Single Scan Research Articles

Brain effect mechanism of lever positioning manipulation on LDH analgesia based on multimodal MRI: a study protocol

IntroductionThe clinical symptoms of Lumbar Disc Herniation (LDH) can be effectively ameliorated through Lever Positioning Manipulation (LPM), which is closely linked to the brain's pain-regulating mechanisms. Magnetic Resonance Imaging (MRI) offers an objective and visual means to study how the brain orchestrates the characteristics of analgesic effects. From the perspective of multimodal MRI, we applied functional MRI (fMRI) and Magnetic Resonance Spectrum (MRS) techniques to comprehensively evaluate the characteristics of the effects of LPM on the brain region of LDH from the aspects of brain structure, brain function and brain metabolism. This multimodal MRI technique provides a biological basis for the clinical application of LPM in LDH.Methods and analysisA total of 60 LDH patients and 30 healthy controls, matched by gender, age, and years of education, will be enrolled in this study. The LDH patients will be divided into two groups (Group 1, n = 30; Group 2, n = 30) using a random number table method. Group 1 will receive LPM treatment once every two days, for a total of 12 times over 4 weeks. Group 2 will receive sham LPM treatment during the same period as Group 1. All 30 healthy controls will be divided into Group 3. Multimodal MRI will be performed on Group 1 and Group 2 at three time points (TPs): before LPM (TP1), after one LPM session (TP2), and after a full course of LPM treatment. The healthy controls (Group 3) will not undergo LPM and will be subject to only a single multimodal MRI scan. Participants in both Group 1 and Group 2 will be required to complete clinical questionnaires. These assessments will focus on pain intensity and functional disorders, using the Visual Analog Scale (VAS) and the Japanese Orthopaedic Association (JOA) scoring systems, respectively.DiscussionThe purpose of this study is to investigate the multimodal brain response characteristics of LDH patients after treatment with LPM, with the goal of providing a biological basis for clinical applications.Trial registration numberhttps://clinicaltrials.gov/ct2/show/NCT05613179, identifier: NCT05613179.

Mirror-assisted 360° panoramic 3D measurement system based on rotary laser profilometer

Abstract The full surface digitization of real samples plays a crucial role in fields such as object recognition, industrial quality control, and reverse modeling. To achieve panoramic three-dimensional (3D) reconstruction of samples in complex scenes, we propose an advanced panoramic laser scanning system, consisting of a laser rotation scanning system and a pair of plane mirrors. With the assistance of mirrors, the projected line laser beam can illuminate the sample from full angles in a single scanning sequence, and the camera can capture the 3D information of the sample from several specific viewpoints. The scanning method of rotating allows us to achieve more efficient and larger-scale 3D scanning. After obtaining the sample’s 3D information from different viewpoints using the same calibration parameters, the sample’s full-surface 3D data in the global coordinate can be obtained using mirror reflections. We conducted precision and effectiveness tests on samples with different sizes, materials, shapes, and textures to demonstrate the ability of the proposed method to perform full-surface 3D reconstruction under different scenarios. The experimental results of accuracy evaluation demonstrate that the proposed system can achieve a measurement accuracy at the level of 100 μm. Additionally, the robust experimental results in different scenarios also indicate that the proposed panoramic laser scanning system is capable of accurately obtaining full-surface 3D data of various types of samples, providing a cost-effective, easy-to-use, efficient, high-resolution, and effective approach for achieving panoramic 3D reconstruction.

Development of a quantum cascade laser absorption spectrometer for simultaneous measurement of 13C-18O and 18O-18O clumping in CO2.

Dual clumped isotope paleothermometry determines carbonate formation temperatures by measuring the frequency of 13C-18O (∆638) and 18O-18O (∆828) pairs in carbonates. It resolves isotopic kinetic biases and thus enables more accurate paleotemperature reconstructions. However, high-precision measurements of 18O-18O clumping using current techniques requires large sample sizes and long acquisition times. We developed a mid-infrared isotope ratio laser spectrometer (IRLS) for simultaneous measurement of the isotopologue ratios ∆638 and ∆828 in gas-phase carbon dioxide (CO2) at room temperature. Our IRLS uses a single laser scanning from 2290.7 to 2291.1cm-1 and a 31 m pathlength optical cell, and it simultaneously measures the five isotopologues required for calculating ∆638 and ∆828: 16O12C16O, 16O13C16O, 16O12C18O, 16O13C18O, and 18O12C18O. In addition, our IRLS can measure 16O12C17O, enabling ∆17O analysis. At ~20°C and a CO2 pressure of ~2Torr, our IRLS system achieved precisions of 0.128‰ and 0.140‰ within 20 s for abundances of the clumped isotopologues 16O13C18O and 18O12C18O, respectively, and precisions of 0.267‰, 0.245‰, and 0.128‰ for 16O12C16O, 16O13C16O, and 16O12C18O. This yielded precisions of 0.348‰ (∆638) and 0.302‰ (∆828) within 25 s. Simulated sample-reference switching highlights the potential of our system and the need for further development. We demonstrated simultaneous measurements of ∆638 and ∆828 in CO2 to precisions of <0.35‰ within 25 s using a room-temperature, single-laser IRLS. Future developments on better resolving 16O12C16O and 16O13C16O peaks and system temperature control could further improve the measurement precision.

Clinical evaluation of 3D high-resolution isotropic knee MRI using Multi-Interleaved X-prepared TSE with inTUitive RElaxometry (MIXTURE) for simultaneous morphology and T2 mapping

PurposeQuantitative MRI techniques such as T2 mapping are useful in comprehensive evaluation of various pathologies of the knee joint yet require separate scans to conventional morphological measurements and long acquisition times. The recently introduced 3D MIXTURE (Multi-Interleaved X-prepared Turbo-Spin Echo with Intuitive Relaxometry) technique can obtain simultaneous morphologic and quantitative information of the knee joint. To compare MIXTURE with conventional methods and to identify differences in morphological and quantitative information. MethodsPhantom studies were conducted, and in vivo human scans were performed (20 patients) presented with knee arthralgia. MIXTURE is based on 3D TSE without and with T2 preparation modules in an interleaved manner for both morphology with PDW and fat suppressed T2W imaging as well as quantitative T2 mapping within one single scan. Image quality and lesion depiction were visually assessed and compared between MIXTURE and conventional 2D TSE by two experienced radiologists. Contrast-to-noise ratio was used to assess the adjacent tissue contrast in a quantitative way for both obtained PDW and fat suppressed T2W images. Quantitative T2 values were measured in phantom and from in vivo knee cartilage. ResultsThe overall diagnostic confidence and contrast-to-noise ratio were deemed comparable between MIXTURE and 2D TSE. While the chosen T2 preparation modules for MIXTURE rendered consistent T2 values comparing to the current standard, measured cartilage T2 values ranged from 26.1 to 50.7 ms, with significant difference between the lesion and normal areas (p < 0.05). ConclusionsMIXTURE can help to provide high-resolution information for both anatomical and pathological assessment.

BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI

PurposeMRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose “BrainLossNet”, a convolutional neural network (CNN)-based method for BVL-estimation.MethodsBrainLossNet uses CNN-based non-linear registration of baseline(BL)/follow-up(FU) 3D-T1w-MRI pairs. BVL is computed by non-linear registration of brain parenchyma masks segmented in the BL/FU scans. The BVL estimate is corrected for image distortions using the apparent volume change of the total intracranial volume. BrainLossNet was trained on 1525 BL/FU pairs from 83 scanners. Agreement between BrainLossNet and SIENA was assessed in 225 BL/FU pairs from 94 MS patients acquired with a single scanner and 268 BL/FU pairs from 52 scanners acquired for various indications. Robustness to short-term variability of 3D-T1w-MRI was compared in 354 BL/FU pairs from a single healthy men acquired in the same session without repositioning with 116 scanners (Frequently-Traveling-Human-Phantom dataset, FTHP).ResultsProcessing time of BrainLossNet was 2–3 min. The median [interquartile range] of the SIENA-BrainLossNet BVL difference was 0.10% [− 0.18%, 0.35%] in the MS dataset, 0.08% [− 0.14%, 0.28%] in the various indications dataset. The distribution of apparent BVL in the FTHP dataset was narrower with BrainLossNet (p = 0.036; 95th percentile: 0.20% vs 0.32%).ConclusionBrainLossNet on average provides the same BVL estimates as SIENA, but it is significantly more robust, probably due to its built-in distortion correction. Processing time of 2–3 min makes BrainLossNet suitable for clinical routine. This can pave the way for widespread clinical use of BVL estimation from intra-scanner BL/FU pairs.

An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography.

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.

Volumetric Ultrasound Imaging for the Whole Soft Tissue: Toward Enhanced Thyroid Disease Examination

Ultrasound imaging (USI) is the gold standard in the clinical diagnosis of thyroid diseases. Compared with two-dimensional (2D) USI, three-dimensional (3D) USI could provide more structural information. However, the unstable pressure generated by the hand-hold ultrasound probe scanning can cause tissue deformation, especially in soft tissues such as the thyroid. The deformation is manifested as tissue structure being compressed in 2D USI, which results in structural discontinuity in 3D USI. Furthermore, multiple scans apply pressure in different directions to the tissue, which will cause relative displacement between the 3D images obtained from multiple thyroid scans. In this work, we proposed a framework to minimize the influence of the variation of pressure in thyroid 3D USI. To correct pressure artifacts in a single scanning sequence, an adaptive method to smooth the position of the 2D ultrasound (US) image sequence is adopted before performing volumetric reconstruction. To build a whole 3D US image including both sides of the thyroid gland, an iterative closest point (ICP) based registration pipeline is adopted to eliminate the relative displacement caused by different pressure directions. Our proposed method was validated by in vivo experiments, including healthy volunteers and volunteers with thyroid nodules at different grading levels. The thyroid gland and nodule are rendered intelligently in the whole scanning region to facilitate the observation of 3D USI results by the doctor. This work might make a positive contribution to the clinical diagnosis of diseases of the thyroid or other soft tissues.

Artificial intelligence-assisted quantitative CT analysis of airway changes following SABR for central lung tumors

IntroductionUse of stereotactic ablative radiotherapy (SABR) for central lung tumors can result in up to a 35% incidence of late pulmonary toxicity. We evaluated an automated scoring method to quantify post-SABR bronchial changes by using artificial intelligence (AI)-based airway segmentation. Materials and methodsCentral lung SABR patients treated at Amsterdam UMC (AUMC, internal reference dataset) and Peter MacCallum Cancer Centre (PMCC, external validation dataset) were identified. Patients were eligible if they had pre- and post-SABR CT scans with ≤ 1 mm resolution. The first step of the automated scoring method involved AI-based airway auto-segmentation using MEDPSeg, an end-to-end deep learning-based model. The Vascular Modeling Toolkit in 3D Slicer was then used to extract a centerline curve through the auto-segmented airway lumen, and cross-sectional measurements were computed along each bronchus for all CT scans. For AUMC patients, airway stenosis/occlusion was evaluated by both visual assessment and automated scoring. Only the automated method was applied to the PMCC dataset. ResultsStudy patients comprised 26 from AUMC, and 33 from PMCC. Visual scoring identified stenosis/occlusion in 8 AUMC patients (31 %), most frequently in the segmental bronchi. After airway auto-segmentation, minor manual edits were needed in 9 % of patients. Segmentation for a single scan averaged 83sec (range 73–136). Automated scoring nearly doubled detected airway stenosis/occlusion (n = 15, 58 %), and allowed for earlier detection in 5/8 patients who had also visually scored changes. Estimated rates were 48 % and 66 % at 1- and 2-years, respectively, for the internal dataset. The automated detection rate was 52 % in the external dataset, with 1- and 2-year risks of 56 % and 61 %, respectively. ConclusionAn AI-based automated scoring method allows for detection of more bronchial stenosis/occlusion after lung SABR, and at an earlier time-point. This tool can facilitate studies to determine early airway changes and establish more reliable airway tolerance doses.

Variational model-based reconstruction techniques for multi-patch data in Magnetic Particle Imaging

Magnetic Particle Imaging is an emerging imaging modality through which it is possible to detect tracers containing superparamagnetic nanoparticles. The exposure of the particles to dynamic magnetic fields generates a non-linear response that is used to locate the particles and produce an image of their distribution. The bounding box that can be covered by a single scan curve depends on the strength of the gradients of the magnetic fields applied, which is limited due to the risk of causing peripheral nerve stimulation (PNS) in the patients. To address this issue, multiple scans are performed. The scan data must be merged together to produce reconstructions of larger regions of interest. In this paper we propose a mathematical framework which can deal with rather general multi-patching scenarios including rigid transformations of the field of view (FoV), the specimen and of the scanner. We show the flexibility of this framework in a variety of different scanning scenarios. Moreover, we describe an iterative reconstruction algorithm that yields a reconstruction of the target distribution by minimizing a convex functional which includes positivity constraints and sparsity enforcing priors. We show its convergence to a minimizer and perform numerical experiments on simulated data.

Adaptive multi-beam X-ray ptychography.

Ptychography has evolved as an important method for nanoscale X-ray imaging with synchrotron radiation. Recently, it has been proposed to work with multiple beams in parallel. The main advantage of so-called multi-beam ptychography is that larger areas can be imaged much faster than with a conventional single beam scan. We introduce adaptive multi-beam ptychography performed with two Fresnel zone plates, placed one behind the other. In contrast to previous demonstrations of multi-beam ptychography, our optical scheme allows for adapting the spatial beam separation to the needs of the sample under investigation, relaxes thickness requirements on zone plates and is straightforward to implement. Moreover, it is simple to switch between single and multi-beam illumination during the same experiment. This opens the possibility of combining large and fast overview scans with detailed imaging of certain regions of interests.

Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion for Undersampled MRI Reconstruction.

Magnetic resonance imaging is subject to slow acquisition times due to the inherent limitations in data sampling. Recently, supervised deep learning has emerged as a promising technique for reconstructing sub-sampled MRI. However, supervised deep learning requires a large dataset of fully-sampled data. Although unsupervised or self-supervised deep learning methods have emerged to address the limitations of supervised deep learning approaches, they still require a database of images. In contrast, scan-specific deep learning methods learn and reconstruct using only the sub-sampled data from a single scan. Here, we introduce Scan-Specific Self-Supervised Bayesian Deep Non-Linear Inversion (DNLINV) that does not require an auto calibration scan region. DNLINV utilizes a Deep Image Prior-type generative modeling approach and relies on approximate Bayesian inference to regularize the deep convolutional neural network. We demonstrate our approach on several anatomies, contrasts, and sampling patterns and show improved performance over existing approaches in scan-specific calibrationless parallel imaging and compressed sensing.

Reducing Metal Artifacts in X-ray Computed Tomography Based on Metal Effect Estimation and Multiple CT Volume Fusion

In recent years, X-ray Computed Tomography (CT) has been widely used for investigating industrial parts. Many industrial parts often include metals, which cause various CT artifacts such as beam hardening artifacts, and metal artifacts. These CT artifacts lower the quality of X-ray CT images, prevent precise measurements. In order to reduce metal artifacts, here we propose using multiple X-ray CT scanning. Our multiple X-ray CT scanning method includes local artifacts estimation, proposing adequate multiple scanning angles, multiple scanned data registration and multiple CT data fusion. Based on local artifacts estimation, optimal multiple scan angles for data fusion are selected. Data registration and fusion are also based on local artifacts estimation, excluding effects of artifacts during the process. In order to demonstrate our method, we measured industrial parts which includes metals and resins, proposed optimal multiple scanning angles and conducted volume fusion. As a result, the metal artifacts in the CT volume reduced significantly compared to single X-ray CT scanning. Experiments also showed that metal artifacts in different regions are reduced by additional scans, which suggest that our scan angle selection method successfully selected optimal multiple scan angles.

Machine-to-Machine Transfer Function in Deep Learning-Based Quantitative Ultrasound.

A transfer function approach was recently demonstrated to mitigate data mismatches at the acquisition level for a single ultrasound scanner in deep learning (DL)-based quantitative ultrasound (QUS). As a natural progression, we further investigate the transfer function approach and introduce a machine-to-machine (M2M) transfer function, which possesses the ability to mitigate data mismatches at a machine level. This ability opens the door to unprecedented opportunities for reducing DL model development costs, enabling the combination of data from multiple sources or scanners, or facilitating the transfer of DL models between machines. We tested the proposed method utilizing a SonixOne machine and a Verasonics machine with an L9-4 array and an L11-5 array. We conducted two types of acquisitions to obtain calibration data: stable and free-hand, using two different calibration phantoms. Without the proposed method, the mean classification accuracy when applying a model on data acquired from one system to data acquired from another system was 50%, and the mean average area under the receiver operator characteristic (ROC) curve (AUC) was 0.405. With the proposed method, mean accuracy increased to 99%, and the AUC rose to the 0.999. Additional observations include the choice of the calibration phantom led to statistically significant changes in the performance of the proposed method. Moreover, robust implementation inspired by Wiener filtering provided an effective method for transferring the domain from one machine to another machine, and it can succeed using just a single calibration view. Lastly, the proposed method proved effective when a different transducer was used in the test machine.

Numerical investigation of laser powder bed fusion of glass

Additive manufacturing of glass using laser powder bed fusion has been recently developed, demonstrating its potential to be applied in small scale applications such as flow reactors for the chemical engineering and pharmaceutical manufacturing industries. While previous research demonstrated that complex 3-dimensional shapes can be manufactured, built parts are often brittle, exhibit high porosity and lack transparency. This study employs a transient, heat transfer finite element analysis to shed light on the thermal response of laser—glass powder bed interaction and the impact of processing parameters. Through this understanding, the research seeks to identify practical strategies that can be employed to improve the quality and properties of the built parts. Bulk solid and powder soda lime silica glass properties are used as input in the model, while the laser heat flux and scan strategy, conversion of powder feedstock to bulk solid glass and heat losses from convection and radiation effects are introduced in the model through Fortran coding. The study showed that effective powder consolidation, resulting in well-defined geometrical features, is achieved for temperatures near the glass melting point. Additionally, uniform consolidation depths and widths can be achieved by increasing laser power, elevating substrate temperature and reducing scan speed within certain limits, whilst ensuring hatch spacing is below the corresponding single scan track width for unidirectional adjacent laser trajectories.

Analysis of optical coherence tomography biomarker probability detection in central serous chorioretinopathy by using an artificial intelligence-based biomarker detector

AimTo adopt a novel artificial intelligence (AI) optical coherence tomography (OCT)-based program to identify the presence of biomarkers associated with central serous chorioretinopathy (CSC) and whether these can differentiate between acute and chronic central serous chorioretinopathy (aCSC and cCSC).MethodsMulticenter, observational study with a retrospective design enrolling treatment-naïve patients with aCSC and cCSC. The diagnosis of aCSC and cCSC was established with multimodal imaging and for the current study subsequent follow-up visits were also considered. Baseline OCTs were analyzed by an AI-based platform (Discovery® OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland). This software allows to detect several different biomarkers in each single OCT scan, including subretinal fluid (SRF), intraretinal fluid (IRF), hyperreflective foci (HF) and flat irregular pigment epithelium detachment (FIPED). The presence of SRF was considered as a necessary inclusion criterion for performing biomarker analysis and OCT slabs without SRF presence were excluded from the analysis.ResultsOverall, 160 eyes of 144 patients with CSC were enrolled, out of which 100 (62.5%) eyes were diagnosed with cCSC and 60 eyes (34.5%) with aCSC. In the OCT slabs showing presence of SRF the presence of biomarkers was found to be clinically relevant (> 50%) for HF and FIPED in aCSC and cCSC. HF had an average percentage of 81% (± 20) in the cCSC group and 81% (± 15) in the aCSC group (p = 0.4295) and FIPED had a mean percentage of 88% (± 18) in cCSC vs. 89% (± 15) in the aCSC (p = 0.3197).ConclusionWe demonstrate that HF and FIPED are OCT biomarkers positively associated with CSC when present at baseline. While both HF and FIPED biomarkers could aid in CSC diagnosis, they could not distinguish between aCSC and cCSC at the first visit. AI-assisted biomarker detection shows promise for reducing invasive imaging needs, but further validation through longitudinal studies is needed.

An integrated system for measuring 3D dose distributions of carbon-ion pencil beams in regular QA practice

The accurate measurement of three-dimensional (3D) dose distribution of carbon-ion pencil beams in water is crucial for regular quality assurance (QA) practice. Although ionization chambers scanned in a water tank is conventionally used for this purpose, these measuring methods are time-consuming and labor-intensive. In addition, the beam of Heavy Ion Medical Machine (HIMM) changes continuously in each extracted duration, these methods cannot obtain the accurate 3D dose distribution. To solve these problems, an integrated system (PBDOSE) is conceived and established, which employs multi-strip ionization chambers (MSICs) to measure 2D lateral profile dose distributions. The lateral profile dose distribution in the context of various depths is measured by a mobile MSIC and normalized by the measurement of a reference MSIC to reduce the effect of beam instability. On this basis, a 3D dose distribution is created by stacking those profiles in depth direction. By conducting a single depth scan within 3 min at HIMM, information such as the 3D dose distribution, lateral contour images, the Bragg curve, beam spot size, beam position, incident direction, and energy of the beam can be obtained. The Bragg curve acquired from PBDOSE with statistical uncertainties of 1.5% was almost identical to those measured by a commercial water phantom. Besides that, the lateral profiles revealed a remarkable agreement with Gafchromic EBT3 film measurements within differences less than 0.1 mm. PBDOSE shows great prospects in the accurate and efficient measurement of 3D dose distribution for carbon-ion therapy, and the clinical application of this system will improve the efficiency of regular QA procedures to a great extent.

Generative AI for Rapid Diffusion MRI with Improved Image Quality, Reliability and Generalizability

Abstract We use generative AI to enable rapid diffusion MRI (dMRI) with high fidelity, reproducibility, and generalizability across clinical and research settings. We employ a Swin UNEt Transformers (SWIN) model, trained on Human Connectome Project (HCP) data (n=1021) and conditioned on registered T1 scans, to perform generalized dMRI denoising. We also qualitatively demonstrate super-resolution with artificially downsampled HCP data. Remarkably, SWIN can be fine-tuned for an out-of-domain dataset with a single example scan, as we demonstrate on dMRI of children with neurodevelopmental disorders (n=40), adults with acute traumatic brain injury (n=40), and adolescents with intracerebral hemorrhage due to vascular malformations undergoing resection (n = 8), each cohort scanned on different scanner models with different imaging protocols at different sites. This robustness to scan acquisition parameters, patient populations, scanner types, and sites eliminates the advantages of self-supervised methods over our fully-supervised generative AI approach. We exceed current state-of-the-art denoising methods in accuracy and test-retest reliability of rapid diffusion tensor imaging (DTI) requiring only 90 seconds of scan time. SWIN denoising also achieves dramatic improvements over the state-of-the-art for test-retest reliability of intracellular volume fraction and free water fraction measurements and can remove heavy-tail noise, improving biophysical modeling fidelity. SWIN enables rapid diffusion MRI with unprecedented accuracy and reliability, especially at high diffusion-weighting for probing biological tissues at microscopic spatial scales. The code and model are publicly available at https://github.com/ucsfncl/dmri-swin.

Intelligence and cortical morphometry: caveats in brain-behavior associations

It is well-established that brain size is associated with intelligence. But the relationship between cortical morphometric measures and intelligence is unclear. Studies have produced conflicting results or no significant relations between intelligence and cortical morphometric measures such as cortical thickness and peri-cortical contrast. This discrepancy may be due to multicollinearity amongst the independent variables in a multivariate regression analysis, or a failure to fully account for the relationship between brain size and intelligence in some other way. Our study shows that neither cortical thickness nor peri-cortical contrast reliably improves IQ prediction accuracy beyond what is achieved with brain volume alone. We show this in multiple datasets, with child data, developmental data, and with adult data; we show this with data acquired either at multiple sites, or at a single site; we show this with data acquired with different MRI scanner manufacturers, or with all data acquired on a single scanner; and we show this with fluid intelligence, full-scale IQ, performance IQ, and verbal IQ. But our point is not really even about IQ; rather we proffer a methodological caveat and potential explanation of the discrepancies in previous results, and which applies broadly.

Video Compression using Modified SPIHT with H.264

This project focuses on video compression. The need of video compression is HD quality with less size for video transmission, video conference and video calling. By using the proposed method, we can achieve better video compression. A workable low-memory solution is needed to make the SPIHT algorithm implementation possible. The sorting and refining phases are condensed into a single scan run in this updated technique

Oblique radiographic measurement of knot position and orientation in logs

A novel X-ray scanner system to identify the positions of knots in logs is described. The scanner has a simple, low-cost design that is suitable for use in medium and smaller sawmills. It makes X-ray measurements in an oblique direction as the log moves longitudinally past the X-ray source and line-detector. This unconventional oblique measurement direction creates a more side-on view that better reveals the spatial arrangement of the knots within the log. This view, when combined with the knowledge that all knots start from along the pith and radiate outwards gives sufficient information to identify knot orientations in space. Experimental oblique X-ray measurements on a sample log are described, followed by the processing and analysis of the measured radiographs, and a comparison of the results with independent measurements of knot locations. With the knot identification algorithms developed, knot axial position could be identified within 11 mm, and knot circumferential orientation with a root mean square (rms) error of 7.9°–11.6° when using a single view X-ray scanner, or 5.6°–7.7° when using a dual view scanner.