate well into the real world. The versatility of the Virtual Metabolomics Mass Spectrometer (ViMMS) framework in simulating different parameters of both Data-Dependent and Data-Independent Acquisition (DDA and DIA) modes is a key advantage of this work. Researchers can easily explore and compare the performance of different acquisition methods within the ViMMS framework, without the need for expensive and time-consuming experiments with real experimental data. By identifying the strengths and limitations of each acquisition method, researchers can optimize their choice and obtain more accurate and robust results. Furthermore, the ability to simulate and validate results using the ViMMS framework can save significant time and resources, as it eliminates the need for numerous experiments. This work not only provides valuable insights into the performance of DDA and DIA, but it also opens the door for further advancements in LC-MS/MS data acquisition methods.

Adversarial co-training for semantic segmentation over medical images

Background and objective:Abundant labeled data drives the model training for better performance, but collecting sufficient labels is still challenging. To alleviate the pressure of label collection, semi-supervised learning merges unlabeled data into training process. However, the joining of unlabeled data (e.g., data from different hospitals with different acquisition parameters) will change the original distribution. Such a distribution shift leads to a perturbation in the training process, potentially leading to a confirmation bias. In this paper, we investigate distribution shift and develop methods to increase the robustness of our models, with the goal of improving performance in semi-supervised semantic segmentation of medical images. We study distribution shift and increase model robustness to it, for improving practical performance in semi-supervised segmentation over medical images. Methods:To alleviate the issue of distribution shift, we introduce adversarial training into the co-training process. We simulate perturbations caused by the distribution shift via adversarial perturbations and introduce the adversarial perturbation to attack the supervised training to improve the robustness against the distribution shift. Benefiting from label guidance, supervised training does not collapse under adversarial attacks. For co-training, two sub-models are trained from two views (over two disjoint subsets of the dataset) to extract different kinds of knowledge independently. Co-training outperforms single-model by integrating both views of knowledge to avoid confirmation bias. Results:For practicality, we conduct extensive experiments on challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts (Yu and Wang, 2019; Peng et al., 2020; Perone et al., 2019). We achieve a DSC score of 87.37% with only 20% of labels on the ACDC dataset, almost same to using 100% of labels. On the SCGM dataset with more distribution shift, we achieve a DSC score of 78.65% with 6.5% of labels, surpassing 10.30% over Peng et al. (2020). Our evaluative results show superior robustness against distribution shifts in medical scenarios. Conclusion:Empirical results show the effectiveness of our work for handling distribution shift in medical scenarios.

4DCT is long overdue for improvement.

4DCT is long overdue for improvement.

A Framework for Simulating Cardiac MR Images with Varying Anatomy and Contrast.

One of the limiting factors for the development and adoption of novel deep-learning (DL) based medical image analysis methods is the scarcity of labeled medical images. Medical image simulation and synthesis can provide solutions by generating ample training data with corresponding ground truth labels. Despite recent advances, generated images demonstrate limited realism and diversity. In this work, we develop a flexible framework for simulating cardiac magnetic resonance (MR) images with variable anatomical and imaging characteristics for the purpose of creating a diversified virtual population. We advance previous works on both cardiac MR image simulation and anatomical modeling to increase the realism in terms of both image appearance and underlying anatomy. To diversify the generated images, we define parameters: 1) to alter the anatomy, 2) to assign MR tissue properties to various tissue types, and 3) to manipulate the image contrast via acquisition parameters. The proposed framework is optimized to generate a substantial number of cardiac MR images with ground truth labels suitable for downstream supervised tasks. A database of virtual subjects is simulated and its usefulness for aiding a DL segmentation method is evaluated. Our experiments show that training completely with simulated images can perform comparable with a model trained with real images for heart cavity segmentation in mid-ventricular slices. Moreover, such data can be used in addition to classical augmentation for boosting the performance when training data is limited, particularly by increasing the contrast and anatomical variation, leading to better regularization and generalization. The database is publicly available at https://osf.io/ bkzhm/ and the simulation code will be available at https: //github.com/sinaamirrajab/CMRI_Simulation.

Novel synergism of Cedecea lapagei KU14 and Bacillus subtilis KU21 for sustainable productivity of Rosmarinus officinalis in Northwest Himalayas

The current study aimed to assess the scope of native potential endophytic bacteria Bacillus subtilis KU21, Pseudomonas aeruginosa SI12 and Cedecea lapagei KU14 for synergistic effect to develop as consortium biofertilizer of rosemary. All the three strains were assessed for multiple plant growth promoting (PGP) traits viz., P solubilization, siderophore and Indole-3-acetic acid production (IAA). Consortia of strains (KU21, SI12 and KU14) showed significantly high production of IAA, siderophore production and P-solubilization over the single inoculants. All the isolates i.e., KU21, SI12 and KU14 individually and their consortia were assessed for in vivo growth studies of rosemary under net house conditions. Combined application of these strains significantly increased growth parameters of rosemary over single inoculation and untreated control. Based on the results, two consortia i.e., B. subtilis KU21+ C. lapagei KU14 and B. subtilis KU21+ C. lapagei KU14+ P. aeruginosa SI12 were further evaluated under field conditions. The combined application of B. subtilis KU21+ C. lapagei KU14 significantly improved rosemary growth parameters (at vegetative and flowering stage), nutrient acquisition, and soil quality parameters over uninoculated control. This is the first field report that describes the efficacy of novel endophytic strains i.e., B. subtilis KU21+ C. lapagei KU14 for improving rosemary production and soil health simultaneously, hence may be recommended for sustainable production of R. officinalis in Northwest Himalayan region.

Application of full-waveform inversion to land data: Case studies in onshore Mexico

Velocity model building and imaging for land surveys are often challenging due to near-surface complexity contaminating the reflection signal. Incorporating full-waveform inversion (FWI) in the velocity model building workflow for land surveys offers benefits not achieved with traditional model building tools, but it also brings difficulties. We have developed an effective model building workflow for land seismic data that incorporates dynamic matching FWI (DMFWI). DMFWI employs an objective function that uses multidimensional local windowed crosscorrelations between the dynamically matched version of observed and synthetic data. Dynamic matching de-emphasizes the impact of amplitudes, allowing the algorithm to focus on using kinematic information for velocity updates. The proposed workflow produces a geologically plausible and consistent model for data acquired with limited offsets. Refraction and reflection tomography may also be included in the workflow. The workflow is applied to onshore surveys in Mexico. Despite challenges of the near-surface geology and limitations of the acquisition parameters in the study areas, the proposed model building workflow successfully derives a high-resolution velocity model that significantly improves the migrated depth image.

The effectiveness of atrial fibrillation identification using noninvasive long-term electrocardiographic monitoring system (NOMED-AF TECH).

Although it is well‑known that longer electrocardiographic (ECG) monitoring allows for detection of paroxysmal silent atrial fibrillation (SAF), it is still unknown how long the ECG monitoring should last to increase the probability of SAF diagnosis. Our aim was to analyze ECG acquisition parameters and timing to detect SAF during the Noninvasive Monitoring for Early Detection of Atrial Fibrillation study. The protocol assumed up to 30 days of ECG telemonitoring of each participant in order to reveal AF / atrial flutter (AFL) episodes lasting at least 30 seconds. SAF was defined as AF detected and confirmed by cardiologists in asymptomatic individuals. The ECG signal analysis was based on the results of 2974 participants (98.67%). AF/AFL episodes were registered and confirmed by cardiologists in 515 individuals, that is, 75.7% of all patients (n = 680) in whom AF/AFL diagnosis was established. The median monitoring time to detect the first SAF episode was 6 days (interquartile range [IQR], 1-13). Fifty percent of the patients with this type of arrhythmia were identified by 6th day (IQR, 1-13) of the monitoring, and 75% by the 13th day of the study. Paroxysmal AF was registered on average on 4th day (IQR, 1-10). The ECG monitoring time to detect the first episode of SAF in at least 75% of patients at risk of this arrhythmia was 14 days. Detection of 1 patient with de novo AF, SAF, or de novo SAF, required monitoring of, respectively, 17, 11, and 23 patients.

Ultrasound localization microscopy of the human kidney allograft on a clinical ultrasound scanner

Chronic kidney disease is a major medical problem, causing more than a million deaths each year worldwide. Peripheral kidney microvascular damage characterizes most chronic kidney diseases, yet noninvasive and quantitative diagnostic tools to measure this are lacking. Ultrasound Localization Microscopy (ULM) can assess tissue microvasculature with unprecedented resolution. Here, we optimized methods on 35 kidney transplants and studied the feasibility of ULM in seven human kidney allografts with a standard low frame rate ultrasound scanner to access microvascular damage. Interlobar, arcuate, cortical radial vessels, and part of the medullary organization were visible on ULM density maps. The medullary vasa recta can be seen but are not as clear as the cortical vessels. Acquisition parameters were derived from Contrast-Enhanced Ultrasound examinations by increasing the duration of the recorded clip at the same plane. ULM images were compared with Color Doppler, Advanced Dynamic Flow, and Superb Microvascular Imaging with a contrast agent. Despite some additional limitations due to movement and saturation artifacts, ULM identified vessels two to four times thinner compared with Doppler modes. The mean ULM smallest analyzable vessel cross section was 0.3 ± 0.2 mm in the seven patients. Additionally, ULM was able to provide quantitative information on blood velocities in the cortical area. Thus, this proof-of-concept study has shown ULM to be a promising imaging technique for qualitative and quantitative microvascular assessment. Imaging native kidneys in patients with kidney diseases will be needed to identify their ULM biomarkers.

Seismic amplitude response to internal heterogeneity of mass-transport deposits

Abstract. Compared to unfailed sediments, mass-transport deposits are often characterised by a low-amplitude response in single-channel seismic reflection images. This “acoustic transparency” amplitude signature is widely used to delineate mass-transport deposits and is conventionally interpreted as a lack of coherent internal reflectivity due to a loss of preserved internal structure caused by mass-transport processes. In this study we examine the variation in the single-channel seismic response with changing heterogeneity using synthetic 2-D elastic seismic modelling. We model the internal structure of mass-transport deposits as a two-component random medium, using the lateral correlation length (ax) as a proxy for the degree of internal deformation. The average internal reflectivity is held approximately constant with increasing deformation by fixing the two component sediment lithologies to have realistic P-wave velocity and density based on sediment core measurements from the study area. For a controlled single-source synthetic model a reduction in observed amplitude with reduced ax is consistently observed across a range of vertical correlation lengths (az). For typical autonomous underwater vehicle (AUV) sub-bottom profiler acquisition parameters, in a simulated mass-transport deposit with realistic geostatistical properties, we find that when ax≈1 m, recorded seismic amplitudes are, on average, reduced by ∼25 % relative to unfailed sediments (ax≫103 m). We also observe that deformation significantly larger than core scale (ax>0.1 m) can generate a significant amplitude decrease. These synthetic modelling results should discourage interpretation of the internal structure of mass-transport deposits based on seismic amplitudes alone, as acoustically transparent mass-transport deposits may still preserve coherent, metre-scale internal structure. In addition, the minimum scale of heterogeneity required to produce a significant reduction in seismic amplitudes is likely much larger than the typical diameter of sediment cores, meaning that acoustically transparent mass-transport deposits may still appear well stratified and undeformed at core scale.

Machine learning based de-noising of electron back scatter patterns of various crystallographic metallic materials fabricated using laser directed energy deposition

A novel machine learning (ML) method of refining noisy Electron Back Scatter Patterns (EBSP) is proposed. For this, conditional generative adversarial networks (c-GAN) have been employed. The problem of de-noising the EBSPs was formulated as an image translation task conditioned on the input images to get refined/denoised output of EBSPs which can be indexed using conventional Hough transform based indexing algorithms. The ML model was trained using 10,000 EBSPs acquired under different settings for additively manufactured FCC, BCC and HCP alloy samples ensuring enough diversity and complexity in training data set. Pairs of noisy and corresponding optimal EBSPs were acquired by suitable tweaking of the EBSP acquisition parameters such as beam defocus, pattern binning and EBSD camera exposure duration. The trained model has brought out significant improvement in EBSD indexing success rate on test data, accompanied by betterment of indexing accuracy, quantified through ‘pattern fit’. Complete automation of the EBSP refinement was demonstrated where in entire EBSD scan data can be fed to the model to get the refined EBSPs from which high quality EBSD data can be obtained.

Information flow and dynamic functional connectivity during electroconvulsive therapy in patients with depression

BackgroundElectroconvulsive therapy is effectively used for treatment-resistant depression; however, its neural mechanism is largely unknown. Resting-state functional magnetic resonance imaging is promising for monitoring outcomes of electroconvulsive therapy for depression. This study aimed to explore the imaging correlates of the electroconvulsive therapy effects on depression using Granger causality analysis and dynamic functional connectivity analyses. MethodsWe performed advanced analyses of resting-state functional magnetic resonance imaging data at the beginning and intermediate stages and end of the therapeutic course to identify neural markers that reflect or predict the therapeutic effects of electroconvulsive therapy on depression. ResultsWe demonstrated that information flow between the functional networks analyzed by Granger causality changes during electroconvulsive therapy, and this change was correlated with the therapeutic outcome. Information flow and the dwell time (an index reflecting the temporal stability of functional connectivity) before electroconvulsive therapy are correlated with depressive symptoms during and after treatment. LimitationsFirst, the sample size was small. A larger group is needed to confirm our findings. Second, the influence of concomitant pharmacotherapy on our results was not fully addressed, although we expected it to be minimal because only minor changes in pharmacotherapy occurred during electroconvulsive therapy. Third, different scanners were used the groups, although the acquisition parameters were the same; a direct comparison between patient and healthy participant data was not possible. Thus, we presented the data of the healthy participants separately from that of the patients as a reference. ConclusionsThese results show the specific properties of functional brain connectivity.

Digital colorimetric sensor for assessment of iodate in food-grade salt based on paper-based analytical device

A digital colorimetric sensor was developed for the on-site measurement of iodate in food-grade salt. It involved a paper-based analytical device (PAD) containing an immobilized iodide/starch reagent and a smartphone as the detection system. Iodate ions produce a blue-black coloration on the PAD, the smartphone captures a digital image of the PAD, and an installed mobile app performs an analysis to generate color values related to the intensity of the color in the PAD. Parameters in the image acquisition and PAD preparation were optimized, and a matrix-matched calibration plot was employed for quantification. The plot exhibited a linear response in the concentration range of 10 to 100 mg iodate kg−1 of salt, and a detection limit of 2.20 mg kg−1 was determined. The present method was applied to analyze real samples, and the results agreed well with those obtained using the reference method at the 95% confidence level.

Commissioning, clinical implementation, and initial experience with a new brain tumor treatment package on a low-field MR-linac.

To evaluate the image quality, dosimetric properties, setup reproducibility, and planar cine motion detection of a high-resolution brain coil and integrated stereotactic brain immobilization system that constitute a new brain treatment package (BTP) on a low-field magnetic resonance imaging (MRI) linear accelerator (MR-linac). Image quality of the high-resolution brain coil was evaluated with the 17cm diameter spherical phantom and the American College of Radiology (ACR) Large MRI Phantom. Patient imaging studies approved by the institutional review board (IRB) assisted in selecting image acquisition parameters. Radiographic and dosimetric evaluation of the high-resolution brain coil and the associated immobilization devices was performed using dose calculations and ion chamber measurements. End-to-end testing was performed simulating a cranial lesion in a phantom. Inter-fraction setup variability and motion detection tests were evaluated on four healthy volunteers. Inter-fraction variability was assessed based on three repeat setups for each volunteer. Motion detection was evaluated using three-plane (axial, coronal, and sagittal) MR-cine imaging sessions, where volunteers were asked to perform a set of specific motions. The images were post-processed and evaluated using anin-house program. Contrast resolution of the high-resolution brain coil is superior to the head/neck and torso coils. The BTP receiver coils have an average HU value of 525 HU. The most significant radiation attenuation (3.14%) of the BTP, occurs through the lateral portion of the overlay board where the high-precision lateral-profile mask clips attach to the overlay. The greatest inter-fraction setup variability occurred in the pitch (average 1.08 degree) and translationally in the superior/inferior direction (average 4.88mm). Three plane cine imaging with the BTP was able to detect large and small motions. Small voluntary motions, sub-millimeter in magnitude (maximum 0.9mm), from motion of external limbs were detected. Imaging tests, inter-fraction setup variability, attenuation, and end-to-end measurements were quantified and performed for the BTP. Results demonstrate better contrast resolution and low contrast detectability that allows for better visualization of soft tissue anatomical changes relative to head/neck and torso coil systems.

Quantifying robustness of CT-ventilation biomarkers to image noise.

Purpose: To quantify the impact of image noise on CT-based lung ventilation biomarkers calculated using Jacobian determinant techniques. Methods: Five mechanically ventilated swine were imaged on a multi-row CT scanner with acquisition parameters of 120kVp and 0.6mm slice thickness in static and 4-dimensional CT (4DCT) modes with respective pitches of 1 and 0.09. A range of tube current time product (mAs) values were used to vary image dose. On two dates, subjects received two 4DCTs: one with 10mAs/rotation (low-dose, high-noise) and one with CT simulation standard of care 100mAs/rotation (high-dose, low-noise). Additionally, 10 intermediate noise level breath-hold (BHCT) scans were acquired with inspiratory and expiratory lung volumes. Images were reconstructed with and without iterative reconstruction (IR) using 1mm slice thickness. The Jacobian determinant of an estimated transformation from a B-spline deformable image registration was used to create CT-ventilation biomarkers estimating lung tissue expansion. 24 CT-ventilation maps were generated per subject per scan date: four 4DCT ventilation maps (two noise levels each with and without IR) and 20 BHCT ventilation maps (10 noise levels each with and without IR). Biomarkers derived from reduced dose scans were registered to the reference full dose scan for comparison. Evaluation metrics were gamma pass rate (Γ) with 2mm distance-to-agreement and 6% intensity criterion, voxel-wise Spearman correlation (ρ) and Jacobian ratio coefficient of variation (CoV JR ). Results: Comparing biomarkers derived from low (CTDI vol = 6.07mGy) and high (CTDI vol = 60.7mGy) dose 4DCT scans, mean Γ, ρ and CoV JR values were 93% ± 3%, 0.88 ± 0.03 and 0.04 ± 0.009, respectively. With IR applied, those values were 93% ± 4%, 0.90 ± 0.04 and 0.03 ± 0.003. Similarly, comparisons between BHCT-based biomarkers with variable dose (CTDI vol = 1.35-7.95mGy) had mean Γ, ρ and CoV JR of 93% ± 4%, 0.97 ± 0.02 and 0.03 ± 0.006 without IR and 93% ± 4%, 0.97 ± 0.03 and 0.03 ± 0.007 with IR. Applying IR did not significantly change any metrics (p 0.05). Discussion: This work demonstrated that CT-ventilation, calculated using the Jacobian determinant of an estimated transformation from a B-spline deformable image registration, is invariant to Hounsfield Unit (HU) variation caused by image noise. This advantageous finding may be leveraged clinically with potential applications including dose reduction and/or acquiring repeated low-dose acquisitions for improved ventilation characterization.

The validation of low-dose CT scans from the [18F]-FDG PET-CT scan to assess skeletal muscle mass in comparison with diagnostic neck CT scans

PurposeRadiologically defined sarcopenia, or a low skeletal muscle index (SMI), is an emerging biomarker for adverse clinical outcomes in head and neck cancer (HNC) patients. Recently, SMI measurements have been validated at the level of the third cervical vertebra (C3) on diagnostic neck CT scans but are not yet validated on low-dose (LD) neck CT scans from the [18F]-FDG PET-CT. This hampers SMI analysis in HNC patients without a diagnostic neck CT but with a [18F]-FDG PET-CT scan. Therefore, the aim was to study whether (low) SMI based on LD CT scan from [18F]-FDG PET-CT is comparable to those derived from diagnostic neck CT scans.MethodsHNC patients with both diagnostic CT and [18F]-FDG PET-CT of the neck were prospectively included into the OncoLifeS data-biobank. Skeletal muscle was retrospectively delineated at the level of the third cervical vertebra (C3), and (low) SMI (cm2/m2) was calculated for diagnostic and LD neck CTs. (Low) SMI from the diagnostic neck CT was considered the reference standard. Intra-class correlation coefficient (ICC), Bland–Altman plots, and Cohen’s Kappa analysis were performed.ResultsThe cohort (n = 233) mean age was 66.2 ± 12.8 years, and 74.2% of patients were male. Inter-rater reliability was excellent (ICC > 0.990, 95% confidence interval 0.975–0.996, p < 0.001). The agreement of SMI between both modalities was high according to the Bland–Altman plot (mean ΔSMI = − 0.19 cm2/m2), and there was no substantial bias. Cohen’s Kappa analysis showed an almost perfect agreement of low SMI between the two modalities (κ = 0.911, p < 0.001). The position of arms didn't affect the high agreement of (low) SMI.ConclusionSkeletal muscle mass, as measured with (low) SMI, remains constant irrespective of CT acquisition parameters (diagnostic neck CT scans versus LD neck scans of the [18F]-FDG PET-CT scan), positioning of arms, and observers. These findings contribute to the construction of a clinically useful radiological biomarker for SMI and therefore identify patients at risk for adverse clinical outcomes.