Application Of Magnetic Resonance Imaging Research Articles

Magnetic Resonance Imaging in Clinical Trials of Diabetic Kidney Disease.

Chronic kidney disease (CKD) associated with diabetes mellitus (DM) (known as diabetic kidney disease, DKD) is a serious and growing healthcare problem worldwide. In DM patients, DKD is generally diagnosed based on the presence of albuminuria and a reduced glomerular filtration rate. Diagnosis rarely includes an invasive kidney biopsy, although DKD has some characteristic histological features, and kidney fibrosis and nephron loss cause disease progression that eventually ends in kidney failure. Alternative sensitive and reliable non-invasive biomarkers are needed for DKD (and CKD in general) to improve timely diagnosis and aid disease monitoring without the need for a kidney biopsy. Such biomarkers may also serve as endpoints in clinical trials of new treatments. Non-invasive magnetic resonance imaging (MRI), particularly multiparametric MRI, may achieve these goals. In this article, we review emerging data on MRI techniques and their scientific, clinical, and economic value in DKD/CKD for diagnosis, assessment of disease pathogenesis and progression, and as potential biomarkers for clinical trial use that may also increase our understanding of the efficacy and mode(s) of action of potential DKD therapeutic interventions. We also consider how multi-site MRI studies are conducted and the challenges that should be addressed to increase wider application of MRI in DKD.

Applications of artificial intelligence in magnetic resonance imaging of primary pediatric cancers: a scoping review and CLAIM score assessment.

To review the uses of AI for magnetic resonance (MR) imaging assessment of primary pediatric cancer and identify common literature topics and knowledge gaps. To assess the adherence of the existing literature to the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines. A scoping literature search using MEDLINE, EMBASE and Cochrane databases was performed, including studies of > 10 subjects with a mean age of < 21years. Relevant data were summarized into three categories based on AI application: detection, characterization, treatment and monitoring.Readers independently scored each study using CLAIM guidelines, and inter-rater reproducibility was assessed using intraclass correlation coefficients. Twenty-one studies were included. The most common AI application for pediatric cancer MR imaging was pediatric tumor diagnosis and detection (13/21 [62%] studies). The most commonly studied tumor was posterior fossa tumors (14 [67%] studies). Knowledge gaps included a lack of research in AI-driven tumor staging (0/21 [0%] studies), imaging genomics (1/21 [5%] studies), and tumor segmentation (2/21 [10%] studies). Adherence to CLAIM guidelines was moderate in primary studies, with an average (range) of 55% (34%-73%) CLAIM items reported. Adherence has improved over time based on publication year. The literature surrounding AI applications of MR imaging in pediatric cancers is limited. The existing literature shows moderate adherence to CLAIM guidelines, suggesting that better adherence is required for future studies.

Magnetic resonance imaging and ultrasound elastography in the context of preclinical pharmacological research: significance for the 3R principles.

The 3Rs principles-reduction, refinement, replacement-are at the core of preclinical research within drug discovery, which still relies to a great extent on the availability of models of disease in animals. Minimizing their distress, reducing their number as well as searching for means to replace them in experimental studies are constant objectives in this area. Due to its non-invasive character in vivo imaging supports these efforts by enabling repeated longitudinal assessments in each animal which serves as its own control, thereby enabling to reduce considerably the animal utilization in the experiments. The repetitive monitoring of pathology progression and the effects of therapy becomes feasible by assessment of quantitative biomarkers. Moreover, imaging has translational prospects by facilitating the comparison of studies performed in small rodents and humans. Also, learnings from the clinic may be potentially back-translated to preclinical settings and therefore contribute to refining animal investigations. By concentrating on activities around the application of magnetic resonance imaging (MRI) and ultrasound elastography to small rodent models of disease, we aim to illustrate how in vivo imaging contributes primarily to reduction and refinement in the context of pharmacological research.

Retracted: Application of Scanning Magnetic Resonance Imaging in the Diagnosis of Prenatal Placental Implantation and Related Care.

[This retracts the article DOI: 10.1155/2022/4883989.].

Fluorescent and Magnetic Radical Dendrimers as Potential Bimodal Imaging Probes.

Dual or multimodal imaging probes have emerged as powerful tools that improve detection sensitivity and accuracy in disease diagnosis by imaging techniques. Two imaging techniques that are complementary and do not use ionizing radiation are magnetic resonance imaging (MRI) and optical fluorescence imaging (OFI). Herein, we prepared metal-free organic species based on dendrimers with magnetic and fluorescent properties as proof-of-concept of bimodal probes for potential MRI and OFI applications. We used oligo(styryl)benzene (OSB) dendrimers core that are fluorescent on their own, and TEMPO organic radicals anchored on their surfaces, as the magnetic component. In this way, we synthesized six radical dendrimers and characterized them by FT-IR, 1H NMR, UV-Vis, MALDI-TOF, SEC, EPR, fluorimetry, and in vitro MRI. Importantly, it was demonstrated that the new dendrimers present two properties: on one hand, they are paramagnetic and show the ability to generate contrast by MRI in vitro, and, on the other hand, they also show fluoresce emission. This is a remarkable result since it is one of the very few cases of macromolecules with bimodal magnetic and fluorescent properties using organic radicals as the magnetic probe.

Attention transformer mechanism and fusion-based deep learning architecture for MRI brain tumor classification system

Most primary brain malignancies are malignant tumors characterized by masses of abnormal tissue that grow uncontrollably. Recently, deep transfer learning (DTL) has been considered in the automated clinical application of magnetic resonance imaging (MRI) for determining brain tumor characteristics. Several previous studies have shown that tumors analyzed in MR images usually have local features, are extensive, and are associated with a high level of uncertainty. Accordingly, we introduce a two-branch parallel model that integrates the Transformer Module (TM) with the Self-Attention Unit (SAU) and Convolutional Neural Networks (CNN) to classify brain tumors in MR images. We also propose a novel approach that combines local and global features derived from CNNs and TMs to improve classification accuracy through a cross-fusion strategy. Hybrid architecture combined with cross-fusion allows parallel systems to be merged between branches, resulting in a pattern that identifies various types of tumors. Additionally, we developed a lightweight and improved CNN architecture (iResNet) that distinguishes tumor features based on MR images. From the 3064 slices in the four-class MR images from the Figshare dataset, 20 % were considered unseen images. The remainder was divided into 60 %, 20 %, and 20 % slices for training, validation, and testing. The overall structure was combined with other similar CNN networks (i.e., DenseNet, VGG, and ResNet), and we found that the accuracy of iVGG, iDensNet, and iResNet is 98.59, 98.94, and 99.30 %, respectively. Using local and global features, we developed an accurate and generalizable model to detect brain tumors in MRI, allowing rapid and accurate diagnosis.

First application of dynamic oxygen-17 magnetic resonance imaging at 7 Tesla in a patient with early subacute stroke.

Dynamic oxygen-17 (17O) magnetic resonance imaging (MRI) is an imaging method that enables a direct and non-invasive assessment of cerebral oxygen metabolism and thus potentially the distinction between viable and non-viable tissue employing a three-phase inhalation experiment. The purpose of this investigation was the first application of dynamic 17O MRI at 7 Tesla (T) in a patient with stroke. In this proof-of-concept experiment, dynamic 17O MRI was applied during 17O inhalation in a patient with early subacute stroke. The analysis of the relative 17O water (H217O) signal for the affected stroke region compared to the healthy contralateral side revealed no significant difference. However, the technical feasibility of 17O MRI has been demonstrated paving the way for future investigations in neurovascular diseases.

Conductor Losses in Radiofrequency Coils for Magnetic Resonance below 3T: Estimation Methods and Minimization Strategies

The design of optimized radiofrequency (RF) coils is a fundamental task for maximizing the signal-to-noise ratio (SNR) in Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) applications. An efficient coil should be designed by minimizing the coil noise with respect to the sample noise, since coil conductor resistance affects data quality by reducing the SNR, especially for coils tuned to a low frequency. Such conductor losses strongly depend on the frequency (due to the skin effect) and on the conductor cross-sectional shape (strip or wire). This paper reviews the different methods for estimating conductor losses in RF coils for MRI/MRS applications, comprising analytical formulations, theoretical/experimental hybrid approaches and full-wave simulations. Moreover, the different strategies for minimizing such losses, including the use of Litz wire, cooled and superconducting coils, are described. Finally, recent emerging technologies in RF coil design are briefly reviewed.

Two-photon excited luminescence of structural light enhancement in subwavelength SiO2 coating europium ion-doped paramagnetic gadolinium oxide nanoparticle and application for magnetic resonance imaging

BackgroundOxides of lanthanide rare-earth elements show great potential in the fields of imaging and therapeutics due to their unique electrical, optical and magnetic properties. Oxides of lanthanide-based nanoparticles enable high-resolution imaging of biological tissues by magnetic resonance imaging (MRI), computed tomography (CT) imaging, and fluorescence imaging. In addition, they can be used to detect, treat, and regulate diseases by fine-tuning their structure and function. It remains challenging to achieve safer, efficient, and more sensitive nanoparticles for clinical applications through the structural design of functional and nanostructured rare-earth materials.ResultIn this study, we designed a mesoporous silica-coated core–shell structure of europium oxide ions to obtain near-infrared two-photon excitation fluorescence while maintaining high contrast and resolution in MRI. We designed enhanced 800 nm photoexcitation nanostructures, which were simulated by the finite-difference method (FDM) and finite-difference time-domain method (FDTD). The nanoparticle structure, two-photon absorption, up-conversion fluorescence, magnetic properties, cytotoxicity, and MRI were investigated in vivo and in vitro. The nanoparticle has an extremely strong optical fluorescence response and multiple excitation peaks in the visible light band under the 405 nm continuous-wave laser excitation. The nanoparticle was found to possess typical optical nonlinearity induced by two-photon absorption by ultrafast laser Z-scan technique. Two-photon excited fluorescence of visible red light at wavelengths of 615 nm and 701 nm, respectively, under excitation of the more biocompatible near-infrared (pulsed laser at 800 nm). In an in vitro MRI study, a T1 relaxation rate of 6.24 mM−1 s−1 was observed. MRI in vivo showed that the nanoparticles could significantly enhance the signal intensity in liver tissue.ConclusionsThese results suggest that this sample has applied potential in visible light fluorescence imaging and MRI.

MRI-MECH: mechanics-informed MRI to estimate esophageal health.

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique that generates image sequences of the flow of a contrast material inside tissues and organs. However, its application to imaging bolus movement through the esophagus has only been demonstrated in few feasibility studies and is relatively unexplored. In this work, we present a computational framework called mechanics-informed MRI (MRI-MECH) that enhances that capability, thereby increasing the applicability of dynamic MRI for diagnosing esophageal disorders. Pineapple juice was used as the swallowed contrast material for the dynamic MRI, and the MRI image sequence was used as input to the MRI-MECH. The MRI-MECH modeled the esophagus as a flexible one-dimensional tube, and the elastic tube walls followed a linear tube law. Flow through the esophagus was governed by one-dimensional mass and momentum conservation equations. These equations were solved using a physics-informed neural network. The physics-informed neural network minimized the difference between the measurements from the MRI and model predictions and ensured that the physics of the fluid flow problem was always followed. MRI-MECH calculated the fluid velocity and pressure during esophageal transit and estimated the mechanical health of the esophagus by calculating wall stiffness and active relaxation. Additionally, MRI-MECH predicted missing information about the lower esophageal sphincter during the emptying process, demonstrating its applicability to scenarios with missing data or poor image resolution. In addition to potentially improving clinical decisions based on quantitative estimates of the mechanical health of the esophagus, MRI-MECH can also be adapted for application to other medical imaging modalities to enhance their functionality.

Experimental verification of magnetic near-field channeling using a helical-shaped wire medium lens

We experimentally verify that a magnetic uniaxial wire medium lens consisting of a racemic array of helical-shaped metallic wires may enable channeling the normal component of the magnetic field of near-field sources with resolution well below the diffraction limit over a broad bandwidth. It is experimentally demonstrated that the helical-shaped wire medium lens can be regarded as the magnetic counterpart of the usual wire medium lenses formed by straight metallic wires. The experimental results are validated with full-wave numerical simulations. We envision that the proposed metamaterial lens may have potential applications in magnetic resonance imaging, near-field wireless power transfer, and sensing.

Opportunities and challenges with hyperpolarized bioresponsive probes for functional imaging using magnetic resonance.

The development of hyperpolarized bioresponsive probes for magnetic resonance imaging (MRI) applications is an emerging and rapidly growing topic in chemistry. A wide range of hyperpolarized molecular biosensors for functional MRI have been developed in recent years. These probes comprise many different types of small-molecule reporters that can be hyperpolarized using dissolution dynamic nuclear polarization and parahydrogen-induced polarization or xenon-chelated macromolecular conjugates hyperpolarized using spin-exchange optical pumping. In this Perspective, we discuss how the amplified magnetic resonance signals of these agents are responsive to biologically relevant stimuli such as target proteins, reactive oxygen species, pH or metal ions. We examine how functional MRI using these systems allows a great number of biological processes to be monitored rapidly. Consequently, hyperpolarized bioresponsive probes may play a critical role in functional molecular imaging for observing physiology and pathology in real time.

Exploring the impact of pH on the properties of citric acid-coated iron oxide nanoparticles as high-performance T2 contrast agent for MRI applications

Iron oxide nanoparticles (IONPs) were synthesized using an aqueous co-precipitation method at various pH levels ranging from 8 to 12. The impact of solution pH on IONPs' properties was analyzed by studying their size, shape, and surface charge. Both the crystalline structure and size of IONPs were significantly affected by pH levels, as revealed by X-ray diffraction analysis. Across all pH values, magnetite was found to be the predominant phase. Interestingly, the smallest nanoparticle size was observed at pH 10. The IONPs synthesized at the optimal pH were then coated with citric acid to be used as contrast agents in Magnetic Resonance Imaging (MRI). The citric acid coating was applied to the IONPs to increase their biocompatibility and optimize their dispersity. The coated samples were further characterized using Zetasizer, Fourier-Transform Infrared Spectroscopy (FTIR), and Vibrating Sample Magnetometry (VSM). The results indicated high stability and appropriate magnetization, with a relaxivity ratio (r2/r1) of 73.75, making citric acid-coated IONPs a promising candidate for use as T2 MRI contrast agents. FTIR, Zetasizer, and VSM measurements indicated that the citric acid coating had a positive effect on the biocompatibility, stability, and magnetization of the IONPs. IONPs are excellent T2 MRI contrast agents because of their enhanced physiochemical properties, and their high relaxivity ratio confirms their effectiveness. The findings of this study emphasize the crucial role that pH plays in determining the properties of IONPs and highlight the potential use of citric acid-coated IONPs in medical imaging applications such as MRI.

Multiparametric Magnetic Resonance Investigations on Acute and Long-Term Kidney Injury.

Acute kidney injury (AKI) is a frequent complication of critical illness and carries a significant risk of short- and long-term mortality. The prediction of the progression of AKI to long-term injury has been difficult for renal disease treatment. Radiologists are keen for the early detection of transition from AKI to long-term kidney injury, which would help in the preventive measures. The lack of established methods for early detection of long-term kidney injury underscores the pressing needs of advanced imaging technology that reveals microscopic tissue alterations during the progression of AKI. Fueled by recent advances in data acquisition and post-processing methods of magnetic resonance imaging (MRI), multiparametric MRI is showing great potential as a diagnostic tool for many kidney diseases. Multiparametric MRI studies offer a precious opportunity for real-time noninvasive monitoring of pathological development and progression of AKI to long-term injury. It provides insight into renal vasculature and function (arterial spin labeling, intravoxel incoherent motion), tissue oxygenation (blood oxygen level-dependent), tissue injury and fibrosis (diffusion tensor imaging, diffusion kurtosis imaging, T1 and T2 mapping, quantitative susceptibility mapping). The multiparametric MRI approach is highly promising but the longitudinal investigation on the transition of AKI to irreversible long-term impairment is largely ignored. Further optimization and implementation of renal MR methods in clinical practice will enhance our comprehension of not only AKI but chronic kidney diseases. Novel imaging biomarkers for microscopic renal tissue alterations could be discovered and benefit the preventative interventions. This review explores recent MRI applications on acute and long-term kidney injury while addressing lingering challenges, with emphasis on the potential value of the development of multiparametric MRI for renal imaging on clinical systems. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Magnetic resonance imaging of white matter in Alzheimer's disease: a global bibliometric analysis from 1990 to 2022.

Alzheimer's disease (AD) is a common, progressive, irreversible, and fatal neurodegenerative disorder with rapidly increasing worldwide incidence. Although much research on magnetic resonance imaging (MRI) of the white matter (WM) in AD has been published, no bibliometric analysis study has investigated this issue. Thus, this study aimed to provide an overview of the current status, hotspots, and trends in MRI of WM in AD. We searched for records related to MRI studies of WM in AD from 1990 to 2022 in the Web of Science Core Collection (WOSCC) database. CiteSpace (version 5.1.R8) and VOSviewer (version 1.6.19) software were used for bibliometric analyses. A total of 2,199 articles were obtained from this study. From 1990 to 2022, the number of published articles showed exponential growth of y = 4.1374e0.1294x, with an average of 17.9 articles per year. The top country and institutions were the United States and the University of California Davis, accounting for 44.52 and 5.32% of the total studies, respectively. The most productive journal was Neurology, and the most co-cited journal was Lancet Neurology. Decarli C was the most productive author. The current research frontier trend focuses on the association between small vessel disease and AD, the clinical application and exploration of diffusion MRI, and related markers. This study provides an in-depth overview of publications on MRI of WM in AD, identifying the current research status, hotspots, and frontier trends in the field.

High-Performance T1-T2 Dual-Modal MRI Contrast Agents through Interface Engineering.

Iron oxide nanoparticles (IONPs) have been developed as contrast agents for T1- or T2-weighted magnetic resonance imaging (MRI) on account of their excellent physicochemical and biological properties. However, general strategies to improve longitudinal relaxivity (r1) often decrease transverse relaxivity (r2), thus synchronously strengthening the T1 and T2 enhancement effect of IONPs remains a challenge. Here, we report interface regulation and size tailoring of a group of FePt@Fe3O4 core-shell nanoparticles (NPs), which possess high r1 and r2 relaxivities. The increase of r1 and r2 is due to the enhancement of the saturation magnetization (Ms), which is a result of the strengthened exchange coupling across the core-shell interface. In vivo subcutaneous tumor study and brain glioma imaging revealed that FePt@Fe3O4 NPs can serve as a favorable T1-T2 dual-modal contrast agent. We envision that the core-shell NPs, through interface engineering, have great potential in preclinical and clinical MRI applications.

Semi-supervised deep neural networks for left atrial segmentation from cardiac MRI

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Left atrium size and function is an important but less studied prognostic marker for many cardiovascular diseases [1]. Cardiac Magnetic Resonance Imaging (MRI) can capture atrial structure with a high temporal and spatial resolution. However, diagnoses and accurate measurement of volumes from MRI often rely on time-consuming manual processing, or needs correction of software segmentation error by clinicians. Robust, automated methods are highly desirable to assist morphological analysis of dynamic changes in the heart. Ultimately this facilitates the wider application of cardiac MRI in early identification and prevention of potential cardiovascular disease. Purpose To introduce a semi-supervised teacher-student deep neural networks (requiring less labelled data) for accurate segmentation of left atrium from cardiac MRI images, and compare their performance with conventional supervised learning. Methods The basic structure for a mean teacher deep neural network is shown in Fig.1. Network training starts in a supervised manner. At each MRI batch input, both the student and the teacher model evaluate the images with applied noise. The student network is updated by gradient descent and segmentation loss is obtained by comparing its prediction with the label. The exponential moving average (EMA) of the student weight is then migrated to update the teacher network. Consistency loss between the student’s and teacher’s prediction (filtered by uncertainty map in Uncertainty-Aware Mean Teacher, UA-MT [2]) is obtained to jointly update the student. Unlabeled images continually improve the network without using supervised loss. We compared three semi-supervised teacher-student networks UA-MT, Hierarchical Consistency Regularized Mean Teacher (HCR-MT) and Semi-supervised Contrastive Consistency model (SCC), with the supervised network TransUNnet [3], using benchmark cardiac MRI dataset from MICCAI 2018 Atrial Segmentation Challenge [4]. Out of 20 MRI scans (1760 images), 16 scans were used for training, and 4 scans for testing. For semi-supervised learning, 75% of the training set was used as labelled data and 25% was assumed to be unlabeled. In the supervised learning, all labels were enabled. Results Example segmentation results from three semi-supervised models are shown in Fig 2. For comparison, the fully supervised TransUNnet produced an accuracy of 76%. Benefiting from effective teacher-student learning, the semi-supervised models achieved a superior accuracy of 89%, while also requiring less labelled data. Conclusion The proposed teacher-student deep neural network facilitates effective semi-supervised learning, and outperforms supervised TransUNet in segmenting the left atrium in cardiac MRI. Our semi-supervised approach uses less training data, reducing tedious and time-consuming manual labelling tasks, ultimately unlocking the ability to effectively utilise the full power of deep learning in medical applications.

Exploration and expectation of magnetic resonance imaging in the evaluation of prostate cancer

With the progress of imaging technology, magnetic resonance imaging (MRI) has become the preferred imaging method for prostate cancer due to its excellent soft tissue resolution and the capability of multiparametric and multi-planar imaging. This paper briefly describes the current application and research progress of MRI in the preoperative qualitative diagnosis, staging assessment and postoperative recurrence monitoring of prostate cancer. The purpose is to deepen the understanding of clinicians and radiologists on the value of MRI in prostate cancer, and to promote the exploration of MRI in the management of prostate cancer.

A New Breakpoint to Classify 3D Voxels in MRI: A Space Transform Strategy with 3t2FTS-v2 and Its Application for ResNet50-Based Categorization of Brain Tumors.

Three-dimensional (3D) image analyses are frequently applied to perform classification tasks. Herein, 3D-based machine learning systems are generally used/generated by examining two designs: a 3D-based deep learning model or a 3D-based task-specific framework. However, except for a new approach named 3t2FTS, a promising feature transform operating from 3D to two-dimensional (2D) space has not been efficiently investigated for classification applications in 3D magnetic resonance imaging (3D MRI). In other words, a state-of-the-art feature transform strategy is not available that achieves high accuracy and provides the adaptation of 2D-based deep learning models for 3D MRI-based classification. With this aim, this paper presents a new version of the 3t2FTS approach (3t2FTS-v2) to apply a transfer learning model for tumor categorization of 3D MRI data. For performance evaluation, the BraTS 2017/2018 dataset is handled that involves high-grade glioma (HGG) and low-grade glioma (LGG) samples in four different sequences/phases. 3t2FTS-v2 is proposed to effectively transform the features from 3D to 2D space by using two textural features: first-order statistics (FOS) and gray level run length matrix (GLRLM). In 3t2FTS-v2, normalization analyses are assessed to be different from 3t2FTS to accurately transform the space information apart from the usage of GLRLM features. The ResNet50 architecture is preferred to fulfill the HGG/LGG classification due to its remarkable performance in tumor grading. As a result, for the classification of 3D data, the proposed model achieves a 99.64% accuracy by guiding the literature about the importance of 3t2FTS-v2 that can be utilized not only for tumor grading but also for whole brain tissue-based disease classification.

Selectively Fluorinated PAMAM-Arginine Conjugates as Gene Delivery Vectors.

Polyamidoamine (PAMAM) dendrimers are among the most studied cationic polymers as non-viral gene delivery vectors. However, an "ideal" PAMAM-based gene delivery vector is still missing due to the high manufacturing costs and non-negligible cytotoxicity associated with the use of high-generation dendrimers, whereas low-generation dendrimers are far from displaying efficient gene transfection. In order to cover this gap in the literature, in this study, we propose the functionalization of the outer primary amines of PAMAM G2 and PAMAM G4 with building blocks bearing fluorinated moieties along with a guanidino functional group. We have designed and synthetized two fluorinated arginine (Arg)-based Michael acceptors which were straightforwardly "clicked" to PAMAM dendrimers without the need for coupling reagents and/or catalysts. The obtained conjugates, in particular, derivative 1 formed starting from the low-cost PAMAM G2 and a building block bearing two trifluoromethyl groups, were able to efficiently complex plasmid DNA, had negligible cytotoxicity, and showed improved gene transfection efficiency as compared to undecorated PAMAM dendrimers and a corresponding unfluorinated PAMAM-Arg derivative, with derivative 1 being two orders of magnitude more efficient than the gold standard branched polyethylenimine, bPEI, 25 kDa. These results highlight the importance of the presence of trifluoromethyl moieties for both gene transfection and a possible future application in 19F magnetic resonance imaging.