The best of two worlds: reprojecting 2D image annotations onto 3D models.

Imagery has become one of the main data sources for investigating seascape spatial patterns. This is particularly true in deep-sea environments, which are only accessible with underwater vehicles. On the one hand, using collaborative web-based tools and machine learning algorithms, biological and geological features can now be massively annotated on 2D images with the support of experts. On the other hand, geomorphometrics such as slope or rugosity derived from 3D models built with structure from motion (sfm) methodology can then be used to answer spatial distribution questions. However, precise georeferencing of 2D annotations on 3D models has proven challenging for deep-sea images, due to a large mismatch between navigation obtained from underwater vehicles and the reprojected navigation computed in the process of building 3D models. In addition, although 3D models can be directly annotated, the process becomes challenging due to the low resolution of textures and the large size of the models. In this article, we propose a streamlined, open-access processing pipeline to reproject 2D image annotations onto 3D models using ray tracing. Using four underwater image datasets, we assessed the accuracy of annotation reprojection on 3D models and achieved successful georeferencing to centimetric accuracy. The combination of photogrammetric 3D models and accurate 2D annotations would allow the construction of a 3D representation of the landscape and could provide new insights into understanding species microdistribution and biotic interactions.

Deep Bayesian active learning-to-rank with relative annotation for estimation of ulcerative colitis severity.

Automatic image-based severity estimation is an important task in computer-aided diagnosis. Severity estimation by deep learning requires a large amount of training data to achieve a high performance. In general, severity estimation uses training data annotated with discrete (i.e., quantized) severity labels. Annotating discrete labels is often difficult in images with ambiguous severity, and the annotation cost is high. In contrast, relative annotation, in which the severity between a pair of images is compared, can avoid quantizing severity and thus makes it easier. We can estimate relative disease severity using a learning-to-rank framework with relative annotations, but relative annotation has the problem of the enormous number of pairs that can be annotated. Therefore, the selection of appropriate pairs is essential for relative annotation. In this paper, we propose a deep Bayesian active learning-to-rank that automatically selects appropriate pairs for relative annotation. Our method preferentially annotates unlabeled pairs with high learning efficiency from the model uncertainty of the samples. We prove the theoretical basis for adapting Bayesian neural networks to pairwise learning-to-rank and demonstrate the efficiency of our method through experiments on endoscopic images of ulcerative colitis on both private and public datasets. We also show that our method achieves a high performance under conditions of significant class imbalance because it automatically selects samples from the minority classes.

Metabolomics 2023 workshop report: moving toward consensus on best QA/QC practices in LC-MS-based untargeted metabolomics.

INTRODUCTION: During the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC-MS-based untargeted metabolomics. OBJECTIVES: The Best Practices Working Group disseminated recent findings from community forums and discussed aspects to include in a living guidance document. METHODS: Presentations focused on reference materials, data quality review, metabolite identification/annotation and quality assurance. RESULTS: Live polling results and follow-up discussions offered a broad international perspective on QA/QC practices. CONCLUSIONS: Community input gathered from this workshop series is being used to shape the living guidance document, a continually evolving QA/QC best practices resource for metabolomics researchers.

Dataset: Annotated soybean market news articles.

This dataset involves a collection of soybean market news through web scraping from a Brazilian website. The news articles gathered span from January 2015 to June 2023 and have undergone a labeling process to categorize them as relevant or non-relevant. The news labeling process was conducted under the guidance of an agricultural economics expert, who collaborated with a group of nine individuals. Ten parameters were considered to assist participants in the labeling process. The dataset comprises approximately 11,000 news articles and serves as a valuable resource for researchers interested in exploring trends in the soybean market. Importantly, this dataset can be utilized for tasks such as classification and natural language processing. It provides insights into labeled soybean market news and supports open science initiatives, facilitating further analysis within the research community.

Comparative genomics of Deinococcus radiodurans: unveiling genetic discrepancies between ATCC 13939K and BAA-816 strains.

The Deinococcus genus is renowned for its remarkable resilience against environmental stresses, including ionizing radiation, desiccation, and oxidative damage. This resilience is attributed to its sophisticated DNA repair mechanisms and robust defense systems, enabling it to recover from extensive damage and thrive under extreme conditions. Central to Deinococcus research, the D. radiodurans strains ATCC BAA-816 and ATCC 13939 facilitate extensive studies into this remarkably resilient genus. This study focused on delineating genetic discrepancies between these strains by sequencing our laboratory's ATCC 13939 specimen (ATCC 13939K) and juxtaposing it with ATCC BAA-816. We uncovered 436 DNA sequence differences within ATCC 13939K, including 100 single nucleotide variations, 278 insertions, and 58 deletions, which could induce frameshifts altering protein-coding genes. Gene annotation revisions accounting for gene fusions and the reconciliation of gene lengths uncovered novel protein-coding genes and refined the functional categorizations of established ones. Additionally, the analysis pointed out genome structural variations due to insertion sequence (IS) elements, underscoring the D. radiodurans genome's plasticity. Notably, ATCC 13939K exhibited a loss of six ISDra2 elements relative to BAA-816, restoring genes fragmented by ISDra2, such as those encoding for α/ß hydrolase and serine protease, and revealing new open reading frames, including genes imperative for acetoin decomposition. This comparative genomic study offers vital insights into the metabolic capabilities and resilience strategies of D. radiodurans.

A Marginalized Zero-Inflated Negative Binomial Model for Spatial Data: Modeling COVID-19 Deaths in Georgia.

Spatial count data with an abundance of zeros arise commonly in disease mapping studies. Typically, these data are analyzed using zero-inflated models, which comprise a mixture of a point mass at zero and an ordinary count distribution, such as the Poisson or negative binomial. However, due to their mixture representation, conventional zero-inflated models are challenging to explain in practice because the parameter estimates have conditional latent-class interpretations. As an alternative, several authors have proposed marginalized zero-inflated models that simultaneously model the excess zeros and the marginal mean, leading to a parameterization that more closely aligns with ordinary count models. Motivated by a study examining predictors of COVID-19 death rates, we develop a spatiotemporal marginalized zero-inflated negative binomial model that directly models the marginal mean, thus extending marginalized zero-inflated models to the spatial setting. To capture the spatiotemporal heterogeneity in the data, we introduce region-level covariates, smooth temporal effects, and spatially correlated random effects to model both the excess zeros and the marginal mean. For estimation, we adopt a Bayesian approach that combines full-conditional Gibbs sampling and Metropolis-Hastings steps. We investigate features of the model and use the model to identify key predictors of COVID-19 deaths in the US state of Georgia during the 2021 calendar year.

Innovative construction of the first reliable catalogue of bovine circular RNAs.

The aim of this study was to compare the circular transcriptome of divergent tissues in order to understand: i) the presence of circular RNAs (circRNAs) that are not exonic circRNAs, i.e. originated from backsplicing involving known exons and, ii) the origin of artificial circRNA (artif_circRNA), i.e. circRNA not generated in-vivo. CircRNA identification is mostly an in-silico process, and the analysis of data from the BovReg project (https://www.bovreg.eu/) provided an opportunity to explore new ways to identify reliable circRNAs. By considering 117 tissue samples, we characterized 23,926 exonic circRNAs, 337 circRNAs from 273 introns (191 ciRNAs, 146 intron circles), 108 circRNAs from small non-coding genes and nearly 36.6K circRNAs classified as other_circRNAs. Furthermore, for 63 of those samples we analysed in parallel data from total-RNAseq (ribosomal RNAs depleted prior to library preparation) with paired mRNAseq (library prepared with poly(A)-selected RNAs). The high number of circRNAs detected in mRNAseq, and the significant number of novel circRNAs, mainly other_circRNAs, led us to consider all circRNAs detected in mRNAseq as artificial. This study provided evidence of 189 false entries in the list of exonic circRNAs: 103 artif_circRNAs identified by total RNAseq/mRNAseq comparison using two circRNA tools, 26 probable artif_circRNAs, and 65 identified by deep annotation analysis. Extensive benchmarking was performed (including analyses with CIRI2 and CIRCexplorer-2) and confirmed 94% of the 23,737 reliable exonic circRNAs. Moreover, this study demonstrates the effectiveness of a panel of highly expressed exonic circRNAs (5-8%) in analysing the tissue specificity of the bovine circular transcriptome.

Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database.

BACKGROUND: The use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of computer-aided diagnosis systems, often based on annotated medical radiology images. However, the lack of large annotated image datasets remains a major obstacle, as the annotation process is time-consuming and costly. This study aims to overcome this challenge by proposing an automated method for annotating a large database of medical radiology images based on their semantic similarity. RESULTS: An automated, unsupervised approach is used to create a large annotated dataset of medical radiology images originating from the Clinical Hospital Centre Rijeka, Croatia. The pipeline is built by data-mining three different types of medical data: images, DICOM metadata and narrative diagnoses. The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. CONCLUSIONS: The results indicate that fusing the embeddings of all three data sources together provides the best results for the task of unsupervised clustering of large-scale medical data and leads to the most concise clusters. Hence, this work marks the initial step towards building a much larger and more fine-grained annotated dataset of medical radiology images.

Genome- and Toxicology-Based Safety Assessment of Probiotic Akkermansia muciniphila ONE Isolated from Humans.

In this study, the genome of Akkermansia muciniphila ONE (designated AKK ONE) was sequenced, assembled, and analyzed. In addition, the safety of this strain was further evaluated by toxicological studies. The results showed that the AKK ONE genome is contained on a single chromosome with a total length of 2,817,524 bp and an average GC content of 55.48%. In total, 2411, 1131, 1168, 1745, and 1402 genes were annotated to the NR, GO, KEGG, COG, and SwissProt database, respectively. Potential resistance genes, adeF, tetW, ANT(3″)-IIa, and aadA1 were detected. AKK ONE was sensitive to ampicillin, ceftriaxone, cefotaxime, meropenem, tetracycline, and chloramphenicol and resistant to moxifloxacin. No potential virulence-related genes were detected. The PathogenFinder database analysis showed that AKK ONE was a non-potential human pathogen. This strain had good gastroenteric fluid tolerance and a weak ability to colonize the gut. No test item-related adverse effects were observed in the acute and subchronic toxicity test. AKK ONE did not display mutagenic activity either. This strain did not change the hematological and clinical biochemical parameters of mice. The weights of the organs were not affected by AKK ONE treatment. These results support that AKK ONE is safe for use as a probiotic at a dose of 8.28 × 109 CFU/kg bw/day.

Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study.

BACKGROUND: Named entity recognition (NER) is a fundamental task in natural language processing. However, it is typically preceded by named entity annotation, which poses several challenges, especially in the clinical domain. For instance, determining entity boundaries is one of the most common sources of disagreements between annotators due to questions such as whether modifiers or peripheral words should be annotated. If unresolved, these can induce inconsistency in the produced corpora, yet, on the other hand, strict guidelines or adjudication sessions can further prolong an already slow and convoluted process. OBJECTIVE: The aim of this study is to address these challenges by evaluating 2 novel annotation methodologies, lenient span and point annotation, aiming to mitigate the difficulty of precisely determining entity boundaries. METHODS: We evaluate their effects through an annotation case study on a Japanese medical case report data set. We compare annotation time, annotator agreement, and the quality of the produced labeling and assess the impact on the performance of an NER system trained on the annotated corpus. RESULTS: We saw significant improvements in the labeling process efficiency, with up to a 25% reduction in overall annotation time and even a 10% improvement in annotator agreement compared to the traditional boundary-strict approach. However, even the best-achieved NER model presented some drop in performance compared to the traditional annotation methodology. CONCLUSIONS: Our findings demonstrate a balance between annotation speed and model performance. Although disregarding boundary information affects model performance to some extent, this is counterbalanced by significant reductions in the annotator's workload and notable improvements in the speed of the annotation process. These benefits may prove valuable in various applications, offering an attractive compromise for developers and researchers.

Draft genome assemblies of the ponerine ant Odontoponera transversa and the carpenter ant Camponotus friedae (Hymenoptera: Formicidae).

OBJECTIVES: Ants are ecologically dominant insects in most terrestrial ecosystems, with more than 14,000 extant species in about 340 genera recorded to date. However, genomic resources are still scarce for most species, especially for species endemic in East or Southeast Asia, limiting the study of phylogeny, speciation and adaptation of this evolutionarily successful animal lineage. Here, we assemble and annotate the genomes of Odontoponera transversa and Camponotus friedae, two ant species with a natural distribution in China, to facilitate future study of ant evolution. DATA DESCRIPTION: We obtained a total of 16 Gb and 51 Gb PacBio HiFi data for O. transversa and C. friedae, respectively, which were assembled into the draft genomes of 339 Mb for O. transversa and 233 Mb for C. friedae. Genome assessments by multiple metrics showed good completeness and high accuracy of the two assemblies. Gene annotations assisted by RNA-seq data yielded a comparable number of protein-coding genes in the two genomes (10,892 for O. transversa and 11,296 for C. friedae), while repeat annotations revealed a remarkable difference of repeat content between these two ant species (149.4 Mb for O. transversa versus 49.7 Mb for C. friedae). Besides, complete mitochondrial genomes for the two species were assembled and annotated.

In-silico analysis of cattle blood transcriptome to identify lncRNAs and their role during bovine tuberculosis.

Long noncoding RNAs (lncRNAs) are RNA molecules with a length greater than 200 nucleotides that do not code for functional proteins. Although, genes play a vital role in immune response against a disease, it is less known that lncRNAs also contribute through gene regulation. Bovine tuberculosis is a significant zoonotic disease caused by Mycobacterium bovis (M. bovis) in cattle. Here, we report the in-silico analysis of the publicly available transcriptomic data of calves infected with M. bovis. A total of 51,812 lncRNAs were extracted across all the samples. A total of 216 genes and 260 lncRNAs were found to be differentially expressed across all the 4 conditions-infected vs uninfected at 8- and 20-week post-infection (WPI), 8 vs 20-WPI of both infected and uninfected. Gene Ontology and Functional annotation showed that 8 DEGs were annotated with immune system GOs and 2 DEGs with REACTOME immune system pathways. Co-expression analysis of DElncRNAs with DEGs revealed the involvement of lncRNAs with the genes annotated with Immune related GOs and pathways. Overall, our study sheds light on the dynamic transcriptomic changes in response to M. bovis infection, particularly highlighting the involvement of lncRNAs with immune-related genes. The identified immune pathways and gene-lncRNA interactions offer valuable insights for further research in understanding host-pathogen interactions and potential avenues for genetic improvement strategies in cattle.

AAPM WGTG51 Report 385: Addendum to the AAPM's TG-51 protocol for clinical reference dosimetry of high-energy electron beams.

An Addendum to the AAPM's TG-51 protocol for the determination of absorbed dose to water is presented for electron beams with energies between 4 MeV and 22 MeV ( 1.70 cm ≤ R 50 ≤ 8.70 cm $1.70\nobreakspace {\rm cm} \le R_{\text{50}} \le 8.70\nobreakspace {\rm cm}$ ). This updated formalism allows simplified calibration procedures, including the use of calibrated cylindrical ionization chambers in all electron beams without the use of a gradient correction. New k Q $k_{Q}$ data are provided for electron beams based on Monte Carlo simulations. Implementation guidance is provided. Components of the uncertainty budget in determining absorbed dose to water at the reference depth are discussed. Specifications for a reference-class chamber in electron beams include chamber stability, settling, ion recombination behavior, and polarity dependence. Progress in electron beam reference dosimetry is reviewed. Although this report introduces some major changes (e.g., gradient corrections are implicitly included in the electron beam quality conversion factors), they serve to simplify the calibration procedure. Results for absorbed dose per linac monitor unit are expected to be up to approximately 2 % higher using this Addendum compared to using the original TG-51 protocol.

Combination of real-time and hyphenated mass spectrometry for improved characterisation of exhaled breath biomarkers in clinical research.

Exhaled breath volatilomics is a powerful non-invasive tool for biomarker discovery in medical applications, but compound annotation is essential for pathophysiological insights and technology transfer. This study was aimed at investigating the interest of a hybrid approach combining real-time proton transfer reaction-time-of-flight mass spectrometry (PTR-TOF-MS) with comprehensive thermal desorption-two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GCxGC-TOF-MS) to enhance the analysis and characterization of VOCs in clinical research, using COVID-19 as a use case. VOC biomarker candidates were selected from clinical research using PTR-TOF-MS fingerprinting in patients with COVID-19 and matched to the Human Breathomic Database. Corresponding analytical standards were analysed using both a liquid calibration unit coupled to PTR-TOF-MS and TD-GCxGC-TOF-MS, together with confirmation on new clinical samples with TD-GCxGC-TOF-MS. From 26 potential VOC biomarkers, 23 were successfully detected with PTR-TOF-MS. All VOCs were successfully detected using TD-GCxGC-TOF-MS, providing effective separation of highly chemically related compounds, including isomers, and enabling high-confidence annotation based on two-dimensional chromatographic separation and mass spectra. Four VOCs were identified with a level 1 annotation in the clinical samples. For future applications, the combination of real-time PTR-TOF-MS and comprehensive TD-GCxGC-TOF-MS, at least on a subset of samples from a whole study, would enhance the performance of VOC annotation, offering potential advancements in biomarker discovery for clinical research.

Identification of Novel Bacterial Microproteins Encoded by Small Open Reading Frames Using a Computational Proteogenomics Workflow.

Genome annotation has historically ignored small open reading frames (smORFs), which encode a class of proteins shorter than 100 amino acids, collectively referred to as microproteins. This cutoff was established to avoid thousands of false positives due to limitations of pure genomics pipelines. Proteogenomics, a computational approach that combines genomics, transcriptomics, and proteomics, makes it possible to accurately identify these short sequences by overlaying different levels of omics evidence. In this chapter, we showcase the use of µProteInS, a bioinformatics pipeline developed for the identification of unannotated microproteins encoded by smORFs in bacteria. The workflow covers all the steps from quality control and transcriptome assembly to the scoring and post-processing of mass spectrometry data. Additionally, we provide an example on how to apply the pipeline's machine learning method to identify high-confidence spectra and pinpoint the most reliable identifications from large datasets.

Interpreting Gene Ontology Annotations Derived from Sequence Homology Methods.

The Gene Ontology (GO) project describes the functions of the gene products of organisms from all kingdoms of life in a standardized way, enabling powerful analyses of experiments involving genome-wide analysis. The scientific literature is used to convert experimental results into GO annotations that systematically classify gene products' functions. However, to address the fact that only a minor fraction of all genes has been characterized experimentally, multiple predictive methods to assign GO annotations have been developed since the inception of GO. Sequence homologies between novel genes and genes with known functions help to approximate the roles of these non-characterized genes. Here we describe the main sequence homology methods to produce annotations: pairwise comparison (BLAST), protein profile models (InterPro), and phylogenetic-based annotation (PAINT). Some of these methods can be implemented with genome analysis pipelines (BLAST and InterPro2GO), while PAINT is curated by the GO consortium.

A Chromosome-Level Genome Assembly and Annotation for the Oecanthus rufescens (Orthoptera: Oecanthidae).

Oecanthus is a genus of cricket known for its distinctive chirping and distributed across major zoogeographical regions worldwide. This study focuses on Oecanthus rufescens, and conducts a comprehensive examination of its genome through genome sequencing technologies and bioinformatic analysis. A high-quality chromosome-level genome of O. rufescens was successfully obtained, revealing significant features of its genome structure. The genome size is 877.9 Mb, comprising ten pseudo-chromosomes and 70 other sequences, with a GC content of 41.38% and an N50 value of 157,110,771 bp, indicating a high level of continuity. BUSCO assessment results demonstrate that the genome's integrity and quality are high (of which 96.8% are single-copy and 1.6% are duplicated). Comprehensive genome annotation was also performed, identifying approximately 310 Mb of repetitive sequences, accounting for 35.3% of the total genome sequence, and discovering 15,481 tRNA genes, 4,082 rRNA genes, and 1,212 other noncoding genes. Furthermore, 15,031 protein-coding genes were identified, with BUSCO assessment results showing that 98.4% (of which 96.3% are single-copy and 1.6% are duplicated) of the genes were annotated.

Unraveling genomic features and phylogenomics through the analysis of three Mexican endemic Myotis genomes.

Background: Genomic resource development for non-model organisms is rapidly progressing, seeking to uncover molecular mechanisms and evolutionary adaptations enabling thriving in diverse environments. Limited genomic data for bat species hinder insights into their evolutionary processes, particularly within the diverse Myotis genus of the Vespertilionidae family. In Mexico, 15 Myotis species exist, with three-M. vivesi, M. findleyi, and M. planiceps-being endemic and of conservation concern. Methods: We obtained samples of Myotis vivesi, M. findleyi, and M. planiceps for genomic analysis. Each of three genomic DNA was extracted, sequenced, and assembled. The scaffolding was carried out utilizing the M. yumanensis genome via a genome-referenced approach within the ntJoin program. GapCloser was employed to fill gaps. Repeat elements were characterized, and gene prediction was done via ab initio and homology methods with MAKER pipeline. Functional annotation involved InterproScan, BLASTp, and KEGG. Non-coding RNAs were annotated with INFERNAL, and tRNAscan-SE. Orthologous genes were clustered using Orthofinder, and a phylogenomic tree was reconstructed using IQ-TREE. Results: We present genome assemblies of these endemic species using Illumina NovaSeq 6000, each exceeding 2.0 Gb, with over 90% representing single-copy genes according to BUSCO analyses. Transposable elements, including LINEs and SINEs, constitute over 30% of each genome. Helitrons, consistent with Vespertilionids, were identified. Values around 20,000 genes from each of the three assemblies were derived from gene annotation and their correlation with specific functions. Comparative analysis of orthologs among eight Myotis species revealed 20,820 groups, with 4,789 being single copy orthogroups. Non-coding RNA elements were annotated. Phylogenomic tree analysis supported evolutionary chiropterans' relationships. These resources contribute significantly to understanding gene evolution, diversification patterns, and aiding conservation efforts for these endangered bat species.

Improved gradient echo magnitude- and phase-based mapping of T 2 $$ {\mathrm{T}}_2 $$ using multiple RF spoiling increments at 3T and 7T.

PURPOSE: The transverse relaxation time T 2 $$ {}_2 $$ holds significant relevance in clinical applications and research studies. Conventional T 2 $$ {}_2 $$ mapping approaches rely on spin-echo sequences, which require lengthy acquisition times and involve high radiofrequency (RF) power deposition. An alternative gradient echo (GRE) phase-based T 2 $$ {}_2 $$ mapping method, utilizing steady-state acquisitions at one small RF spoil phase increment, was recently demonstrated. Here, a modified magnitude- and phase-based T 2 $$ {}_2 $$ mapping approach is proposed, which improves T 2 $$ {\mathrm{T}}_2 $$ estimations by simultaneous fitting of T 1 $$ {\mathrm{T}}_1 $$ and signal amplitude ( A ∝ P D $$ A\propto PD $$ ) at three or more RF spoiling phase increments, instead of assuming a fixed T 1 $$ {\mathrm{T}}_1 $$ value. METHODS: The feasibility of the magnitude-phase-based method was assessed by simulations, in phantom and in vivo experiments using skipped-CAIPI three-dimensional-echo-planar imaging (3D-EPI) for rapid GRE imaging. T 2 $$ {\mathrm{T}}_2 $$ , T 1 $$ {\mathrm{T}}_1 $$ and PD estimations obtained by our method were compared to T 2 $$ {\mathrm{T}}_2 $$ of the phase-based method and T 1 $$ {\mathrm{T}}_1 $$ and PD of spoiled GRE-based multi-parameter mapping using a multi-echo version of the same sequence. RESULTS: The agreement of the proposed T 2 $$ {\mathrm{T}}_2 $$ with ground truth and reference T 2 $$ {\mathrm{T}}_2 $$ values was higher than that of phase-based T 2 $$ {\mathrm{T}}_2 $$ in simulations and in phantom data. While phase-based T 2 $$ {\mathrm{T}}_2 $$ overestimation increases with actual T 2 $$ {\mathrm{T}}_2 $$ and T 1 $$ {\mathrm{T}}_1 $$ , the proposed method is accurate over a large range of physiologically meaningful T 2 $$ {\mathrm{T}}_2 $$ and T 1 $$ {\mathrm{T}}_1 $$ values. At the same time, precision is improved. In vivo results were in line with these observations. CONCLUSION: Accurate magnitude-phase-based T 2 $$ {}_2 $$ mapping is feasible in less than 5 min scan time for 1 mm nominal isotropic whole-head coverage at 3T and 7T.

Efficient standardization of clinical T2-weighted images: Phase-conjugacy e-CAMP with projected gradient descent.

PURPOSE: To standardize T 2 $$ {}_2 $$ -weighted images from clinical Turbo Spin Echo (TSE) scans by generating corresponding T 2 $$ {}_2 $$ maps with the goal of removing scanner- and/or protocol-specific heterogeneity. METHODS: The T 2 $$ {}_2 $$ map is estimated by minimizing an objective function containing a data fidelity term in a Virtual Conjugate Coils (VCC) framework, where the signal evolution model is expressed as a linear constraint. The objective function is minimized by Projected Gradient Descent (PGD). RESULTS: The algorithm achieves accuracy comparable to methods with customized sampling schemes for accelerated T 2 $$ {}_2 $$ mapping. The results are insensitive to the tunable parameters, and the relaxed background phase prior produces better T 2 $$ {}_2 $$ maps compared to the strict real-value enforcement. It is worth noting that the algorithm works well with challenging T 2 $$ {}_2 $$ w-TSE data using typical clinical parameters. The observed normalized root mean square error ranges from 6.8% to 12.3% over grey and white matter, a clinically common level of quantitative map error. CONCLUSION: The novel methodological development creates an efficient algorithm that allows for T 2 $$ {}_2 $$ map generated from TSE data with typical clinical parameters, such as high resolution, long echo train length, and low echo spacing. Reconstruction of T 2 $$ {}_2 $$ maps from TSE data with typical clinical parameters has not been previously reported.