Electromagnetic Field-Assisted Frozen Tissue Planarization Enhances MALDI-MSI in Plant Spatial Omics.

Plant samples with irregular morphology are challenging for longitudinal tissue sectioning. This has restricted the ability to gain insight into some plants using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Herein, we develop a novel technique termed electromagnetic field-assisted frozen tissue planarization (EMFAFTP). This technique involves using a pair of adjustable electromagnets on both sides of a plant tissue. Under an optimized electromagnetic field strength, nondestructive planarization and regularization of the frozen tissue is induced, allowing the longitudinal tissue sectioning that favors subsequent molecular profiling by MALDI-MSI. As a proof of concept, flowers, leaves and roots with irregular morphology from six plant species are chosen to evaluate the performance of EMFAFTP for MALDI-MSI of secondary metabolites, amino acids, lipids, and proteins among others in the plant samples. The significantly enhanced MALDI-MSI capabilities of these endogenous molecules demonstrate the robustness of EMFAFTP and suggest it has the potential to become a standard technique for advancing MALDI-MSI into a new era of plant spatial omics.

High-throughput MALDI-MSI metabolite analysis of plant tissue microarrays.

A novel metabolomics analysis technique, termed matrix-assisted laser desorption/ionization mass spectrometry imaging-based plant tissue microarray (MALDI-MSI-PTMA), was successfully developed for high-throughput metabolite detection and imaging from plant tissues. This technique completely overcomes the disadvantage that metabolites cannot be accessible on an intact plant tissue due to the limitations of the special structures of plant cells (e.g. epicuticular wax, cuticle and cell wall) through homogenization of plant tissues, preparation of PTMA moulds and matrix spraying of PTMA sections. Our study shows several properties of MALDI-MSI-PTMA, including no need of sample separation and enrichment, high-throughput metabolite detection and imaging (>1000 samples per day), high-stability mass spectrometry data acquisition and imaging reconstruction and high reproducibility of data. This novel technique was successfully used to quickly evaluate the effects of two plant growth regulator treatments (i.e. 6-benzylaminopurine and N-phenyl-N'-1,2,3-thiadiazol-5-ylurea) on endogenous metabolite expression in plant tissue culture specimens of Dracocephalum rupestre Hance (D. rupestre). Intra-day and inter-day evaluations indicated that the metabolite data detected on PTMA sections had good reproducibility and stability. A total of 312 metabolite ion signals in leaves tissues of D. rupestre were detected, of which 228 metabolite ion signals were identified, they were composed of 122 primary metabolites, 90 secondary metabolites and 16 identified metabolites of unknown classification. The results demonstrated the advantages of MALDI-MSI-PTMA technique for enhancing the overall detection ability of metabolites in plant tissues, indicating that MALDI-MSI-PTMA has the potential to become a powerful routine practice for high-throughput metabolite study in plant science.

A concentration-descending washing strategy with methanol for the enhancement of protein imaging in biological tissues by MALDI-MS.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a powerful approach that has been widely used for in situ detection of various endogenous compounds in tissues. However, there are still challenges with in situ analysis of proteins using MALDI-MSI due to the ion suppression effects of small molecules in tissue sections. Therefore, tissue-washing steps are crucial for protein MALDI tissue imaging to remove these interfering molecules. Here, we successfully developed a new method named the concentration-descending washing strategy (CDWS) with methanol (MeOH), i.e., washing of biological tissue with 100%, 95%, and 70% MeOH solutions, for the enhancement of endogenous in situ protein detection and imaging in tissues using MALDI-MS. The method of MeOH-based CDWS (MeOH-CDWS) led to the successful in situ detection of 272 ± 3, 185 ± 4, and 134 ± 2 protein ion signals from rat liver, rat brain, and germinating Chinese-yew seed tissue sections, respectively. By comparison, 161 ± 2, 121 ± 1, and 114 ± 2 protein ions were detected by three commonly used methods, i.e., Carnoy's wash, ethanol (EtOH)-based CAWS (i.e., concentration-ascending washing strategy, 70% EtOH followed by 90% EtOH/9% AcOH), and isopropanol (iPrOH)-based CAWS (70% iPrOH followed by 95% iPrOH), respectively, in rat liver tissue sections, indicating that 68.9 ± 3.1%, 124.8 ± 3.3%, and 138.6 ± 4.4% more protein ion signals could be detected by the use of MeOH-CDWS than the three abovementioned washing strategies. Our results show that the use of MeOH-CDWS improves the performance of MALDI-MSI for in situ protein detection such as the number and intensity of proteins. The use of MeOH-CDWS improves the fixation of proteins and thus reduces the loss of proteins, which significantly reduces protein delocalization in tissue and enhances the performance of MALDI tissue imaging of protein. Thus, the use of MeOH-CDWS improves the quality of protein images in tissue sections through MALDI-MSI and has the potential to be used as standard practice for MALDI tissue imaging of proteins.

The long-term effects of microplastics on soil organomineral complexes and bacterial communities from controlled-release fertilizer residual coating.

Controlled-release fertilizer (CRF) was applied widely in China as an efficient utilization strategy for improving grain yield and reducing the nitrogen contamination. However, it was indeterminate to know the impacts of inevitably imported plastic into the soil on sustainable development. After ten-year fixed-site experiment, the visible residual coating microplastics were separated from the soil to measure their changes, then the long-term effects of CRF application (theoretical microplastic content 0.018-0.151 g kg-1 soil) on soil architecture and bacterial communities were evaluated. Based on soil organomineral complexes (OMC) distribution experiments and soil 16S rRNA sequence analysis, residual coating microplastics had no significant impact on soil architecture and limited effects on soil bacteria, but became the specific microbial habitat. The nitrogen rate and nitrogen release mode affected sand- and silt-grade OMC, and nitrogen rate impacted soil bacteria communities. The residual coating, small inert particles, is safe for soil OMC and bacterial communities in agricultural soil. Due to the effectiveness of CRF on reducing environmental pollution, CRF is considered as a favorable measure to the sustainable agricultural development in Shandong Province, China.

A Caffeic Acid Matrix Improves In Situ Detection and Imaging of Proteins with High Molecular Weight Close to 200,000 Da in Tissues by Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

To our knowledge, this was the first study in which caffeic acid (CA) was successfully evaluated as a matrix to enhance the in situ detection and imaging of endogenous proteins in three biological tissue sections (i.e., a rat brain and Capparis masaikai and germinating soybean seeds) by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Our results show several properties of CA, including strong ultraviolet absorption, a super-wide MS detection mass range close to 200,000 Da, micrometer-sized matrix crystals, uniform matrix deposition, and high ionization efficiency. More high-molecular-weight (HMW) protein ion signals (m/z > 30,000) could be clearly detected in biological tissues with the use of CA, compared to two commonly used MALDI matrices, i.e., sinapinic acid (SA) and ferulic acid (FA). Notably, CA shows excellent performance for HMW protein in situ detection from biological tissues in the mass range m/z > 80,000, compared to the use of SA and FA. Furthermore, the use of a CA matrix also significantly enhanced the imaging of proteins on the surface of selected biological tissue sections. Three HMW protein ion signals (m/z 50,419, m/z 65,874, and m/z 191,872) from a rat brain, two sweet proteins (mabinlin-2 and mabinlin-4) from a Capparis masaikai seed, and three HMW protein ion signals (m/z 94,838, m/z 134,204, and m/z 198,738) from a germinating soybean seed were successfully imaged for the first time. Our study proves that CA has the potential to become a standard organic acid matrix for enhanced tissue imaging of HMW proteins by MALDI-MSI in both animal and plant tissues.

Nitrogen addition does not reduce the role of spatial asynchrony in stabilising grassland communities.

While nitrogen (N) amendment is known to affect the stability of ecological communities, whether this effect is scale-dependent remains an open question. By conducting a field experiment in a temperate grassland, we found that both plant richness and temporal stability of community biomass increased with spatial scale, but N enrichment reduced richness and stability at the two scales considered. Reduced local-scale stability under N enrichment arose from N-induced reduction in population stability, which was partly attributable to the decline in local species richness, as well as reduction in asynchronous local population dynamics across species. Importantly, N enrichment did not alter spatial asynchrony among local communities, which provided similar spatial insurance effects at the larger scale, regardless of N enrichment levels. These results suggest that spatial variability among local communities, in addition to local diversity, may help stabilise ecosystems at larger spatial scales even in the face of anthropogenic environmental changes.

Sesquiterpenoids from Artemisia vestita and Their Antifeedant and Antifungal Activities.

Four new sesquiterpenoids, named artemivestinolide D-G (1-4) and three known sesquiterpenoids (5-7), were isolated from Artemisia vestita. The structures of these new compounds were determined based on extensive spectroscopic data analyses. Furthermore, the electronic circular dichroism data determined the absolute configurations of the new compounds. The antifeedant and antifungal activities of the isolates were evaluated against third-instar larvae of Plutella xylostella and three plant pathogenic fungi. Compounds 1-7 showed moderate antifeedant activities and compounds 1-4 and 6-7 exhibited antifungal activities.

Addition of T2-guided optical tomography improves noncontrast breast magnetic resonance imaging diagnosis.

BACKGROUND: While dynamic contrast-enhanced magnetic resonance imaging (DCE MRI) is recognized as the most sensitive examination for breast cancer detection, it has a substantial false positive rate and gadolinium (Gd) contrast agents are not universally well tolerated. As a result, alternatives to diagnosing breast cancer based on endogenous contrast are of growing interest. In this study, endogenous near-infrared spectral tomography (NIRST) guided by T2 MRI was evaluated to explore whether the combined imaging modality, which does not require contrast injection or involve ionizing radiation, can achieve acceptable diagnostic performance. METHODS: Twenty-four subjects-16 with pathologically confirmed malignancy and 8 with benign abnormalities-were simultaneously imaged with MRI and NIRST prior to definitive pathological diagnosis. MRIs were evaluated independently by three breast radiologists blinded to the pathological results. Optical image reconstructions were constrained by grayscale values in the T2 MRI. MRI and NIRST images were used, alone and in combination, to estimate the diagnostic performance of the data. Outcomes were compared to DCE results. RESULTS: Sensitivity, specificity, accuracy, and area under the curve (AUC) of noncontrast MRI when combined with T2-guided NIRST were 94%, 100%, 96%, and 0.95, respectively, whereas these values were 94%, 63%, 88%, and 0.81 for DCE MRI alone, and 88%, 88%, 88%, and 0.94 when DCE-guided NIRST was added. CONCLUSION: In this study, the overall accuracy of imaging diagnosis improved to 96% when T2-guided NIRST was added to noncontrast MRI alone, relative to 88% for DCE MRI, suggesting that similar or better diagnostic accuracy can be achieved without requiring a contrast agent.

Oleanane-Type Saponins from the Roots of Ligulariopsis shichuana and Their α-Glucosidase Inhibitory Activities.

Five new oleanane-type saponins, named ligushicosides A-E, and three known oleanane-type saponins were isolated from the roots of Ligulariopsis shichuana. Their structures were established by a combination of spectroscopic techniques, including 1D and 2D NMR and high resolution electrospray ionization mass spectroscopy (HR-ESI-MS). Furthermore, all isolates were evaluated for their yeast α-glucosidase inhibitory effects and exhibited potent inhibition against α-glucosidase, while compounds 1 and 2 showed excellent inhibitory activities. The 3-O-glycoside moiety in oleanane-type saponin is important for the α-glucosidase inhibitory effects.

Methane emissions from the trunks of living trees on upland soils.

Upland forests are traditionally thought to be net sinks for atmospheric methane (CH4 ). In such forests, in situ CH4 fluxes on tree trunks have been neglected relative to soil and canopy fluxes. We measured in situ CH4 fluxes from the trunks of living trees and other surfaces, such as twigs and soils, using a static closed-chamber method, and estimated the CH4 budget in a temperate upland forest in Beijing. We found that the trunks of Populus davidiana emitted large quantities of CH4 during July 2014-July 2015, amounting to mean annual emissions of 85.3 and 103.1 µg m(-2)  h(-1) on a trunk surface area basis on two replicate plots. The emission rates were similar in magnitude to those from tree trunks in wetland forests. The emitted CH4 was derived from the heartwood of trunks. On a plot or ecosystem scale, trunk CH4 emissions were equivalent to c. 30-90% of the amount of CH4 consumed by soils throughout the year, with an annual average of 63%. Our findings suggest that wet heartwoods, regardless of rot or not, occur widely in living trees on various habitats, where CH4 can be produced.

Cuticle lipids on heteromorphic leaves of Populus euphratica Oliv. growing in riparian habitats differing in available soil moisture.

Populus euphratica is an important native tree found in arid regions from North Africa and South Europe to China, and is known to tolerate many forms of environmental stress, including drought. We describe cuticle waxes, cutin and cuticle permeability for the heteromorphic leaves of P. euphratica growing in two riparian habitats that differ in available soil moisture. Scanning electron microscopy revealed variation in epicuticular wax crystallization associated with leaf type and site. P. euphratica leaves are dominated by cuticular wax alkanes, primary-alcohols and fatty acids. The major cutin monomers were 10,16-diOH C16:0 acids. Broad-ovate leaves (associated with adult phase growth) produced 1.3- and 1.6-fold more waxes, and 2.1- and 0.9-fold more cutin monomers, than lanceolate leaves (associated with juvenile phase growth) at the wetter site and drier site, respectively. The alkane-synthesis-associated ECERIFERUM1 (CER1), as well as ABC transporter- and elongase-associated genes, were expressed at much higher levels at the drier than wetter sites, indicating their potential function in elevating leaf cuticle lipids in the dry site conditions. Higher cuticle lipid amounts were closely associated with lower cuticle permeability (both chlorophyll efflux and water loss). Our results implicate cuticle lipids as among the xeromorphic traits associated with P. euphratica adult-phase broad-ovate leaves. Results here provide useful information for protecting natural populations of P. euphratica and their associated ecosystems, and shed new light on the functional interaction of cuticle and leaf heterophylly in adaptation to more arid, limited-moisture environments.

Leaf cuticular lipids on the Shandong and Yukon ecotypes of saltwater cress, Eutrema salsugineum, and their response to water deficiency and impact on cuticle permeability.

The impact of water-deficit stress on leaf cuticular waxes and cutin monomers, and traits associated with cuticle permeability were examined in Shandong and Yukon ecotypes of Eutrema salsugineum (syn. Thellungiella salsuginea). Although Shandong exhibits glaucous leaves, and Yukon is non-glaucous, wax amounts on non-stressed Yukon leaves were 4.6-fold higher than on Shandong, due mainly to Yukon's eightfold higher wax fatty acids, especially the C22 and C24 acid homologues. Water deficit caused a 26.9% increase in total waxes on Shandong leaves, due mainly to increased C22 and C24 acids; and caused 10.2% more wax on Yukon, due mainly to an increase in wax alkanes. Total cutin monomers on non-stressed leaves of Yukon were 58.3% higher than on Shandong. Water deficit caused a 28.2% increase in total cutin monomers on Shandong, whereas total cutin monomers were not induced on Yukon. With or without stress, more abundant cuticle lipids were generally associated with lower water loss rates, lower chlorophyll efflux rates and an extended time before water deficit-induced wilting. In response to water deficit, Shandong showed elevated transcription of genes encoding elongase subunits, consistent with the higher stress induction of acids by Shandong. Yukon's higher induction of CER1 and CER3 transcripts may explain why alkanes increased most on Yukon after water deficit. Eutrema, with its diverse cuticle lipids and responsiveness, provides a valuable genetic resource for identifying new genes and alleles effecting cuticle metabolism, and lays groundwork for studies of the cuticle's role in extreme stress tolerance.

Stable tissue-mimicking phantoms for longitudinal multimodality imaging studies that incorporate optical, CT, and MRI contrast.

Significance: Tissue phantoms that mimic the optical and radiologic properties of human or animal tissue play an important role in the development, characterization, and evaluation of imaging systems. Phantoms that are easily produced and stable for longitudinal studies are highly desirable. Aim: A new type of long-lasting phantom was developed with commercially available materials and was assessed for fabrication ease, stability, and optical property control. Magnetic resonance imaging (MRI) and x-ray computed tomography (CT) contrast properties were also evaluated. Approach: A systematic investigation of relationships between concentrations of skin-like pigments and composite optical properties was conducted to realize optical property phantoms in the red and near-infrared (NIR) wavelength range that also offered contrast for CT and MRI. Results: Phantom fabrication time was < 1 h and did not involve any heating or cooling processes. Changes in optical properties were < 2 % over a 12-month period. Phantom optical and spectral features were similar to human soft tissue over the red to NIR wavelength ranges. Pigments used in the study also had CT and MRI contrasts for multimodality imaging studies. Conclusions: The phantoms described here mimic optical properties of soft tissue and are suitable for multimodality imaging studies involving CT or MRI without adding secondary contrast agents.

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

Significance: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy. Aim: To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model. Approach: A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments. Results: This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error ( > 94.1 % ), peak signal-to-noise ratio ( > 41.7 % ), and Pearson correlation ( > 19 % ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP. Conclusions: This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

Selfrec-Net: self-supervised deep learning approach for the reconstruction of Cherenkov-excited luminescence scanned tomography.

As an emerging imaging technique, Cherenkov-excited luminescence scanned tomography (CELST) can recover a high-resolution 3D distribution of quantum emission fields within tissue using X-ray excitation for deep penetrance. However, its reconstruction is an ill-posed and under-conditioned inverse problem because of the diffuse optical emission signal. Deep learning based image reconstruction has shown very good potential for solving these types of problems, however they suffer from a lack of ground-truth image data to confirm when used with experimental data. To overcome this, a self-supervised network cascaded by a 3D reconstruction network and the forward model, termed Selfrec-Net, was proposed to perform CELST reconstruction. Under this framework, the boundary measurements are input to the network to reconstruct the distribution of the quantum field and the predicted measurements are subsequently obtained by feeding the reconstructed result to the forward model. The network was trained by minimizing the loss between the input measurements and the predicted measurements rather than the reconstructed distributions and the corresponding ground truths. Comparative experiments were carried out on both numerical simulations and physical phantoms. For singular luminescent targets, the results demonstrate the effectiveness and robustness of the proposed network, and comparable performance can be attained to a state-of-the-art deep supervised learning algorithm, where the accuracy of the emission yield and localization of the objects was far superior to iterative reconstruction methods. Reconstruction of multiple objects is still reasonable with high localization accuracy, although with limits to the emission yield accuracy as the distribution becomes more complex. Overall though the reconstruction of Selfrec-Net provides a self-supervised way to recover the location and emission yield of molecular distributions in murine model tissues.

Insights from multi-omics integration into seed germination of Taxus chinensis var mairei.

The transition from deep dormancy to seed germination is essential for the life cycle of plants, but how this process occurs in the gymnosperm Chinese yew (Taxus chinensis var mairei), the natural source of the anticancer drug paclitaxel, remains unclear. Herein, we analyse the transcriptome, proteome, spatial metabolome, and spatial lipidome of the Chinese yew and present the multi-omics profiles of dormant and germinating seeds. Our results show that abscisic acid and gibberellic acid 12 homoeostasis is closely associated with gene transcription and protein translation, and the balance between these phytohormones thereby determines if seeds remain dormant or germinate. We find that an energy supply of carbohydrates from glycolysis and the TCA cycle feed into the pentose phosphate pathway during seed germination, and energy supplied from lipids are mainly derived from the lipolysis of triacylglycerols. Using mass spectrometry imaging, we demonstrate that the spatial distribution of plant hormones and phospholipids has a remarkable influence on embryo development. We also provide an atlas of the spatial distribution of paclitaxel C in Chinese yew seeds for the first time. The data from this study enable exploration of the germination mechanism of Chinese yew seeds across several omics levels.

Polyacrylamide gel as a new embedding medium for the enhancement of metabolite MALDI imaging.

In this study, polyacrylamide gel (PAAG) was successfully used as a new embedding medium to provide the more effective maintenance of biological tissues during the sectioning process, enhancing the tissue imaging of metabolites via matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Herein, PAAG, agarose, gelatin, optimal cutting temperature compound (OCT), and ice media were used to embed rat liver and Atlantic salmon (Salmo salar) eyeball samples. These embedded tissues were then sectioned into thin slices and thaw-mounted on conductive microscope glass slides for MALDI-MSI detection to evaluate the embedding effects. The results showed that PAAG embedding has characteristics superior to those of commonly-used embedding media (e.g., agarose, gelatin, OCT, and ice) with the advantages of one-step operation without heating, a better performance of morphology maintenance, the absence of PAAG polymer-ion-related interference below m/z 2000, and the more efficient in situ ionization of metabolites, providing a significant enhancement of both the numbers and intensities of the metabolite ion signals. Our study demonstrates the potential of PAAG embedding as a standard practice for metabolite MALDI tissue imaging, which will lead to an expanded application scope of MALDI-MSI.

De novo sequencing and analysis of root transcriptome using 454 pyrosequencing to discover putative genes associated with drought tolerance in Ammopiptanthus mongolicus.

BACKGROUND: De novo assembly of transcript sequences produced by next-generation sequencing technologies offers a rapid approach to obtain expressed gene sequences for non-model organisms. Ammopiptanthus mongolicus, a super-xerophytic broadleaf evergreen wood, is an ecologically important foundation species in desert ecosystems and exhibits substantial drought tolerance in Mid-Asia desert. Root plays an important role in water absorption of plant. There are insufficient transcriptomic and genomic data in public databases for understanding of the molecular mechanism underlying the drought tolerance of A. mongolicus. Thus, high throughput transcriptome sequencing from A. mongolicus root is helpful to generate a large amount of transcript sequences for gene discovery and molecular marker development. RESULTS: A total of 672,002 sequencing reads were obtained from a 454 GS XLR70 Titanium pyrosequencer with a mean length of 279 bp. These reads were assembled into 29,056 unique sequences including 15,173 contigs and 13,883 singlets. In our assembled sequences, 1,827 potential simple sequence repeats (SSR) molecular markers were discovered. Based on sequence similarity with known plant proteins, the assembled sequences represent approximately 9,771 proteins in PlantGDB. Based on the Gene ontology (GO) analysis, hundreds of drought stress-related genes were found. We further analyzed the gene expression profiles of 27 putative genes involved in drought tolerance using quantitative real-time PCR (qRT-PCR) assay. CONCLUSIONS: Our sequence collection represents a major transcriptomic resource for A. mongolicus, and the large number of genetic markers predicted should contribute to future research in Ammopiptanthus genus. The potential drought stress related transcripts identified in this study provide a good start for further investigation into the drought adaptation in Ammopiptanthus.

Efficient sparse reconstruction algorithm for bioluminescence tomography based on duality and variable splitting.

Bioluminescence tomography (BLT) can three-dimensionally and quantitatively resolve the molecular processes in small animals in vivo. In this paper, we propose a BLT reconstruction algorithm based on duality and variable splitting. By using duality and variable splitting to obtain a new equivalent constrained optimization problem and updating the primal variable as the Lagrangian multiplier in the dual augmented Lagrangian problem, the proposed method can obtain fast and stable source reconstruction even without the permissible source region and multispectral measurements. Numerical simulations on a mouse atlas and in vivo mouse experiments were conducted to validate the effectiveness and potential of the method.

Total variation regularization for bioluminescence tomography with the split Bregman method.

Regularization methods have been broadly applied to bioluminescence tomography (BLT) to obtain stable solutions, including l2 and l1 regularizations. However, l2 regularization can oversmooth reconstructed images and l1 regularization may sparsify the source distribution, which degrades image quality. In this paper, the use of total variation (TV) regularization in BLT is investigated. Since a nonnegativity constraint can lead to improved image quality, the nonnegative constraint should be considered in BLT. However, TV regularization with a nonnegativity constraint is extremely difficult to solve due to its nondifferentiability and nonlinearity. The aim of this work is to validate the split Bregman method to minimize the TV regularization problem with a nonnegativity constraint for BLT. The performance of split Bregman-resolved TV (SBRTV) based BLT reconstruction algorithm was verified with numerical and in vivo experiments. Experimental results demonstrate that the SBRTV regularization can provide better regularization quality over l2 and l1 regularizations.