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Tensor‐valued diffusion encoding facilitates data analysis by q‐space trajectory imaging. By modeling the diffusion signal of heterogeneous tissues with a diffusion tensor distribution (DTD) and modulating the encoding tensor shape, this novel approach allows disentangling variations in diffusivity from microscopic anisotropy, orientation dispersion, and mixtures of multiple isotropic diffusivities. To facilitate the estimation of the DTD parameters, a parsimonious acquisition scheme coupled with an accurate and precise estimation of the DTD is needed. In this work, we create two precision‐optimized acquisition schemes: one that maximizes the precision of the raw DTD parameters, and another that maximizes the precision of the scalar measures derived from the DTD. The improved precision of these schemes compared to a naïve sampling scheme is demonstrated in both simulations and real data. Furthermore, we show that the weighted linear least squares (WLLS) estimator that uses the squared reciprocal of the noisy signal as weights can be biased, whereas the iteratively WLLS estimator with the squared reciprocal of the predicted signal as weights outperforms the conventional unweighted linear LS and nonlinear LS estimators in terms of accuracy and precision. Finally, we show that the use of appropriate constraints can considerably increase the precision of the estimator with only a limited decrease in accuracy. In this work, we propose a parsimonious acquisition scheme that maximizes the precision of the scalar metrics, which can improve the precision of the resulting scalar metrics by a factor of up to 3.75 compared to a naive acquisition scheme. In addition, we propose the use of an iteratively reweighted linear least squares estimator in combination with QTI‐specific constraints that results in high‐quality parameter maps, even when the q‐space is sparsely sampled.

Trichodesmium, a filamentous bloom-forming marine cyanobacterium, plays a key role in the biogeochemistry of oligotrophic ocean regions because of the ability to fix nitrogen. Naturally occurring in the Great Barrier Reef (GBR), the contribution of Trichodesmium to the nutrient budget may be of the same order as that entering the system via catchment runoff. However, the cyclicity of Trichodesmium in the GBR is poorly understood and sparsely documented because of the lack of sufficient observations. This study provides the first systematic analysis of Trichodesmium spatial and temporal occurrences in the GBR over the decade-long MERIS ocean color mission (2002-2012). Trichodesmium surface expressions were detected using the Maximum Chlorophyll Index (MCI) applied to MERIS satellite imagery of the GBR lagoonal waters. The MCI performed well (76%), albeit tested on a limited set of images (N = 25) coincident with field measurements. A north (Cape York) to south (Fitzroy) increase in the extent, frequency and timing of the surface expressions characterized the GBR, with surface expressions extending over several hundreds of kilometers. The two southernmost subregions Mackay and Fitzroy accounted for the most (70%) bloom events. The bloom timing of Trichodesmium varied from May in the north to November in the south, with wet season conditions less favorable to Trichodesmium aggregations. MODIS-Aqua Sea Surface Temperature (SST) datasets, wind speed and field measurements of nutrient concentrations were used in combination with MCI positive instances to assess the blooms' driving factors. Low wind speed (<6 m.s-1) and SST > 24°C were associated with the largest surface aggregations. Generalized additive models (GAM) indicated an increase in bloom occurrences over the 10-year period with seasonal bloom patterns regionally distinct. Interannual variability in SST partially (14%) explained bloom occurrences, and other drivers, such as the subregion and the nutrient budget, likely regulate Trichodesmium surface aggregations in the GBR.

MRI diffusion data suffers from significant inter- and intra-site variability, which hinders multi-site and/or longitudinal diffusion studies. This variability may arise from a range of factors, such as hardware, reconstruction algorithms and acquisition settings. To allow a reliable comparison and joint analysis of diffusion data across sites and over time, there is a clear need for robust data harmonization methods. This review article provides a comprehensive overview of diffusion data harmonization concepts and methods, and their limitations. Overall, the methods for the harmonization of multi-site diffusion images can be categorized in two main groups: diffusion parametric map harmonization (DPMH) and diffusion weighted image harmonization (DWIH). Whereas DPMH harmonizes the diffusion parametric maps (e.g., FA, MD, and MK), DWIH harmonizes the diffusion-weighted images. Defining a gold standard harmonization technique for dMRI data is still an ongoing challenge. Nevertheless, in this paper we provide two classification tools, namely a feature table and a flowchart, which aim to guide the readers in selecting an appropriate harmonization method for their study.

This paper studies the measurement requirements of spectral resolution and radiometric sensitivity to enable the quantitative determination of water constituents and benthic parameters for the majority of optically deep and optically shallow waters on Earth. The spectral and radiometric variability is investigated by simulating remote sensing reflectance (Rrs) spectra of optically deep water for twelve inland water scenarios representing typical and extreme concentration ranges of phytoplankton, colored dissolved organic matter and non-algal particles. For optically shallow waters, Rrs changes induced by variable water depth are simulated for fourteen bottom substrate types, from lakes to coastal waters and coral reefs. The required radiometric sensitivity is derived for the conditions that the spectral shape of Rrs should be resolvable with a quantization of 100 levels and that measurable reflection differences at at least one wavelength must occur at concentration changes in water constituents of 10% and depth differences of 20 cm. These simulations are also used to derive the optimal spectral resolution and the most sensitive wavelengths. Finally, the Rrs spectra and their changes are converted to radiances and radiance differences in order to derive sensor (noise-equivalent radiance) and measurement requirements (signal-to-noise ratio) at the water surface and at the top of the atmosphere for a range of solar zenith angles.

•We propose the T2-weighted diffusion kurtosis imaging free water elimination model.•Improved conditioning of the model fitting compared to T2-independent models.•Reduced bias induced by partial volumes compared to single-compartment models.•Validated on both simulated and in vivo multi-echo diffusion data. The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue and free water signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem is ill-conditioned, necessitating the use of advanced estimation techniques that can potentially bias the parameter estimates. In this work, we propose the T2-DKI-FWE model that exploits the T2 relaxation properties of both compartments, thereby better conditioning the parameter estimation problem and providing, at the same time, an additional potential biomarker (the T2 of tissue). In our approach, the T2 of tissue is estimated as an unknown parameter, whereas the T2 of free water is assumed known a priori and fixed to a literature value (1573 ms). First, the error propagation of an erroneous assumption on the T2 of free water is studied. Next, the improved conditioning of T2-DKI-FWE compared to DKI-FWE is illustrated using the Cramér-Rao lower bound matrix. Finally, the performance of the T2-DKI-FWE model is compared to that of the DKI-FWE and T2-DKI models on both simulated and real datasets. The error due to a biased approximation of the T2 of free water was found to be relatively small in various diffusion metrics and for a broad range of erroneous assumptions on its underlying ground truth value. Compared to DKI-FWE, using the T2-DKI-FWE model is beneficial for the identifiability of the model parameters. Our results suggest that the T2-DKI-FWE model can achieve precise and accurate diffusion parameter estimates, through effective reduction of free water partial volume effects and by using a standard nonlinear least squares approach. In conclusion, incorporating T2 relaxation properties into the DKI-FWE model improves the conditioning of the model fitting, while only requiring an acquisition scheme with at least two different echo times.

Arterial spin labeling (ASL) is a promising, non-invasive perfusion magnetic resonance imaging technique for quantifying cerebral blood flow (CBF). Unfortunately, ASL suffers from an inherently low signal-to-noise ratio (SNR) and spatial resolution, undermining its potential. Increasing spatial resolution without significantly sacrificing SNR or scan time represents a critical challenge towards routine clinical use. In this work, we propose a model-based super-resolution reconstruction (SRR) method with joint motion estimation that breaks the traditional SNR/resolution/scan-time trade-off. From a set of differently oriented 2D multi-slice pseudo-continuous ASL images with a low through-plane resolution, 3D-isotropic, high resolution, quantitative CBF maps are estimated using a Bayesian approach. Experiments on both synthetic whole brain phantom data, and on in vivo brain data, show that the proposed SRR Bayesian estimation framework outperforms state-of-the-art ASL quantification. •High-resolution quantitative CBF mapping from 2D multi-slice single-PLD pCASL data.•Joint estimation of perfusion and motion parameters.•Bayesian estimation to exploit prior knowledge of the tissue and noise statistics.•Improved background suppression compared to conventional 2D multi-slice readout.•Validation using whole brain Monte Carlo simulations and in vivo brain data.

Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF). Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: ( 1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or a t least two or more bands a t 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

The structural integrity of brain white matter is commonly assessed using quantitative diffusion metric maps derived from diffusion MRI (dMRI) data. However, in multi-site, multi-scanner studies, variability across and within scanners presents challenges in ensuring consistent and comparable diffusion evaluations. This study assesses the effectiveness of ComBat-based harmonization algorithms in reducing intra- and inter-scanner variability in diffusion metrics such as FA, MD, AD, RD, MK, AK, and RK. Utilizing the B-Q MINDED dataset, which includes anatomical and dMRI data from 38 healthy adults scanned twice on two 3T MRI scanners (Siemens Healthineers PrismaFit and Siemens Healthineers Skyra) on the same day, we evaluated the NeuroCombat and LongCombat algorithms for harmonizing diffusion metrics. These harmonization methods effectively minimized both intra- and inter-scanner variability, highlighting their potential to improve consistency in multi-scanner diffusion analysis. Our findings suggest that NeuroCombat and LongCombat are recommended for harmonizing dMRI metric maps in clinical studies. Additionally, both algorithms applied in either ROI-based or voxel-wise configurations, significantly reduced variability, achieving levels comparable to scan-rescan variability intra-scanner. Nonetheless, the choice of harmonization algorithm and implementation should be tailored to the research question at hand. Moreover, the significant intra- and inter-subject variability on non-harmonized diffusion data demonstrated in this study reinforces the importance of harmonization strategies that address any sources of variability. By minimizing scanner-specific biases, the NeuroCombat and LongCombat harmonization algorithms enhance the reliability of diffusion biomarkers, enabling large-scale studies and more informed clinical decision-making in brain-related conditions.

The bio-optical properties of coral reef waters were examined across coral reef ecosystems not influenced by land-derived run-off, in the Great Barrier Reef lagoon (Heron Island) and the Coral Sea (the Coringa-Herald and Lihou Reefs). The aim was to determine whether the absorption properties, the concentration-specific absorption properties, and the phytoplankton and non-algal pigmented particle (NAP) absorption concentrations varied from the ocean waters flushing onto the reef at high tide to those waters on the reef or flushing off the reef at low tide. The optical and biogeochemical properties of on-reef waters systematically differed from the surrounding ocean waters. The chl a concentration values varied up to 7-fold and the NAP concentrations up to 29-fold; for the reef samples, the chl a values were on average 2 to 3 times lower than for the oceans whilst the NAP values were slightly higher on the reefs. The spectral absorption values of the chl a, NAP, and colored dissolved organic matter (CDOM) varied up to 6-fold for reef waters and up to 15-fold for ocean waters. The spectral absorption for chl a was up to 3-fold lower on the reef waters, the absorption by the CDOM was up to 2-fold higher and the NAP absorption was 1.6-fold higher on the reef waters. The concentration-specific absorption coefficients for chl a and NAP varied up to 9-fold in reef waters and up to 30-fold in ocean waters. In the case of Heron Island and Coringa-Herald cays, this concentration-specific absorption was on average 1.3 to 1.7-fold higher for chl a and up to 2-fold lower for NAP on the reefs. The Lihou Reef measurements were more ambiguous between the reef waters and ocean waters due to the complex nature and size of this reef. Based on our results, the assumption that the optical properties of on-reef waters and the adjacent ocean waters are the same was shown to be invalid. Ocean waters flowing on to the reef are higher in phytoplankton, whilst waters on the reef or flowing off the reefs are higher in CDOM and NAP. We found differences in the pico,- nano-, and microplankton distributions as well as in the ratios of photosynthetic to photoprotective pigments. The variability in the bio-optical properties between the reef waters and adjacent ocean waters has implications for the estimations of sunlight absorption along the water column, the UV penetration depth, the temperature distributions, and the nutrient and carbon fluxes in coral reef ecosystems. As Earth observation algorithms require proper parameterization for the water column effects when estimating benthic cover, the actual optical properties need to be used. These results will improve the use of Earth observation to systematically map the differences in the water quality between reefs and the adjacent ocean.