Reproducibility of automatic adipose tissue segmentation using proton density fat fraction images between 1.5 and 3.0 T magnetic resonance.

Background: Deep learning (DL)-based adipose tissue segmentation methods have shown great performance and efficacy for adipose tissue distribution analysis using magnetic resonance (MR) images, an important indicator of metabolic health and disease. The aim of this study was to evaluate the reproducibility of whole-body adipose tissue distribution analysis using proton density fat fraction (PDFF) images at different MR strengths.

Methods: A total of 24 volunteers were imaged using both 1.

Recent Advances in Indocyanine Green-Based Probes for Second Near-Infrared Fluorescence Imaging and Therapy.

Fluorescence imaging, a highly sensitive molecular imaging modality, is being increasingly integrated into clinical practice. Imaging within the second near-infrared biological window (NIR-II; 1,000 to 1,700 nm), also referred to as shortwave infrared, has received substantial attention because of its markedly reduced autofluorescence, deeper tissue penetration, and enhanced spatiotemporal resolution as compared to traditional near-infrared (NIR) imaging. Indocyanine green (ICG), a US Food and Drug Administration-approved NIR fluorophore, has long been used in clinical applications, including blood vessel angiography, vascular perfusion monitoring, and tumor detection.

In situ structural-functional synchronous dissection of dynamic neuromuscular system via an integrated multimodal wearable patch.

Neuromuscular abnormality is the leading cause of disability in adults. Understanding the complex interplay between muscle structure and function is crucial for effective treatment and rehabilitation. However, the substantial deformation of muscles during movement (up to 40%) poses challenges for accurate assessment.

In vivo imaging of glycogen in human muscle.

Probing regional glycogen metabolism in humans non-invasively has been challenging due to a lack of sensitive approaches. Here we studied human muscle glycogen dynamics post-exercise with a spatial resolution of millimeters and temporal resolution of minutes, using relayed nuclear Overhauser effect (glycoNOE) MRI. Data at 5T showed a homogeneous distribution of glycogen in resting muscle, with an average concentration of 99 ± 13 mM.

On the origin of MTF reduction in grating-based x-ray differential phase contrast CT imaging.

Background: The complementary absorption contrast CT (ACT) and differential phase contrast CT (DPCT) can be generated simultaneously from an x-ray computed tomography (CT) imaging system incorporated with grating interferometer. However, it has been reported that ACT images exhibit better spatial resolution than DPCT images. By far, the primary cause of such discrepancy remains unclear.

Realization of high-end PET devices that assist conventional PET devices in improving image quality via diffusion modeling.

Purpose: This study aimed to implement high-end positron emission tomography (PET) equipment to assist conventional PET equipment in improving image quality via a distribution learning-based diffusion model.

Methods: A diffusion model was first trained on a dataset of high-quality (HQ) images acquired by a high-end PET device (uEXPLORER scanner), and the quality of the conventional PET images was later improved on the basis of this trained model built on null-space constraints. Data from 180 patients were used in this study.

Model-based CBCT scatter correction with dual-layer flat-panel detector.

Background: Recently, the popularity of dual-layer flat-panel detector (DL-FPD) based dual-energy cone-beam CT (CBCT) imaging has been increasing. However, the image quality of dual-energy CBCT remains constrained by the Compton scattered x-ray photons.

Purpose: The objective of this study is to develop a novel scatter correction method, named e-Grid, for DL-FPD based CBCT imaging.

Graph theory analysis of a human body metabolic network: A systematic and organ-specific study.

Purposes: Positron emission tomography (PET) imaging is widely used to detect focal lesions or diseases and to study metabolic abnormalities between organs. However, analyzing organ correlations alone does not fully capture the characteristics of the metabolic network. Our work proposes a graph-based analysis method for quantifying the topological properties of the network, both globally and at the nodal level, to detect systemic or single-organ metabolic abnormalities caused by diseases such as lung cancer.

High Spatiotemporal Near-Infrared II Fluorescence Lifetime Imaging for Quantitative Detection of Clinical Tumor Margins.

Accurate detection of tumor margins is essential for successful cancer surgery. While indocyanine green (ICG)-based near-infrared (NIR) fluorescence (FL) surgical navigation enhances the visual identification of tumor margins, its accuracy remains subjective, underscoring the need for quantitative approaches. In this study, a high spatiotemporal fluorescence lifetime (FLT) imaging technology is developed in the second near-infrared window (NIR-II, 1000-1700 nm) for quantitative tumor margin detection, utilizing folate receptor-targeted ICG nanoprobes (FPH-ICG).

MMD-Net: Image domain multi-material decomposition network for dual-energy CT imaging.

Background: Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms.

Purpose: In this work, a novel multi-material decomposition network (MMD-Net) is proposed to improve the multi-material decomposition performance of DECT imaging.

Methods: To achieve dual-energy multi-material decomposition, a deep neural network, named as MMD-Net, is proposed in this work.

Single-point mutated lanmodulin as a high-performance MRI contrast agent for vascular and kidney imaging.

Magnetic resonance imaging contrast agents can enhance diagnostic precision but often face limitations such as short imaging windows, low tissue specificity, suboptimal contrast enhancement, or potential toxicity, which affect resolution and long-term monitoring. Here, we present a protein contrast agent based on lanmodulin, engineered with a single-point mutation at position 108 from N to D to yield maximum gadolinium binding sites. After loading with Gd ions, the resulting protein complex, LanND-Gd, exhibits efficient renal clearance, high relaxivity, and prolonged renal retention compared to commercial agents.

Bidirectional dynamic frame prediction network for total-body [Ga]Ga-PSMA-11 and [Ga]Ga-FAPI-04 PET images.

Purpose: Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations ([Formula: see text]60 minutes), potentially causing patient discomfort and resulting in artifacts in the final images. Therefore, we propose a dynamic frame prediction method for total-body PET imaging via deep learning technology to reduce the required scanning time.

Study on the impact of bowtie filter on photon-counting CT imaging.

The aim of this study was to investigate the impact of the bowtie filter on the image quality of the photon-counting detector (PCD) based CT imaging.Numerical simulations were conducted to investigate the impact of bowtie filters on image uniformity using two water phantoms, with tube potentials ranging from 60 to 140 kVp with a step of 5 kVp. Subsequently, benchtop PCD-CT imaging experiments were performed to verify the observations from the numerical simulations.

Total-Body PET/CT: A Role of Artificial Intelligence?

The purpose of this paper is to provide an overview of the cutting-edge applications of artificial intelligence (AI) technology in total-body positron emission tomography/computed tomography (PET/CT) scanning technology and its profound impact on the field of medical imaging. The introduction of total-body PET/CT scanners marked a major breakthrough in medical imaging, as their superior sensitivity and ultralong axial fields of view allowed for high-quality PET images of the entire body to be obtained in a single scan, greatly enhancing the efficiency and accuracy of diagnoses. However, this advancement is accompanied by the challenges of increasing data volumes and data complexity levels, which pose severe challenges for traditional image processing and analysis methods.

SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI.

Diffusion models have emerged as a leading methodology for image generation and have proven successful in the realm of magnetic resonance imaging (MRI) reconstruction. However, existing reconstruction methods based on diffusion models are primarily formulated in the image domain, making the reconstruction quality susceptible to inaccuracies in coil sensitivity maps (CSMs). k-space interpolation methods can effectively address this issue but conventional diffusion models are not readily applicable in k-space interpolation.

Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning.

Recently, multi-modal vision-language foundation models have gained significant attention in the medical field. While these models offer great opportunities, they still face crucial challenges, such as the requirement for fine-grained knowledge understanding in computer-aided diagnosis and the capability of utilizing very limited or even no task-specific labeled data in real-world clinical applications. In this study, we present MaCo, a masked contrastive chest X-ray foundation model that tackles these challenges.

Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement.

Recently, vision-language representation learning has made remarkable advancements in building up medical foundation models, holding immense potential for transforming the landscape of clinical research and medical care. The underlying hypothesis is that the rich knowledge embedded in radiology reports can effectively assist and guide the learning process, reducing the need for additional labels. However, these reports tend to be complex and sometimes even consist of redundant descriptions that make the representation learning too challenging to capture the key semantic information.

Habitat Imaging With Tumoral and Peritumoral Radiomics for Prediction of Lung Adenocarcinoma Invasiveness on Preoperative Chest CT: A Multicenter Study.

Tumor growth processes result in spatial heterogeneity, with the development of tumor subregions (i.e., habitats) having unique biologic characteristics.

A transcranial multiple waves suppression method for plane wave imaging based on Radon transform.

Transcranial ultrasound imaging presents a significant challenge due to the intricate interplay between ultrasound waves and the heterogeneous human skull. The skull's presence induces distortion, refraction, multiple scattering, and reflection of ultrasound signals, thereby complicating the acquisition of high-quality images. Extracting reflections from the entire waveform is crucial yet exceedingly challenging, as intracranial reflections are often obscured by strong amplitude direct waves and multiple scattering.

Correction of scatter in CBCT with a new grid design.

This study aims at developing a simple and rapid Compton scatter correction approach for cone-beam CT (CBCT) imaging.In this work, a new Compton scatter estimation model is established based on two distinct CBCT scans: one measures the full primary and scatter signals without anti-scatter grid (ASG), and the other measures a portion of primary and scatter signals with ASG. To accelerate the entire data acquisition speed, a half anti-scatter grid (h-ASG) that covers half of the full detector surface is proposed.

Generalizable Reconstruction for Accelerating MR Imaging via Federated Learning with Neural Architecture Search.

Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model. While a centralized training model is effective in mitigating this problem, it raises concerns about privacy protection. Federated learning is a distributed training paradigm that can utilize multi-institutional data for collaborative training without sharing data.