Multimodal imaging and computer assisted diagnosis for functional tumour characterisation

PlanText: Gradually Masked Guidance to Align Image Phenotypes with Trait Descriptions for Plant Disease Texts

Plant diseases are a critical driver of the global food crisis. The integration of advanced artificial intelligence technologies can substantially enhance plant disease diagnostics. However, current methods for early and complex detection remain challenging. Employing multimodal technologies, akin to medical artificial intelligence diagnostics that combine diverse data types, may offer a more effective solution. Presently, the reliance on single-modal data predominates in plant disease research, which limits the scope for early and detailed diagnosis. Consequently, developing text modality generation techniques is essential for overcoming the limitations in plant disease recognition. To this end, we propose a method for aligning plant phenotypes with trait descriptions, which diagnoses text by progressively masking disease images. First, for training and validation, we annotate 5,728 disease phenotype images with expert diagnostic text and provide annotated text and trait labels for 210,000 disease images. Then, we propose a PhenoTrait text description model, which consists of global and heterogeneous feature encoders as well as switching-attention decoders, for accurate context-aware output. Next, to generate a more phenotypically appropriate description, we adopt 3 stages of embedding image features into semantic structures, which generate characterizations that preserve trait features. Finally, our experimental results show that our model outperforms several frontier models in multiple trait descriptions, including the larger models GPT-4 and GPT-4o. Our code and dataset are available at https://plantext.samlab.cn/.

Construction and application of human neonatal DTI atlases

Atlas-based MRI analysis is one of many analytical methods and is used to investigate typical as well as abnormal neurodevelopment. It has been widely applied to the adult and pediatric populations. Successful applications of atlas-based analysis (ABA) in those cohorts have motivated the creation of a neonatal atlas and parcellation map (PM). The purpose of this review is to discuss the various neonatal diffusion tensor imaging (DTI) atlases that are available for use in ABA, examine how such atlases are constructed, review their applications, and discuss future directions in DTI. Neonatal DTI atlases are created from a template, which can be study-specific or standardized, and merged with the corresponding PM. Study-specific templates can retain higher image registration accuracy, but are usually not applicable across different studies. However, standardized templates can be used to make comparisons among various studies, but may not accurately reflect the anatomies of the study population. Methods such as volume-based template estimation are being developed to overcome these limitations. The applications for ABA, including atlas-based image quantification and atlas-based connectivity analysis, vary from quantifying neurodevelopmental progress to analyzing population differences in groups of neonates. ABA can also be applied to detect pathology related to prematurity at birth or exposure to toxic substances. Future directions for this method include research designed to increase the accuracy of the image parcellation. Methods such as multi-atlas label fusion and multi-modal analysis applied to neonatal DTI currently comprise an active field of research. Moreover, ABA can be used in high-throughput analysis to efficiently process medical images and to assess longitudinal brain changes. The overarching goal of neonatal ABA is application to the clinical setting, to assist with diagnoses, monitor disease progression and, ultimately, outcome prediction.

Clinical evaluation of semi-automatic landmark-based lesion tracking software for CT-scans

Background

To evaluate a semi-automatic landmark-based lesion tracking software enabling navigation between RECIST lesions in baseline and follow-up CT-scans.

Methods

The software automatically detects 44 stable anatomical landmarks in each thoraco/abdominal/pelvic CT-scan, sets up a patient specific coordinate-system and cross-links the coordinate-systems of consecutive CT-scans. Accuracy of the software was evaluated on 96 RECIST lesions (target- and non-target lesions) in baseline and follow-up CT-scans of 32 oncologic patients (64 CT-scans). Patients had to present at least one thoracic, one abdominal and one pelvic RECIST lesion. Three radiologists determined the deviation between lesions’ centre and the software’s navigation result in consensus.

Results

The initial mean runtime of the system to synchronize baseline and follow-up examinations was 19.4 ± 1.2 seconds, with subsequent navigation to corresponding RECIST lesions facilitating in real-time. Mean vector length of the deviations between lesions’ centre and the semi-automatic navigation result was 10.2 ± 5.1 mm without a substantial systematic error in any direction. Mean deviation in the cranio-caudal dimension was 5.4 ± 4.0 mm, in the lateral dimension 5.2 ± 3.9 mm and in the ventro-dorsal dimension 5.3 ± 4.0 mm.

Conclusion

The investigated software accurately and reliably navigates between lesions in consecutive CT-scans in real-time, potentially accelerating and facilitating cancer staging.

Reprint of "Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging"

The development of the brain is structure-specific, and the growth rate of each structure differs depending on the age of the subject. Magnetic resonance imaging (MRI) is often used to evaluate brain development because of the high spatial resolution and contrast that enable the observation of structure-specific developmental status. Currently, most clinical MRIs are evaluated qualitatively to assist in the clinical decision-making and diagnosis. The clinical MRI report usually does not provide quantitative values that can be used to monitor developmental status. Recently, the importance of image quantification to detect and evaluate mild-to-moderate anatomical abnormalities has been emphasized because these alterations are possibly related to several psychiatric disorders and learning disabilities. In the research arena, structural MRI and diffusion tensor imaging (DTI) have been widely applied to quantify brain development of the pediatric population. To interpret the values from these MR modalities, a "growth percentile chart," which describes the mean and standard deviation of the normal developmental curve for each anatomical structure, is required. Although efforts have been made to create such a growth percentile chart based on MRI and DTI, one of the greatest challenges is to standardize the anatomical boundaries of the measured anatomical structures. To avoid inter- and intra-reader variability about the anatomical boundary definition, and hence, to increase the precision of quantitative measurements, an automated structure parcellation method, customized for the neonatal and pediatric population, has been developed. This method enables quantification of multiple MR modalities using a common analytic framework. In this paper, the attempt to create an MRI- and a DTI-based growth percentile chart, followed by an application to investigate developmental abnormalities related to cerebral palsy, Williams syndrome, and Rett syndrome, have been introduced. Future directions include multimodal image analysis and personalization for clinical application.

Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging

The development of the brain is structure-specific, and the growth rate of each structure differs depending on the age of the subject. Magnetic resonance imaging (MRI) is often used to evaluate brain development because of the high spatial resolution and contrast that enable the observation of structure-specific developmental status. Currently, most clinical MRIs are evaluated qualitatively to assist in the clinical decision-making and diagnosis. The clinical MRI report usually does not provide quantitative values that can be used to monitor developmental status. Recently, the importance of image quantification to detect and evaluate mild-to-moderate anatomical abnormalities has been emphasized because these alterations are possibly related to several psychiatric disorders and learning disabilities. In the research arena, structural MRI and diffusion tensor imaging (DTI) have been widely applied to quantify brain development of the pediatric population. To interpret the values from these MR modalities, a "growth percentile chart," which describes the mean and standard deviation of the normal developmental curve for each anatomical structure, is required. Although efforts have been made to create such a growth percentile chart based on MRI and DTI, one of the greatest challenges is to standardize the anatomical boundaries of the measured anatomical structures. To avoid inter- and intra-reader variability about the anatomical boundary definition, and hence, to increase the precision of quantitative measurements, an automated structure parcellation method, customized for the neonatal and pediatric population, has been developed. This method enables quantification of multiple MR modalities using a common analytic framework. In this paper, the attempt to create an MRI- and a DTI-based growth percentile chart, followed by an application to investigate developmental abnormalities related to cerebral palsy, Williams syndrome, and Rett syndrome, have been introduced. Future directions include multimodal image analysis and personalization for clinical application.

The role of magnetic resonance imaging in oncology

Conventional diagnostic magnetic resonance imaging (MRI) techniques have focused on improving the spatial resolution and image acquisition speed (whole-body MRI) or on new contrast agents. Most advances in MRI go beyond morphologic study to obtain functional and structural information in vivo about different physiological processes of tumor microenvironment, such as oxygenation levels, cellular proliferation, or tumor vascularization through MRI analysis of some characteristics: angiogenesis (perfusion MRI), metabolism (MRI spectroscopy), cellularity (diffusion-weighted MRI), lymph node function, or hypoxia [blood-oxygen-level-dependent (BOLD) MRI]. We discuss the contributions of different MRI techniques than must be integrated in oncologic patients to substantially advance tumor detection and characterization risk stratification, prognosis, predicting and monitoring response to treatment, and development of new drugs.

In vivo performance of a liposomal vascular contrast agent for CT and MR-based image guidance applications

PURPOSE

This study evaluated the in vivo performance of a liposome formulation that co-encapsulates iohexol and gadoteridol as a multimodal contrast agent for computed tomography (CT) and magnetic resonance (MR)-based image guidance applications.

MATERIALS AND METHODS

The pharmacokinetics and biodistribution studies were conducted in Balb-C mice using high performance liquid chromatography (HPLC) and inductively coupled plasma atomic emission spectrometry (ICP-AES) to detect iohexol and gadoteridol concentrations. The imaging efficacy of this liposome system was assessed in New Zealand White rabbits using a clinical CT and a clinical 1.5 Tesla MR scanner.

RESULTS

The vascular half-lives of the liposome encapsulated iohexol and gadoteridol in mice were found to be 18.4 +/- 2.4 and 18.1 +/- 5.1 h. When administered at the same dose the distribution (alpha phase) half-lives for the free contrast agents were 12.3 +/- 0.5 min (iohexol) and 7.6 +/- 0.9 min (gadoteridol); while, the elimination (beta phase) half-lives were 3.0 +/- 0.9 h for free iohexol and 3.0 +/- 1.3 h for free gadoteridol. The CT and MR signal increases were measured and correlated with the concentrations of iohexol and gadoteridol detected in plasma samples.

CONCLUSION

The long in vivo circulation lifetime and simultaneous CT and MR signal enhancement provided by this liposome system make it a promising agent for image guidance applications.