Traditional Magnetic Resonance Imaging Research Articles

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation. Approach. The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network. Main results. Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T 2 mapping and comparable results to conventional methods were obtained in the human brain. Significance. As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.

Nanoparticle-Based Activatable MRI Probes for Disease Imaging and Monitoring

Traditional diagnosis relies on identifying anatomical abnormality, which offers a stage for various anatomical imaging techniques, such as X-ray computed tomography (CT), ultrasonic imaging, and magnetic resonance imaging (MRI). The good capacity of providing anatomical details, especially for soft tissues, popularizes the clinical use of MRI. However, as the understanding of various diseases reaches the molecular level, it is gradually accepted that molecular anomaly often precedes anatomical abnormality. Therefore, molecular imaging, which is aimed at gathering various molecular information in organisms via imaging, starts to gain momentum. Unfortunately, traditional MRI is not capable of molecular imaging. As a result, there is an urgent demand for probes that enable MRI to “see” molecules. A promising design strategy for these probes is to elicit a signal change triggered by the presence of molecular targets, i.e. activation. Benefiting from the rapid development of nanotechnology, a number of nanoparticle-based activatable MRI probes have been developed for molecular imaging. This review summarizes recent advances of activatable MRI nanoprobes for imaging pathological characteristics of cancer, inflammation, and neurodegenerative diseases, with a focus on the design strategies and applications of these probes. In addition, the prospects and challenges of activatable MRI nanoprobes are also discussed.

The emerging potential of quantitative MRI biomarkers for the early prediction of brain metastasis response after stereotactic radiosurgery: a scoping review.

At present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS). We systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans. We identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (Ktrans 2), extracellular extravascular volume (ve), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (kIE ) were confirmed to be indicative of the therapeutic effect of SRS for BMs. Quantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.

Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer

BackgroundIt has been believed that traditional handcrafted radiomic features extracted from magnetic resonance imaging (MRI) of tumors are normally shallow and low-ordered. Recent advancement in deep learning technology shows that the high-order deep radiomic features extracted automatically from tumor images can capture tumor heterogeneity in a more efficient way. We hypothesize that MRI-based deep radiomic phenotypes have significant associations with molecular profiles of breast cancer tumors. We aim to identify deep radiomic features (DRFs) from MRI, evaluate their significance in predicting breast cancer (BC) clinical characteristics and explore their associations with multi-level genomic factors.MethodsA denoising autoencoder was built to retrospectively extract 4,096 DRFs from 110 BC patients’ MRI. Visualization and clustering were applied to these DRFs. Linear Mixed Effect models were used to test their associations with multi-level genomic features (GFs) (risk genes, gene signatures, and biological pathway activities) extracted from the same patients’ mRNA expression profile. A Least Absolute Shrinkage and Selection Operator model was used to identify the most predictive DRFs for each clinical characteristic (tumor size (T), lymph node metastasis (N), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status).ResultsThirty-six conventional radiomic features (CRFs) for 87 of the 110 BC patients provided by a previous study were used for comparison. More than 1,000 DRFs were associated with the risk genes, gene signatures, and biological pathways activities (adjusted P-value < 0.05). DRFs produced better performance in predicting T, N, ER, PR, and HER2 status (AUC > 0.9) using DRFs. These DRFs showed significant powers of stratifying patients, linking to relevant biological and clinical characteristics. As a contrast, only eight risk genes were associated with CRFs. The RFs performed worse in predicting clinical characteristics than DRFs.ConclusionsThe deep learning-based auto MRI features perform better in predicting BC clinical characteristics, which are more significantly associated with GFs than traditional semi-auto MRI features. Our radiogenomic approach for identifying MRI-based imaging signatures may pave potential pathways for the discovery of genetic mechanisms regulating specific tumor phenotypes and may enable a more rapid innovation of novel imaging modalities, hence accelerating their translation to personalized medicine.

Utilization of Portable Brain Magnetic Resonance Imaging in an Acute Care Setting.

Background Magnetic resonance imaging (MRI) is an important noninvasive diagnostic tool used in multiple facets of medicine, especially in the assessment of the neurological system with increasing usage over the past decades. Advancement in technology has led to the creation of portable MRI (pMRI) that was cleared for use recently. Methodology A prospectively collectedretrospectivestudy was conducted at a single institution to include patients aged >18 years, admitted to the hospital, and requiring MRI for any brain pathology. pMRI was completed using portable MRI. Traditional MRI was completed with a standard 1.5T MRI, andwhen possible, the results of the two studies were compared. Results We obtained pMRI on 20 patients, with a total of 22 scans completed. Notably, on the pMRI, we wereable to identify midline structures to determine midline shifts, identify the size of ventricles, and see large pathologies, includingischemic and hemorrhagic strokes, edema, and tumors. Patients with implants or electrodes in and around the calvarium sometimes pose challenges to image acquisition. Conclusions Portable brain MRI is a practical and useful technology that can provide immediate information about the head, especially in an acute care setting. Portable brain MRI has a lower resolution and quality of imaging compared to that of transitional MRI, and therefore, it is not a replacement for traditional MRI.

Can DKI-MRI predict recurrence and invasion of peritumoral zone of hepatocellular carcinoma after transcatheter arterial chemoembolization?

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Transcatheter arterial chemoembolization (TACE) has been performed as a palliative treatment for patients with HCC. However, HCC is easy to recur after TACE. Magnetic resonance imaging (MRI) has clinical potential in evaluating the TACE treatment effect for patients with liver cancer. However, traditional MRI has some limitations. To explore the clinical potential of diffusion kurtosis imaging (DKI) in predicting recurrence and cellular invasion of the peritumoral liver zone of HCC after TACE. Seventy-six patients with 82 HCC nodules were recruited in this study and underwent DKI after TACE. According to pathological examinations or the overall modified response evaluation criteria in solid tumors (mRECIST) criterion, 48 and 34 nodules were divided into true progression and pseudo-progression groups, respectively. The TACE-treated area, peritumoral liver zone, and far-tumoral zone were evaluated on DKI-derived metric maps. Non-parametric U test and receiver operating characteristic curve (ROC) analysis were used to evaluate the prediction performance of each DKI metric between the two groups. The independent t-test was used to compare each DKI metric between the peritumoral and far-tumoral zones of the true progression group. DKI metrics, including mean diffusivity (MD), axial diffusivity (DA), radial diffusivity (DR), axial kurtosis (KA), and anisotropy fraction of kurtosis (Fak), showed statistically different values between the true progression and pseudo-progression groups (P < 0.05). Among these, MD, DA, and DR values were higher in pseudo-progression lesions than in true progression lesions, whereas KA and FAk values were higher in true progression lesions than in pseudo-progression lesions. Moreover, for the true progression group, the peritumoral zone showed significantly different DA, DR, KA, and FAk values from the far-tumoral zone. Furthermore, MD values of the liver parenchyma (peritumoral and far-tumoral zones) were significantly lower in the true progression group than in the pseudo-progression group (P < 0.05). DKI has been demonstrated with robust performance in predicting the therapeutic response of HCC to TACE. Moreover, DKI might reveal cellular invasion of the peritumoral zone by molecular diffusion-restricted change.

Ability of MRI Diagnostic Value to Detect the Evidence of Physiotherapy Outcome Measurements in Dealing with Calf Muscles Tearing

Background: Calf muscle tearing is one of the most sudden pain that affects the calf, foot, or thigh area. It can be diagnosed through Magnetic Resonance Imaging (MRI) and treated through physiotherapy interventions. The aim of the current study is to evaluate and assess the efficiency of physiotherapy interventions using Myofascial release and Stretching exercises in calf muscle treatment, and verification this effectiveness by traditional physiotherapy measurement tools and MRI. Methods: The study included 30 patients with at least one degree tearing of the calf muscles. All patients were undergoing leg MRI imaging using a 1.5 Tesla MRI. The Visual analogue scales (VAS) and Functional disability scale for ankle (FADI) were assessed before the patients were offered therapies. Then the patients were randomly distributed into two groups: the first group was 15 patients who were given Myofascial release treatment, while the second group was 15 patients who were given treatment using Stretching exercises. After treatment, the efficacy of treatment was checked for patients by different tools: VAS, FADI, and MR imaging. Patients' outcomes before and after treatment were compared. Results: The results showed that both Myofascial release and Stretching exercises had the same effect on patients. Comparison between pre- and post-score within groups for VAS, FADI, and MRI findings. Whereas, all P value values were less than 0.05 for all samples. When comparing the techniques presented for treatment between the two groups, the results showed that there were no differences between the two techniques in each of the VAS, FADI, and MRI findings, where the P-values post-treatment were 0.644, 0.112, and 0.831, respectively. Conclusion: Myofascial release and Stretching exercises can be used to treat calf muscle tears in the sub-acute stages.

Mapping hippocampal glutamate in mesial temporal lobe epilepsy with glutamate weighted CEST (GluCEST) imaging.

Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non-lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non-sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox.

Role of magnetic resonance imaging in predicting long-term outcomes of cervical cancer treatment

Objective . To study the value of a mathematical model of lymph node (LN) metastasis according to magnetic resonance imaging (MRI) data in cervical cancer (CC) in a comparative aspect with the traditional MRI criterion of LN metastasis (the size on the short axis being ≥ 1.0 cm) for assessing the prognosis of the disease. Materials and methods . To assess the CC prognosis, the indices of one-year, five-year cancer-specific survival (CSS) rates of 100 patients were analyzed in a comparative aspect: if metastatic lymph nodes (MLNs) are detected according to MRI data based on the use of the mathematical model for the MLN diagnosis and the traditional MRI criterion. Results . The comparison of five-year CSS indices for groups of patients with a favorable prognosis (N0) using the traditional criterion and the mathematical model has revealed a statistically significant difference (р &lt; 0.001). Conclusion . The developed mathematical model of LN metastasis according to MRI data makes it possible to predict the unfavorable development of CC, as well as serves as a guide for individual therapy.

Magnetic Resonance Imaging under Image Enhancement Algorithm to Analyze the Clinical Value of Placement of Drainage Tube on Incision Healing after Hepatobiliary Surgery.

This study was aimed at exploring the application value of magnetic resonance imaging (MRI) based on image enhancement algorithm in analyzing the placement of drainage tubes in the healing of incisions after hepatobiliary surgery. A total of 70 patients with liver cancer undergoing laparoscopic hepatobiliary surgery were selected, including 34 males and 36 females. According to the detection method of postoperative recovery, they were divided into a group A (conventional MRI detection) and a group B (MRI detection based on Retinex algorithm). Patients were divided into two groups according to whether subcutaneous drainage tubes were placed: group C (no subcutaneous drainage tubes were placed) and group D (subcutaneous drainage tubes were placed), with 35 patients in each group. The results showed that there was no significant difference between group A and group B in tumor residual or recurrence. The detection rate of tumor capsule in group B was significantly higher than that in group A (P < 0.05). The sensitivity, specificity, and accuracy of group A for the detection of recurrent lesions were 63.40%, 86.90%, and 78.60%, respectively; those in group B were 82.70%, 98.50%, and 93.20%, respectively. Therefore, the difference between the two groups was statistically significant (P < 0.05). The incidence of poor wound healing and infection in group C were significantly lower than those in group D (P < 0.05). Therefore, the effect of MRI detection based on image enhancement algorithm was more conducive to the evaluation of postoperative recovery due to the traditional MRI detection. In addition, the drainage tube was helpful to the postoperative wound healing and showed high clinical value.

Contrast-agent-free state-of-the-art MRI on cerebral small vessel disease-part 1. ASL, IVIM, and CVR.

Cerebral small vessel disease (cSVD), a common cause of stroke and dementia, is traditionally considered the small vessel equivalent of large artery occlusion or rupture that leads to cortical and subcortical brain damage. Microvessel endothelial dysfunction can also contribute to it. Brain imaging, including MRI, is useful to show the presence of lesions of several types, although the association between conventional MRI measures and clinical features of cSVD is not always concordant. We assessed the additional contribution of contrast-agent-free, state-of-the-art MRI techniques such as arterial spin labeling (ASL), diffusion tensor imaging, functional MRI, and intravoxel incoherent motion (IVIM) applied to cSVD in the existing literature. We performed a review following the PICO Worksheet and Search Strategy, including original papers in English, published between 2000 and 2022. For each MRI method, we extracted information about their contributions, in addition to those established with traditional MRI methods and related information about the origins, pathology, markers, and clinical outcomes in cSVD. This paper presents the first part of the review, which includes 37 studies focusing on ASL, IVIM, and cerebrovascular reactivity (CVR) measures. In general, they have shown that, in addition to white matter hyperintensities, alterations in other neuroimaging parameters such as blood flow and CVR also indicate the presence of cSVD. Such quantitative parameters were also related to cSVD risk factors. Therefore, they are promising, noninvasive tools to explore questions that have not yet been clarified about this clinical condition. However, protocol standardization is essential to increase their clinical use.

Localization of epileptic focus in children with intractable epilepsy

Background Epilepsy is a common neurological disorder. Preoperative evaluation of intractable epileptic patients requires identification of the brain area that is responsible for generating seizure activity. The developments of functional brain imaging with positron emission tomography, single-photon emission computed tomography (SPECT), and functional magnetic resonance imaging (MRI) have ensured its clinical relevance. Aim The aim of this work was to evaluate the role of Tc-99m hexamethylpropylene amine oxime brain-perfusion SPECT and comparing it with MRI) and electroencephalogram (EEG) in the localization of epileptic focus in children with medically intractable epilepsy. Patients and Methods The study population consisted of twenty children with medically intractable epilepsy. They were subjected to complete history taking, clinical assessment, laboratory assessment, and radiological assessment, including EEG, traditional MRI, and SPECT. Results Medically intractable epilepsy is more likely to occur in children with early age of onset, high frequency of seizures per month, more reliability of febrile seizures, and having associated neurological abnormalities. It is more likely to occur in focal forms of seizures than in generalized forms. Children with focal EEG activity and focal seizures are the most likely group to have abnormal brain MRI results and abnormal cerebral perfusion in SPECT. MRI is very important in the localization of structural lesions, but SPECT in localization of functional lesions. Conclusion Interictal SPECT results were nearly the same as the MRI results.

Recent advances in the preclinical development of responsive MRI contrast agents capable of detecting hydrogen peroxide

In this review, we focus on the preclinical development and study of coordination complexes that act as magnetic resonance imaging (MRI) contrast agent sensors for hydrogen peroxide. Redox-responsive probes have been developed that provide signals that can be detected through traditional T1-weighted 1H, 19F, and Chemical Exchange Saturation Transfer MRI. The sensors can also be classified with respect to whether the change in the signal corresponds to the oxidation of the metal ion or the organic ligand.

Metal‐Based Nanoparticle Magnetic Resonance Imaging Contrast Agents: Classifications, Issues, and Countermeasures toward their Clinical Translation

AbstractMetal‐based nanoparticles, especially gadolinium, manganese, and iron, have gained ground in the research of magnetic resonance imaging (MRI) contrast agents. Over the years, contrast agents based on these paramagnetic and superparamagnetic nanomaterials have merited keen attention in the biomedical field due to their desired properties such as sizeable magnetic susceptibility, tunable size, easy surface functionalization, low toxicity, etc. Gadolinium‐based chelates are the traditional MRI contrast agents in the clinic but require improved relaxivities and pharmacokinetics. Nanoparticles possess a larger surface area, demonstrate a longer retention time in the body, and allow the conjugation of several functional molecules to enhance tumor targeting, making them more advantageous. The pursuit of clinically acceptable MRI contrast agents has generated diverse nanoparticles which have augmented their properties, performance, and relevance. Yet, most nanoparticles have not headed for the clinic for various reasons. Here, metal‐based MRI contrast agents and their contrast mechanisms are briefly described. According to relaxation times, T1 and T2, the different types that have evolved are reviewed, emphasizing the properties that have improved their function as MRI contrast agents. Finally, pertinent issues restraining their clinical translation and potential measures to develop more effective and clinically relevant MRI contrast agents are discussed.

Deep Learning Algorithm-Based MRI Image in the Diagnosis of Diabetic Macular Edema.

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N = 48) and the control group (N = 48). A deep learning 3D convolutional neural network (3D-CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D-CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group (P < 0.05). In summary, the 3D-CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.

Standard clinical approaches and emerging modalities for glioblastoma imaging.

Glioblastoma (GBM) is the most common primary adult intracranial malignancy and carries a dismal prognosis despite an aggressive multimodal treatment regimen that consists of surgical resection, radiation, and adjuvant chemotherapy. Radiographic evaluation, largely informed by magnetic resonance imaging (MRI), is a critical component of initial diagnosis, surgical planning, and post-treatment monitoring. However, conventional MRI does not provide information regarding tumor microvasculature, necrosis, or neoangiogenesis. In addition, traditional MRI imaging can be further confounded by treatment-related effects such as pseudoprogression, radiation necrosis, and/or pseudoresponse(s) that preclude clinicians from making fully informed decisions when structuring a therapeutic approach. A myriad of novel imaging modalities have been developed to address these deficits. Herein, we provide a clinically oriented review of standard techniques for imaging GBM and highlight emerging technologies utilized in disease characterization and therapeutic development.

Presurgical tear characteristics and estimated shear modulus as predictors of repair integrity and shoulder function one year after rotator cuff repair.

BackgroundRotator cuff repair provides pain relief for many patients; however, retears are relatively common and affect approximately 20%-70% of patients after repair. Although magnetic resonance imaging (MRI) offers the ability to assess tissue characteristics such as tear size, retraction, and fatty infiltration, it provides little insight into the quality of the musculotendinous tissues the surgeon will encounter during surgery. However, shear wave elastography (SWE) could provide an indirect assessment of quality (ie, stiffness) by measuring the speed of shear waves propagating through tissue. The objective of this study was to determine the extent to which estimated shear modulus predicts repair integrity and functional outcomes 1 year after rotator cuff repair.MethodsThirty-three individuals scheduled to undergo arthroscopic rotator cuff repair were enrolled in this study. Before surgery, shear modulus of the supraspinatus tendon and muscle was estimated using ultrasound SWE. MRIs were obtained before and 1 year after surgery to assess tear characteristics and repair integrity, respectively. Shoulder strength, range of motion, and patient-reported pain and function were assessed before and after surgery. Functional outcomes were compared between groups and across time using a two-factor mixed model analysis of variance. Stepwise regression with model comparison was used to investigate the extent to which MRI and shear modulus predicted repair integrity and function at 1 year after surgery.ResultsAt 1 year after surgery, 56.5% of patients had an intact repair. No significant differences were found in any demographic variable, presurgical tear characteristic, or shear modulus between patients with an intact repair and those with a recurrent tear. Compared with presurgical measures, patients in both groups demonstrated significant improvements at 1 year after surgery in pain (P < .01), self-reported function (P < .01), range of motion (P < .01), and shoulder strength (P < .01). In addition, neither presurgical MRI variables (P > .16) nor shear modulus (P > .52) was significantly different between groups at 1 year after surgery. Finally, presurgical shear modulus generally did not improve the prediction of functional outcomes above and beyond that provided by MRI variables alone (P > .22).ConclusionAlthough SWE remains a promising modality for many clinical applications, this study found that SWE-estimated shear modulus did not predict repair integrity or functional outcomes at 1 year after surgery, nor did it add to the prediction of outcomes above and beyond that provided by traditional presurgical MRI measures of tear characteristics. Therefore, it appears that further research is needed to fully understand the clinical utility of SWE for musculoskeletal tissue and its potential use for predicting outcomes after surgical rotator cuff repair.

Magnetic Resonance Imaging of PSMA-Positive Prostate Cancer by a Targeted and Activatable Gd(III) MR Contrast Agent.

Prostate-specific membrane antigen (PSMA) is a transmembrane protein that is highly expressed in aggressive prostate cancer (PCa) and has been extensively studied as a PCa diagnostic imaging biomarker. Multiple imaging modalities have exploited PSMA as a biomarker including magnetic resonance (MR), Optical, and PET imaging. Of all the imaging MR imaging provides the most detailed information, concurrently providing anatomical, functional, and potentially molecular information. However, the lower sensitivity of MR requires development of molecular MR contrast agents that provides high signal-to-noise ratios. Herein, we report the first targeted and activatable Gd(III)-based MR contrast agents prostate cancer probe 1 and 2 (PCP-1 and -2). We successfully used PCP-2 to differentiate between PSMA+ and PSMA- prostate cancer cells with both in vitro fluorescence imaging and in vivo MR imaging. The in vivo MR imaging results were further supported by ex vivo fluorescence imaging studies, showcasing the unique bimodal feature of PCP-2. Furthermore, PCP-2 highlights a unique molecular MR probe design strategy that improved the sensitivity of traditional biomarker-targeted MR imaging, addressing a critical unmet need in molecular MR imaging field. This work represents the first example of a targeted and activatable MR contrast agent that can be systemically administered in vivo to highlight PSMA+ prostate tumors, paving the way for the clinical translation of MR PSMA imaging.

Quantification of peak blood flow velocity at the cardiac valve and great thoracic vessels by four-dimensional flow and two-dimensional phase-contrast MRI compared with echocardiography: a systematic review and meta-analysis

To objectively examine the agreement and correlation between four-dimensional (4D) flow magnetic resonance imaging (MRI) and traditional two-dimensional (2D) phase-contrast (PC) MRI with the reference standard of Doppler echocardiography for measuring peak blood velocity at the cardiac valve and great arteries, and to assess if 4D flow MRI offers an advantage over the traditional 2D method. The literature was searched systematically for studies that evaluate the degree of correlation and agreement between 4D flow MRI or 2D PC MRI and Doppler retrieved from PubMed, EMBASE, and the Cochrane Library. A meta-analysis was conducted to determine the peak velocity pooled bias with 95% limits of agreement (LoA) and correlation coefficient (r) for 4D flow MRI and 2D PC MRI compared with Doppler. Ten studies that compared 4D flow MRI with Doppler and 12 studies that compared 2D PC MRI with Doppler were included. 4D flow MRI showed an underestimation with bias and 95% LoA of -0.09 (-0.41, 0.24) m/s (p=0.079) while 2D PC MRI showed a poorer agreement with a bias and 95% LoA of -0.25 (-0.53, 0.03), p=0.596. 4D flow MRI and 2D PC MRI showed a strong correlation with R=0.80 (95% CI 0.75, 0.84; p<0.001) and R=0.83 (95% CI 0.79, 0.87; p<0.001), respectively. In this meta-analysis, 4D flow MRI provides improved assessment of peak velocity when compared with traditional 2D PC MRI. 4D flow MRI can be considered an important complement or substitute to Doppler echocardiography for peak velocity assessment.

The Value of Convolutional Neural Network-Based Magnetic Resonance Imaging Image Segmentation Algorithm to Guide Targeted Controlled Release of Doxorubicin Nanopreparation.

There was an investigation of the auxiliary role of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image segmentation algorithm in MRI image-guided targeted drug therapy of doxorubicin nanomaterials so that the value of drug-controlled release in liver cancer patients was evaluated. In this study, 80 patients with liver cancer were selected as the research objects. It was hoped that the CNN-based MRI image segmentation algorithm could be applied to the guided analysis of MRI images of the targeted controlled release of doxorubicin nanopreparation to analyze the imaging analysis effect of this algorithm on the targeted treatment of liver cancer with doxorubicin nanopreparation. The results of this study showed that the upgraded three-dimensional (3D) CNN-based MRI image segmentation had a better effect compared with the traditional CNN-based MRI image segmentation, with significant improvement in indicators such as accuracy, precision, sensitivity, and specificity, and the differences were all statistically marked (p < 0.05). In the monitoring of the targeted drug therapy of doxorubicin nanopreparation for liver cancer patients, it was found that the MRI images of liver cancer patients processed by 3D CNN-based MRI image segmentation neural algorithm could be observed more intuitively and guided to accurately reach the target of liver cancer. The accuracy of targeted release determination of nanopreparation reached 80 ± 6.25%, which was higher markedly than that of the control group (66.6 ± 5.32%) (p < 0.05). In a word, the MRI image segmentation algorithm based on CNN had good application potential in guiding patients with liver cancer for targeted therapy with doxorubicin nanopreparation, which was worth promoting in the adjuvant treatment of targeted drugs for cancer.