Data Acquisition Time Research Articles

Development and optimization of a backscatter X-ray detection system based on pencil beam scanning

The backscatter X-ray system makes an image by detecting scattered particles from an object in the opposite direction by Compton scattering. This system has the advantage of detecting drugs or explosives composed of organic substances. In this study, we developed a backscatter X-ray detection system. The 6-ch DAQ system is able to store acquired data for 50 μs as 8 bytes and the 6-ch high voltage power supply board provides adjustable power from 0 to 1600 V. Using this detection system, we varied the tube voltage from 80 to 160 kV and observed backscatter X-ray images of two plastic phantoms at 160 kV. The results indicate a need to optimize the speed of the collimator and data acquisition time of the DAQ system to improve image quality. The backscatter X-ray imaging technology exhibits high scalability, making it applicable not only to security screening but also for medical and industrial purposes.

Feature Fusion for Multi-Coil Compressed MR Image Reconstruction.

Magnetic resonance imaging (MRI) occupies a pivotal position within contemporary diagnostic imaging modalities, offering non-invasive and radiation-free scanning. Despite its significance, MRI's principal limitation is the protracted data acquisition time, which hampers broader practical application. Promising deep learning (DL) methods for undersampled magnetic resonance (MR) image reconstruction outperform the traditional approaches in terms of speed and image quality. However, the intricate inter-coil correlations have been insufficiently addressed, leading to an underexploitation of the rich information inherent in multi-coil acquisitions. In this article, we proposed a method called "Multi-coil Feature Fusion Variation Network" (MFFVN), which introduces an encoder to extract the feature from multi-coil MR image directly and explicitly, followed by a feature fusion operation. Coil reshaping enables the 2D network to achieve satisfactory reconstruction results, while avoiding the introduction of a significant number of parameters and preserving inter-coil information. Compared with VN, MFFVN yields an improvement in the average PSNR and SSIM of the test set, registering enhancements of 0.2622dB and 0.0021dB respectively. This uplift can be attributed to the integration of feature extraction and fusion stages into the network's architecture, thereby effectively leveraging and combining the multi-coil information for enhanced image reconstruction quality. The proposed method outperforms the state-of-the-art methods on fastMRI dataset of multi-coil brains under a fourfold acceleration factor without incurring substantial computation overhead.

Low-altitude remote sensing-based global 3D path planning for precision navigation of agriculture vehicles - beyond crop row detection

Field path planning provides the basis for the autonomous navigation of agricultural vehicles. Existing path planning approaches are constrained to a 2D plane, disregarding the impact of terrain factors on navigation tasks. Furthermore, these methods generate non-global paths, as evidenced by their inability to accommodate the headland turning area while performing path planning based on the actual crop growth in the farmland. Low-altitude remote sensing technology, characterized by its high spatial resolution, timely data acquisition, and strong terrain perception, holds great potential for application in autonomous navigation. This study aims to integrate low-altitude remote sensing technology with path-planning tasks for agricultural vehicles by constructing oblique photography models of fields. The objective is to achieve global 3D path planning leveraging advanced methodologies rooted in deep learning and image processing. Four main steps were included in the proposed method. Low-altitude remote sensing models of field blocks were constructed first. Secondly, the models were converted to image patches for dataset establishment. Thirdly, primary crop row identification was conducted by semantic segmentation. Finally, global 3D paths covering the farmland and headland areas were generated. Tea fields in hilly areas were used to test the algorithm. Experiments revealed that the proposed method could be adapted to different field shapes, row numbers, row spacing, and vehicle turning radii. The proposed method uses deep learning and image processing as the primary technical tools but goes beyond traditional crop row detection to form a global path-planning strategy. In addition, the elevation information enabled by the 3D detection manner allows agricultural vehicles to gain a comprehensive understanding of both their own position and that of their destination. The dataset and the code are available at: https://github.com/ZeroHeading/Global-3D-path-generation.git.

Single posterior implant-supported restorations fabricated using a scannable healing abutment versus a conventional scan body: A randomized controlled trial

Statement of problemThe use of a scannable healing abutment is a convenient option for fabricating implant-supported restorations (ISRs) with a digital workflow; however, clinical studies evaluating prosthetic efficacy are lacking. PurposeThe purpose of this randomized controlled trial was to investigate the prosthetic efficacy of definitive posterior single ISRs fabricated after scanning using a scannable healing abutment-scan peg (SHA-SP) in comparison with a conventional scan body (CSB). The time for data acquisition, quality of proximal and occlusal contacts, and relative occlusal force of ISRs were measured. Material and methodsTwenty-four participants eligible for single ISRs to replace the mandibular first molar with adjacent and antagonist teeth present were randomly allocated to either a study group (n=12) receiving ISRs after intraoral scanning using an SHA-SP or a control group (n=12) receiving ISRs after intraoral scanning using CSB. During the surgical procedure, a prefabricated contoured scannable healing abutment was screwed to the implant in the SHA-SP group, while a custom-made healing abutment was used in the CSB group. After a healing period of 3 months, an intraoral scan was made, and the duration of data acquisition was recorded. The ISRs were milled from zirconia and evaluated for the quality of proximal and occlusal contacts using dental floss and shim stock, respectively. The relative occlusal forces of the ISRs and their contralateral natural teeth were measured using a digital occlusal analyzer. Statistical analysis was done using an independent sample t test for quantitative variables and a Pearson chi-squared test for qualitative variables between the tested groups (α=.05). ResultsThe direct digital workflow using SHA-SP was statistically less time consuming than the CSB (P<.001). The 2 groups were statistically similar regarding the quality of the proximal contacts (P=.281) or occlusal contacts (P=.307) and the relative occlusal forces of ISRs (P=.315). The relative occlusal forces of the ISRs in both groups were significantly lower than those of their contralateral natural teeth (P<.001). ConclusionsDirect digital workflow using SHA-SP was more rapid, saving clinical chairside time, and produced proximal and occlusal contacts of comparable quality with those obtained with CSB. The relative occlusal forces of ISRs in both workflows were lower than their contralateral natural teeth.

Contrast-free microvessel imaging using null subtraction imaging combined with harmonic imaging

Ultrasound localization microscopy (ULM) has superior spatial resolution; however, it requires contrast agents and long data acquisition and processing time. Null subtraction imaging (NSI) is a nonlinear beamforming technique that improves the spatial resolution and reduces grating lobes with low computational cost. We combined pulse-inversion (PI) nonlinear imaging with NSI to increase resolution. Pulses with center frequency of 10.42 MHz and its inverted version were transmitted to obtain the second harmonic at 20.84 MHz (wavelength of ∼74 μm). The transducer pitch was 35% larger than the wavelength of the receive signal, which would introduce grating lobes in the field of view. A total of 1000, 9-angle (−8 to 8 degree in 2 degrees step) coherently compounded frames were acquired in 1 s. SVD filters were applied in raw RF data to filter out tissue signal. The dc offset was set to 0.1 in NSI imaging. Grating lobes, which were obvious in DAS images, were removed in the NSI images. Higher spatial resolution and contrast were observed from the NSI microvessel images. The spatial resolution of the NSI images using harmonic imaging approached one fourth of a wavelength with computation time increased by only 40% compared to DAS.

Optical-pump-terahertz-probe spectroscopy in high magnetic fields with kHz single-shot detection.

We demonstrate optical pump-THz probe (OPTP) spectroscopy with a variable external magnetic field (0-9T), in which the time-dependent THz signal is measured by echelon-based single-shot detection at a repetition rate of 1kHz. The method reduces data acquisition times by more than an order of magnitude compared to conventional electro-optic sampling using a scanning delay stage. The approach illustrates the wide applicability of the single-shot measurement approach to non-equilibrium systems that are studied through OPTP spectroscopy, especially in cases where parameters such as magnetic field strength (B) or other experimental parameters are varied. We demonstrate the capabilities of our measurement by performing cyclotron resonance experiments in bulk silicon, where we observe B-field-dependent carrier relaxation and distinct relaxation rates for different carrier types. We use a pair of economical linear array detectors to measure 500 time points on each shot, offering an equivalent performance to camera-based detection with possibilities for higher repetition rates.

Microwave Vertebrae Strength Probe Development: Robust and Fast Phase Unwrapping Technique.

We have developed a new transmission-based, open-ended coaxial probe for assessing vertebrae strength during spinal fusion surgery. The approach exploits the fact that the probes are within the far field of each other implying that the phase varies linearly with respect to propagation distance. Determining the absolute phase is critical for recovering the associated tissue dielectric properties from which bone strength will be determined. Unfortunately, unwanted multi-path signals corrupt the signals at the lower end of the operating frequency range from which our conventional unwrapping strategy depends. Our new approach requires only three measurements within the prime frequency range and can be determined robustly with a minimum of computations. This will be vital to developing a commercial device since the signal levels will be extremely low power requiring longer than usual data acquisition times, which will be mitigated by measuring the data at only a few frequencies. Fast and efficient operation will be critical for clinical success.

Spatial and Spectral Reconstruction of Breast Lumpectomy Hyperspectral Images.

(1) Background: Hyperspectral imaging has emerged as a promising margin assessment technique for breast-conserving surgery. However, to be implicated intraoperatively, it should be both fast and capable of yielding high-quality images to provide accurate guidance and decision-making throughout the surgery. As there exists a trade-off between image quality and data acquisition time, higher resolution images come at the cost of longer acquisition times and vice versa. (2) Methods: Therefore, in this study, we introduce a deep learning spatial-spectral reconstruction framework to obtain a high-resolution hyperspectral image from a low-resolution hyperspectral image combined with a high-resolution RGB image as input. (3) Results: Using the framework, we demonstrate the ability to perform a fast data acquisition during surgery while maintaining a high image quality, even in complex scenarios where challenges arise, such as blur due to motion artifacts, dead pixels on the camera sensor, noise from the sensor's reduced sensitivity at spectral extremities, and specular reflections caused by smooth surface areas of the tissue. (4) Conclusion: This gives the opportunity to facilitate an accurate margin assessment through intraoperative hyperspectral imaging.

BLUETOOTH ENVIRONMENTAL MONITORING SYSTEM

The primary goal of this project is to construct an environmental monitoring system integrating a Bluetooth HC-06 module. Drawing upon acquired expertise, the project resulted in the development of a functional system capable of gathering sensor data, transmitting it wirelessly to a mobile device via the Bluetooth module, and displaying it in real-time. The utilization of the FreeRTOS operating system facilitated synchronous data collection, orchestrated through tasks synchronized by semaphores, thereby guaranteeing the integrity of real- time data acquisition and transmission.

A Multiple Criteria Decision-Making Method Generated by the Space Colonization Algorithm for Automated Pruning Strategies of Trees

The rise of mechanical automation in orchards has sparked research interest in developing robots capable of autonomous tree pruning operations. To achieve accurate pruning outcomes, these robots require robust perception systems that can reconstruct three-dimensional tree characteristics and execute appropriate pruning strategies. Three-dimensional modeling plays a crucial role in enabling accurate pruning outcomes. This paper introduces a specialized tree modeling approach using the space colonization algorithm (SCA) tailored for pruning. The proposed method extends SCA to operate in three-dimensional space, generating comprehensive cherry tree models. The resulting models are exported as normalized point cloud data, serving as the input dataset. Multiple criteria decision analysis is utilized to guide pruning decisions, incorporating various factors such as tree species, tree life cycle stages, and pruning strategies during real-world implementation. The pruning task is transformed into a point cloud neural network segmentation task, identifying the trunks and branches to be pruned. This approach reduces the data acquisition time and labor costs during development. Meanwhile, pruning training in a virtual environment is an application of digital twin technology, which makes it possible to combine the meta-universe with the automated pruning of fruit trees. Experimental results demonstrate superior performance compared to other pruning systems. The overall accuracy is 85%, with mean accuracy and mean Intersection over Union (IoU) values of 0.83 and 0.75. Trunks and branches are successfully segmented with class accuracies of 0.89 and 0.81, respectively, and Intersection over Union (IoU) metrics of 0.79 and 0.72. Compared to using the open-source synthetic tree dataset, this dataset yields 80% of the overall accuracy under the same conditions, which is an improvement of 6%.

Operation and performance of the MEG II detector

The MEG II experiment, located at the Paul Scherrer Institut (PSI) in Switzerland, is the successor to the MEG experiment, which completed data taking in 2013. MEG II started fully operational data taking in 2021, with the goal of improving the sensitivity of the μ+→e+γ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu ^+ \\rightarrow {\ extrm{e}}^+ \\upgamma $$\\end{document} decay down to ∼6×10-14\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 6 \ imes 10^{-14}$$\\end{document} almost an order of magnitude better than the current limit. In this paper, we describe the operation and performance of the experiment and give a new estimate of its sensitivity versus data acquisition time.

A semi-supervised framework for computational fluid dynamics prediction

Data-driven deep learning approach heavily relies on the diversity and quantity of data. Acquiring data in the computational fluid dynamics (CFD) domain is a time and computationally intensive process. This paper proposes a semi-supervised learning method called discriminative regression fitters (DRF) for aerodynamic prediction of airfoils. DRF utilizes neural networks’ memory property to dynamically divide pseudo-labeled data into easy and difficult subsets using a model of Gaussian distribution. The method classifies unlabeled data based on loss and updates the pseudo-labeled data, improving the model’s generalization capability. Experiments on airfoil regression task datasets show that DRF achieves similar or better prediction accuracy than fully supervised approaches. It reduces data acquisition time by 70%. Ablation studies and qualitative results verify the effectiveness of DRF. The surrogate model obtained from DRF is extended to airfoil optimization, demonstrating its practicality. DRF provides a promising direction for improving the regression task while reducing the reliance on large amounts of CFD data.

Design of remote acquisition system for ecological water demand information of an estuarine wetland

In order to improve data coverage and data collection efficiency, the design method for a remote collection system of ecological water demand information of an estuarine wetland is proposed. The hardware design of the remote information acquisition system is realized through the overall structure design, the Unified Modeling Language (UML), system outline design, system logic design, and system module division. The information is acquired by polling, and the acquisition frequency is adjusted according to the actual situation and user needs. In the system software design, the Scale-invariant feature transform algorithm (SIFT) is used to extract the image features of the estuarine wetland, and a support vector machine is used to cluster the collected information according to the image features to realize the remote acquisition of water demand information of the estuarine wetland. The experimental results show that the maximum data coverage of the proposed method is 95%, and the data acquisition time is less than 0.2 h, indicating that the proposed method has high information acquisition efficiency and high data coverage.

A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958).

Effect of sampling and computing times on a hysteresis vector controller applied to renewable distributed generation

Nowadays the use of renewable energies is increasing at a high rate, but there are limitations related to the low quality of the power sent to the utility. That is the reason why new control systems have been developed. Due to the difficulty of having adequate equipment to implement the different control systems [1] they have been simulated, but taking into account the data acquisition times. The paper considers the delay time in the switching status due to the data sampling and calculation of the parameters required for the control. The calculations used to obtain the phasors required to calculate the optimal switching and the switching time are shown. In order to obtain the results, the starting point is a predictive vector control, described in [2], where the calculation times have been applied for sampling frequencies of 100kHz and 200kHz, comparing the results with the case of having a infinite sampling frequency.

High-resolution non-line-of-sight imaging based on liquid crystal planar optical elements

Abstract Non-line-of-sight (NLOS) imaging aims at recovering hidden objects located beyond the traditional line of sight, with potential applications in areas such as security monitoring, search and rescue, and autonomous driving. Conventionally, NLOS imaging requires raster scanning of laser pulses and collecting the reflected photons from a relay wall. High-time-resolution detectors obtain the flight time of photons undergoing multiple scattering for image reconstruction. Expanding the scanning area while maintaining the sampling rate is an effective method to enhance the resolution of NLOS imaging, where an angle magnification system is commonly adopted. Compared to traditional optical components, planar optical elements such as liquid crystal, offer the advantages of high efficiency, lightweight, low cost, and ease of processing. By introducing liquid crystal with angle magnification capabilities into the NLOS imaging system, we successfully designed a large field-of-view high-resolution system for a wide scanning area and high-quality image reconstruction. Furthermore, in order to reduce the long data acquisition time, a sparse scanning method capitalizing on the correlation between measurement data to reduce the number of sampling points is thus proposed. Both the simulation and experiment results demonstrate a &gt;20 % reduction in data acquisition time while maintaining the exact resolution.

ULM-MbCNRT: In vivo Ultrafast Ultrasound Localization Microscopy by Combining Multi-branch CNN and Recursive Transformer.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit by localizing tiny microbubbles (MBs), thus enabling the microvascular to be rendered at sub-wavelength resolution. Nevertheless, to obtain such superior spatial resolution, it is necessary to spend tens of seconds gathering numerous ultrasound (US) frames to accumulate MB events required, resulting in ULM imaging still suffering from trade-offs between imaging quality, data acquisition time and data processing speed. In this paper, we present a new deep learning (DL) framework combining multi-branch CNN and recursive Transformer, termed as ULM-MbCNRT, that is capable of reconstructing a super-resolution image directly from a temporal mean low-resolution image generated by averaging much fewer raw US frames, i.e., implement an ultrafast ULM imaging. To evaluate the performance of ULM-MbCNRT, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-MbCNRT achieves high-quality ULM imaging with ~10-fold reduction in data acquisition time and ~130-fold reduction in computation time compared to the previous DL method (e.g., the modified sub-pixel convolutional neural network, ULM-mSPCN). For the in vivo experiments, when comparing to the ULM-mSPCN, ULM-MbCNRT allows ~37-fold reduction in data acquisition time (~0.8 s) and ~2134-fold reduction in computation time (~0.87 s) without sacrificing spatial resolution. It implies that ultrafast ULM imaging holds promise for observing rapid biological activity in vivo, potentially improving the diagnosis and monitoring of clinical conditions.

Recognising Potential Ambiguities in Measurements of Oxygen in Tissues.

Measuring oxygen (O2) in tissues has been a central theme of the International Society on Oxygen Transport to Tissue (ISOTT) since its founding 50years ago in 1973. The initial presentations by many distinguished members reflect this focus and demonstrate the importance of the contributions of the members of ISOTT. This paper considers their work and its legacy in the context of the continuing challenges of making meaningful measurements of O2 in tissue. Because many technical, physiological, and pathophysiological factors are directly or implicitly involved in obtaining any measured value of O2 in living tissues, interpretations of what the measured value represents and its biological implications need to take these factors into account. The challenges arise from two very simple but painfully true factors that make it challenging to obtain measurements of O2 in tissues in vivo that are useful for the understanding of physiological and pathophysiological processes. First, throughout the volume of functioning tissue that is assessed by any technique, there is a complex spatial heterogeneity of O2 levels. No technique can usually fully represent this complexity in a given measurement, because the heterogeneity extends from the environment in the tissue surrounding cells to variations within the cell. Therefore, the value of the output from a measurement inevitably consists of a complex, averaged summary of O2 in the tissue. Second, the levels of O2 are constantly changing in living tissues (variations occur in seconds, minutes, hours, and/or days and differ by location) at rates that are difficult to resolve for available techniques, because they occur faster than data acquisition time and/or cannot be used as frequently as needed to follow the longer-term changes. However, as demonstrated in research reported in the publications from ISOTT, studies of O2 in tissue, in spite of the potential ambiguities in the measured values, can provide very valuable insights into physiology and pathophysiology. This is most likely to occur if researchers explicitly recognise why and how their measurement does not fully portray the complexity of O2. When measurements can be repeated, the resulting change between measurements provides information about the dynamics of the physiology and pathophysiology. Assessing change in O2 levels can also provide evidence about responses to treatments. Similarly, finding evidence of hypoxia, even though it does not capture the heterogeneity and dynamics actually happening in the tissue, can still inform clinical care if the measurement is well-understood.

Pine wood nematode disease area identification based on multi-temporal multi-source remote sensing images and BIT model

Pine wood nematode (PWN) disease is a severe contagious disease in forests which endangers the health of forests, as it can rapidly kill trees. The detection of regions impacted by this disease plays a crucial role in forest management, remediation efforts and ensuring the overall ecological security of forests. With the advancements in remote sensing technology, remote sensing images offer distinct advantages such as extensive coverage, timely data acquisition and high spatial resolution, making them highly suitable for accurately and efficiently identifying PWN disease areas. This article introduces a deep learning approach for the identification of PWN disease areas using multi-temporal and multi-source remote sensing images. Specifically, a specialized model called the bitemporal image transformer (BIT) model is developed and trained using multi-temporal Beijing-2 and Beijing-3 images acquired at different stages of PWN disease. BIT is a twin network for semantic segmentation with transformer structure and its backbone utilizes the Resnet structure. The input bitemporal image can be represented by several semantic tokens and encoded using a transformer encoder. The contextual information learned from the tokens is subsequently applied back to the pixel space. Finally, transformer decoder is utilized to refine the original features. To evaluate the model’s generalization ability, the best-trained model for different regions affected by pine nematode disease is identified using a transfer learning mechanism. The experimental results demonstrate a significant enhancement in classification accuracy using the proposed method, with a noteworthy 15.23% improvement in F1-score, 9.34% in accuracy, 11.7% in precision, 12.88% in recall, compared to traditional methods. Moreover, the model exhibits impressive generalization ability across various forest regions. In summary, this research introduces a promising approach that utilizes remote sensing images and deep learning techniques as a powerful tool for precisely identifying and managing areas affected by PWN disease.

Robust Sample Information Retrieval in Dark-Field Computed Tomography with a Vibrating Talbot-Lau Interferometer.

X-ray computed tomography (CT) is a crucial tool for non-invasive medical diagnosis that uses differences in materials' attenuation coefficients to generate contrast and provide 3D information. Grating-based dark-field-contrast X-ray imaging is an innovative technique that utilizes small-angle scattering to generate additional co-registered images with additional microstructural information. While it is already possible to perform human chest dark-field radiography, it is assumed that its diagnostic value increases when performed in a tomographic setup. However, the susceptibility of Talbot-Lau interferometers to mechanical vibrations coupled with a need to minimize data acquisition times has hindered its application in clinical routines and the combination of X-ray dark-field imaging and large field-of-view (FOV) tomography in the past. In this work, we propose a processing pipeline to address this issue in a human-sized clinical dark-field CT prototype. We present the corrective measures that are applied in the employed processing and reconstruction algorithms to mitigate the effects of vibrations and deformations of the interferometer gratings. This is achieved by identifying spatially and temporally variable vibrations in air reference scans. By translating the found correlations to the sample scan, we can identify and mitigate relevant fluctuation modes for scans with arbitrary sample sizes. This approach effectively eliminates the requirement for sample-free detector area, while still distinctly separating fluctuation and sample information. As a result, samples of arbitrary dimensions can be reconstructed without being affected by vibration artifacts. To demonstrate the viability of the technique for human-scale objects, we present reconstructions of an anthropomorphic thorax phantom.