Enhancement Magnetic Resonance Imaging Research Articles

Inner ear MRI enhancement based on three dimensional-real IR sequence in patients with Meniere's disease after intravenous gadolinium injection: Comparison of different doses used and exploration of an appropriate dose.

Three-dimensional inversion-recovery sequence with real reconstruction (3D-real IR) magnetic resonance imaging (MRI) can detect endolymphatic hydrops of the inner ear. We aimed to explore a appropriate dose for intravenous gadolinium injection. Observational prospective study. Tertiary referral centre. We collected 90 unilateral definite Meniere's disease patients. All enrolled patients were divided into three groups randomly (patients in group A, B and C received gadolinium injection in 1/1.5/2 times doses respectively). After 4h, inner ear MRI scans were applied. The signal intensities of B-affected ears and C-affected ears were significantly higher than A-affected ears (p<.05); however, no difference was found between B-affected ears and C-affected ears (p=.267). The same conditions also appeared in the three unaffected-ear groups. Moreover, the signal intensities of affected-ear in group A, B and C were significantly higher than that of the corresponding unaffected-ear groups (p<.05). Besides, the subjective visual evaluation scores of group B and C were significantly better than that of group A (p<.05). Intravenous injection of gadolinium in a single dose may be unbefitting for the inner ear imaging based on 3D-real IR MRI, both the applications of gadolinium in 1.5 times and double doses can have a good perilymphatic enhancement effect of inner ear. In order to minimise the use of dose for avoiding or mitigating the adverse reactions and renal damage, 1.5 times dose may be preferred in clinical practice.

Multi-modal brain MRI images enhancement based on framelet and local weights super-resolution.

Magnetic resonance (MR) image enhancement technology can reconstruct high-resolution image from a low-resolution image, which is of great significance for clinical application and scientific research. T1 weighting and T2 weighting are the two common magnetic resonance imaging modes, each of which has its own advantages, but the imaging time of T2 is much longer than that of T1. Related studies have shown that they have very similar anatomical structures in brain images, which can be utilized to enhance the resolution of low-resolution T2 images by using the edge information of high-resolution T1 images that can be rapidly imaged, so as to shorten the imaging time needed for T2 images. In order to overcome the inflexibility of traditional methods using fixed weights for interpolation and the inaccuracy of using gradient threshold to determine edge regions, we propose a new model based on previous studies on multi-contrast MR image enhancement. Our model uses framelet decomposition to finely separate the edge structure of the T2 brain image, and uses the local regression weights calculated from T1 image to construct a global interpolation matrix, so that our model can not only guide the edge reconstruction more accurately where the weights are shared, but also carry out collaborative global optimization for the remaining pixels and their interpolated weights. Experimental results on a set of simulated MR data and two sets of real MR images show that the enhanced images obtained by the proposed method are superior to the compared methods in terms of visual sharpness or qualitative indicators.

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Based on Intratumoral and Peritumoral DCE-MRI Radiomics Nomogram.

Objective To investigate the value of preoperative prediction of breast cancer axillary lymph node metastasis based on intratumoral and peritumoral dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) radiomics nomogram. Material and Methods. In this study, a radiomics model was developed based on a training cohort involving 250 patients with breast cancer (BC) who had undergone axillary lymph node (ALN) dissection between June 2019 and January 2021. The intratumoral and peritumoral radiomics features were extracted from the second postcontrast images of DCE-MRI. Based on filtered radiomics features, the radiomics signature was built by using the least absolute shrinkage and selection operator method. The Support Vector Machines (SVM) learning algorithm was used to construct intratumoral, periatumoral, and intratumoral combined periatumoral models for predicting axillary lymph node metastasis (ALNM) in BC. Nomogram performance was determined by its discrimination, calibration, and clinical value. Multivariable logistic regression was adopted to establish a radiomics nomogram. Results The intratumoral combined peritumoral radiomics signature, which was composed of fifteen ALN status-related features, showed the best predictive performance and was associated with ALNM in both the training and validation cohorts (P < 0.001). The prediction efficiency of the intratumoral combined peritumoral radiomics model was higher than that of the intratumoral radiomics model and the peritumoral radiomics model. The AUCs of the training and verification cohorts were 0.867 and 0.785, respectively. The radiomics nomogram, which incorporated the radiomics signature, MR-reported ALN status, and MR-reported maximum diameter of the lesion, showed good calibration and discrimination in the training (AUC = 0.872) and validation cohorts (AUC = 0.863). Conclusion The intratumoral combined peritumoral radiomics model derived from DCE-MRI showed great predictive value for ALNM and may help to improve clinical decision-making for BC.

Efficient Segmentation for Left Atrium With Convolution Neural Network Based on Active Learning in Late Gadolinium Enhancement Magnetic Resonance Imaging.

BackgroundTo propose fully automatic segmentation of left atrium using active learning with limited dataset in late gadolinium enhancement in cardiac magnetic resonance imaging (LGE-CMRI).MethodsAn active learning framework was developed to segment the left atrium in cardiac LGE-CMRI. Patients (n = 98) with atrial fibrillation from the Korea University Anam Hospital were enrolled. First, 20 cases were delineated for ground truths by two experts and used for training a draft model. Second, the 20 cases from the first step and 50 new cases, corrected in a human-in-the-loop manner after predicting using the draft model, were used to train the next model; all 98 cases (70 cases from the second step and 28 new cases) were trained. An additional 20 LGE-CMRI were evaluated in each step.ResultsThe Dice coefficients for the three steps were 0.85 ± 0.06, 0.89 ± 0.02, and 0.90 ± 0.02, respectively. The biases (95% confidence interval) in the Bland-Altman plots of each step were 6.36% (−14.90–27.61), 6.21% (−9.62–22.03), and 2.68% (−8.57–13.93). Deep active learning-based annotation times were 218 ± 31 seconds, 36.70 ± 18 seconds, and 36.56 ± 15 seconds, respectively.ConclusionDeep active learning reduced annotation time and enabled efficient training on limited LGE-CMRI.

A study of rhino-orbito-cerebral mucormycosis with COVID-19: A new challenge in North West of Rajasthan.

Mucormycosis is a life-threatening fungal disease in immunocompromised patients. There has been increase in the number of mucormycosis associated with COVID-19 patients in second wave. Now country battle with both COVID-19 and mucormycosis. An invasive mucormycosis infection has been a significant burden in India after COVID-19. It has been recently emerged a notifiable disease by the Rajasthan government. Our aim is to develop awareness regarding the importance of early detection and treatment of mucormycosis with COVID-19 and reduce the morbidity and mortality. This is a Prospective longitudinal study including 34 patients diagnosed with acute invasive fungal infection by contrast enhancement magnetic resonance imaging studies of paranasal, orbit and brain or nasal biopsy for KOH/culture. Diagnosis is made through routine blood tests, biopsy, and radiological imaging. The patients taken for the study were COVID-19 reverse transcription-polymerase chain reaction positive or recent post COVID-19 (within 15 days) or symptoms of COVID-19 with bilateral pneumonitis. The study was conducted with 34 patients admitted to the department of medicine with mucormycosis within a month may 2021. A total of 34 patients with a mean age of 50.92 years old and male female ratio 24/10 (70.5/29.41) were included in this study. The most common comorbidity was diabetes mellitus (23 patients 67.64%). Nine patients were newly diagnosed or recent onset of diabetes with or after COVID-19 infection. Twenty-four (70.58%) patients were COVID-19 positive or recent (within 15 days) history of COVID-19 positive. Seven (20.58) patients had the history of steroid as a treatment during COVID-19 and 5 (14.70) patients was on oxygen inhalation. One (2.94%) patient was fully vaccinated, and 5 (14.70) patients had the history of steam inhalation. The most common involvement was naso-orbital mucormycosis found in 28 patients (82.35%) followed by nasal-and orbital 26 (76.47) and 18 (52.94), respectively. Naso-Orbito-Cerebral was seen in 16 (47.05) patients. The more common reported symptoms and signs were headache (76.47), facial numbness (64.70), Nasal discharge (52.94), and ophthalmoplegia (52.94). Cranial nerve involvement was seen in 10 patients (facial palsy in 8 patients and bulbar palsy in 2 patients). Total mortality was 7 (7/34 20.58%). COVID-19 infection associated with the wide range of invasive mucormycosis. Early diagnosis and Clinical suspicion of acute invasive fungal sinusitis among COVID-19 patients is essential for better outcomes and higher survival.

Can Apparent Diffusion Coefficient Predict the Grade, Genotype, or Proliferation Index of Oligodendrogliomas.

Background:Genetic subsets of oligodendrogliomas (OD) have distinct chromosomal and biophysical profiles. Pretherapeutic tumor grade and genotype analysis is a challenging aspect of management, with 1p/19q codeletion status and grade of oligodendroglioma among the most important considerations for clinical decision making.Methodology:Seventy-three patients with histopathological diagnosis of oligodendroglioma were selected, and their preoperative 1.5T magnetic resonance imaging (MRI) scans were reviewed through parameters including diffusion weighted image, susceptibility-weighted imaging, and apparent diffusion coefficient (ADC). These images were correlated with patients' histopathological and chromosomal testing. Tumor border irregularity, homogeneity, contrast enhancement, and other MRI characteristics were also studied. For analysis, descriptive statistics were generated, and normality was evaluated for ADC value, age, and Ki-67 tumor proliferation index.Objectives:The study aimed to determine the correlation of ADC with Ki-67, grade, and 1p/19q co-deletion in oligodendroglioma at a tertiary care hospital within a low-middle income country.Results:Ki-67 tumor proliferation index was high in 33 tumors. It was found to be statistically significant (P = 0.048) with respect to ADC, showing that 1p/19q co-deleted tumors have a difference in their Ki-67 index. Ki-67 also showed a significant relationship (P < 0.05) with grade of OD. However, there was no statistically significant relationship between 1p19q chromosomal co-deletion and ADC. Linear regression was carried out as the data set was continuous. Univariate analysis showed no significant result with all P values above 0.10.Conclusion:Mean ADC is a viable tool to predict Ki-67 and assist prognostic clinical decisions. However, mean ADC alone cannot predict 1p/19q codeletion and tumor grades in OD. Further supplementation with other radiological modalities may provide greater yield and positive results.

Investigation of annular pancreas through multiple detector spiral CT (MDCT) and MRI.

Background and purposeTo investigate annular pancreas in adults using imaging features displayed on computed tomography (CT) and magnetic resonance imaging (MRI).MethodsRetrospective review of annular pancreas in patients undergoing CT or MRI examinations. Two abdominal radiologists blindly reviewed the CT, MRI, and magnetic resonance cholangiopancreatography (MRCP) images from the Picture Archiving and Communication Systems (PACS). A Kruskal–Wallis test was performed to evaluate subjective scoring, with Mann–Whitney test for the comparison. A p‐value less than 0.05 was considered statistically significant.ResultsEleven patients (45.8%) presented a complete ring of pancreatic tissue surrounding duodenum, displayed as circular, triangular, or sandwich sign configuration, the other 13 patients (54.2%) had incomplete annular pancreas which displayed a crocodile jaw appearance, pancreatic tissue extending in a posterolateral or anterolateral direction towards duodenum. When comparing CT images of each sequence, the arterial phase group had the highest score compared with the venous phase and the plain film group (χ 2 = 58.21, p < 0.05). When comparing MRI enhancement volumetric interpolated breath‐hold examination (VIBE) sequences, arterial phase group scores were the highest (χ 2 = 18.98, p < 0.05).ConclusionsBoth CT and MRI could detect annular pancreas, with artery phase being the best sequence to diagnose annular pancreas.

Radiomics Analysis of Multi-Phase DCE-MRI in Predicting Tumor Response to Neoadjuvant Therapy in Breast Cancer.

Objective: To explore whether the pretreatment dynamic contrast enhancement magnetic resonance imaging (DCE-MRI) and radiomics signatures were associated with pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer. Method: A retrospective review of 70 patients with breast invasive carcinomas proved by biopsy between June 2017 and October 2020 (26 patients were pathological complete response, and 44 patients were non-pathological complete response). Within the pre-contrast and five post-contrast dynamic series, a total of 1037 quantitative imaging features were extracted from in each phase. Additionally, the Δfeatures (the difference between the features before and after the comparison) were used for subsequent analysis. The least absolute shrinkage and selection operator (LASSO) regression method was used to select features related to pCR, and then use these features to train multiple machine learning classifiers to predict the probability of pCR for a given patient. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the predictive performances of the radiomics model for each of the five phases of time points. Result: Among the five phases, each individual phase performed with AUCs ranging from 0.845 to 0.919 in predicting pCR. The best single phases performance was given by the 3rd phase (AUC = 0.919, sensitivity 0.885, specificity 0.864). 5 of the features have significant differences between pCR and non-pCR groups in each phase, most features reach their maximum or minimum in the 2nd or 3rd phase. Conclusion: The radiomic features extracted from each phase of pre-treatment DCE-MRI possess discriminatory power to predict tumor response.

Effect of DrOnedarone on atrial fibrosis progression and atrial fibrillation recurrence postablation: Design of the EDORA randomized clinical trial.

BackgroundAtrial fibrillation (AF) recurrence after catheter ablation is associated with worse outcomes and quality of life. Left atrial (LA) structural remodeling provides the essential substrate for AF perpetuation. Baseline extent and the progression of LA fibrosis after ablation are strong predictors of postprocedural AF recurrence. Dronedarone is an antiarrhythmic drug proven to efficiently maintain sinus rhythm.ObjectiveWe sought to investigate the effect of the antiarrhythmic drug Dronedarone in decreasing LA fibrosis progression and AF recurrence after ablation of AF patients.MethodsEDORA (NCT04704050) is a multicenter, prospective, randomized controlled clinical trial. Patients with persistent or paroxysmal AF undergoing AF ablation will be randomized into Dronedarone versus placebo/standard of care. The co‐primary outcomes are the recurrence of atrial arrhythmias (AA) within 13 months of follow‐up after ablation and the progression of left atrial fibrosis postablation. All patients will receive a late‐gadolinium enhancement magnetic resonance imaging at baseline, 3‐ and 12‐month follow‐up for the quantification of LA fibrosis and ablation‐related scarring. AA recurrence and burden will be assessed using a 30‐day ECG patch every 3 months with daily ECG recordings in between. Quality of life improvement is assessed using the AFEQT and AFSS questionnaires.ConclusionEDORA will be the first trial to assess the progression of LA structural remodeling after ablation and its association with Dronedarone treatment and ablation success in a randomized controlled fashion. The trial will provide insight into the pathophysiology of AF recurrence after ablation and may provide potential therapeutic targets to optimize procedural outcomes.

Noise-residue learning convolutional network model for magnetic resonance image enhancement

Magnetic Resonance Image (MRI) is an important medical image acquisition technique used to acquire high contrast images of human body anatomical structures and soft tissue organs. MRI system does not use any harmful radioactive ionized material like x-rays and computerized tomography (CT) imaging techniques. High-resolution MRI is desirable in many clinical applications such as tumor segmentation, image registration, edges & boundary detection, and image classification. During MRI acquisition, many practical constraints limit the MRI quality by introducing random Gaussian noise and some other artifacts by the thermal energy of the patient body, random scanner voltage fluctuations, body motion artifacts, electronics circuits impulse noise, etc. High-resolution MRI can be acquired by increasing scan time, but considering patient comfort, it is not preferred in practice. Hence, postacquisition image processing techniques are used to filter noise contents and enhance the MRI quality to make it fit for further image analysis tasks. The main motive of MRI enhancement is to reconstruct a high-quality MRI while improving and retaining its important features. The new deep learning image denoising and artifacts removal methods have shown tremendous potential for high-quality image reconstruction from noise degraded MRI while preserving useful image information. This paper presents a noise-residue learning convolution neural network (CNN) model to denoise and enhance the quality of noise-corrupted low-resolution MR images. The proposed technique shows better performance in comparison with other conventional MRI enhancement methods. The reconstructed image quality is evaluated by the peak-signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics by optimizing information loss in reconstructed MRI measured in mean squared error (MSE) metric.

Non-Invasive Evaluation of Patients Undergoing Percutaneous Coronary Intervention for Chronic Total Occlusion.

Background: As percutaneous coronary intervention (PCI) for chronic total occlusion (CTO) gains wider acceptance as a therapeutic option for coronary artery disease, the importance of appropriate patient selection has increased. Although cardiovascular magnetic resonance imaging (MRI) allows segmental and quantitative analyses of myocardial ischemia and scar transmurality, it has limitations, including contraindications, cost, and accessibility. This study established a non-invasive method to evaluate patients undergoing CTO-PCI using two-dimensional speckle-tracking echocardiography (2D-STE). Methods: Overall, we studied 55 patients who underwent successful CTO-PCI. Cardiovascular MRI and 2D-STE were performed before and 8 ± 2 months after CTO-PCI. Segmental findings of strain parameters were compared with those obtained with late gadolinium enhancement and stress-perfusion MRI. Results: With a cutoff of −10.7, pre-procedural circumferential strain (CS) showed reasonable sensitivity (71%) and specificity (73%) for detecting segments with transmural scar. The discriminatory ability of longitudinal strain (LS) for segments with transmural scar significantly improved during follow-up after successful CTO-PCI in the territory of the recanalized artery (area under the curve (AUC) 0.70 vs. 0.80, p < 0.001). LS accuracy was lower than that of CS at baseline (AUC 0.70 vs. 0.79, p = 0.048), and was increased at follow-up (AUC 0.80 vs. 0.82, p = 0.81). Changes in myocardial perfusion reserve from baseline to follow-up were significantly associated with those in LS but not in CS. Conclusions: Use of 2D-STE may allow the non-invasive evaluation of patients undergoing CTO-PCI to assess the indication before the procedure and treatment effects at follow-up.

Imaging analysis for multiple paramagnetic agents using OMRI and electrophoresis.

Nitroxides have been widely used as a molecular probe for analysis of various diseases models. This article describes an analytical method for separation and semi-quantification of multiple paramagnetic contrast agents with simple procedure combining electrophoresis and Overhauser enhancement magnetic resonance imaging (OMRI) imaging. We used three nitroxides, 3-carbamoyl PROXYL, 3-carboxy PROXYL, and CAT-1, which have different ionic charges in the molecule. In addition, we showed that this method could apply for in vitro measurement using biological sample. The results showed the nitroxides were successfully separated with electrophoresis depending on their charge, and their separation was visualized with OMRI after electrophoresis. Vehicle media such as whole blood did not affect the electrophoresis results and OMRI enhancement factor. Thus, the method can be used to analyze the redox status of biological samples without preprocessing. This analytical method enables in vitro measurement of biological samples to determine the redox status of specific tissue layers using paramagnetic agents, which is helpful for detailed analysis of redox-related diseases.

Abbreviated protocol combining quantitative diffusion-weighted imaging: a new strategy increasing diagnostic accuracy for breast magnetic resonance imaging?

To compare the diagnostic accuracy of an abbreviated protocol (AP) with or without quantitative apparent diffusion coefficient (ADC) values on diffusion-weighted imaging (DWI) and a full diagnostic protocol (FDP) in terms of the Breast Imaging Reporting and Data System (BI-RADS) classification of breast magnetic resonance imaging (MRI). Our study sample consisted of 436 patients undergoing breast MRI from January to October 2015 in a clinical setting. The three reviews included a pre-contrast and the first single post-contrast T1-weighted (T1W) sequences (AP1), AP1 combined with quantitative DWI (AP2), and the FDP, the AP1 of which were assessed independently by a junior and senior radiologist. Agreement on the evaluation of the BI-RADS classifications (between the junior and senior radiologists, between AP1 and FDP, and between AP2 and FDP) was assessed using the kappa test statistic. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were compared between AP1 and FDP plus between AP2 and FDP. Diagnostic parameters of these reviews were examined using the McNemar test. The study included 436 patients, with 251 breast cancers, 99 benign lesions, and 86 patients with benign or no lesions and followed up for at least 24 months. The agreement of the BI-RADS classifications between the junior and senior radiologists was very good (kappa =0.847). The agreement between AP2 and FDP (kappa =0.931) was higher than the agreement between AP1 and FDP (kappa =0.872) on evaluating the BI-RADS benign and malignant classifications. The sensitivity/specificity/PPV/NPV was 95.6%/83.8%/88.9%/93.4% for AP1, 98.0%/83.8%/89.1%/96.9% for AP2, 98.8%/83.8%/89.2%/98.1% for FDP, respectively. The addition of quantitative DWI to the abbreviated MRI protocol based on the pre-and first post-contrast sequence improved diagnostic performance for characterizing breast lesions. Quantitative DWI may be a useful adjunct to dynamic contrast enhancement (DCE) of breast MRI.

Real-time MR tracking of AAV gene therapy with βgal-responsive MR probe in a murine model of GM1-gangliosidosis

Transformative results of adeno-associated virus (AAV) gene therapy in patients with spinal muscular atrophy and Leber’s congenital amaurosis led to approval of the first two AAV products in the United States to treat these diseases. These extraordinary results led to a dramatic increase in the number and type of AAV gene-therapy programs. However, the field lacks non-invasive means to assess levels and duration of therapeutic protein function in patients. Here, we describe a new magnetic resonance imaging (MRI) technology for real-time reporting of gene-therapy products in the living animal in the form of an MRI probe that is activated in the presence of therapeutic protein expression. For the first time, we show reliable tracking of enzyme expression after a now in-human clinical trial AAV gene therapy (ClinicalTrials.gov: NTC03952637) encoding lysosomal acid beta-galactosidase (βgal) using a self-immolative βgal-responsive MRI probe. MRI enhancement in AAV-treated enzyme-deficient mice (GLB-1−/−) correlates with βgal activity in central nervous system and peripheral organs after intracranial or intravenous AAV gene therapy, respectively. With >1,800 gene therapies in phase I/II clinical trials (ClinicalTrials.gov), development of a non-invasive method to track gene expression over time in patients is crucial to the future of the gene-therapy field.

B-PO05-017 IN VIVO TO EX VIVO HIGH RESOLUTION OPTICAL MAPPING AND CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING TO REVEAL ATRIAL FIBRILLATION DRIVERS AND IMPROVE IDENTIFICATION OF ARRHYTHMOGENIC STRUCTURAL SUBSTRATES IN PERSISTENT ATRIAL FIBRILLATION CANINE MODEL

Persistent AF (perAF) may be maintained by reentrant drivers within arrhythmogenic fibrotic substrates. Conventional multielectrode mapping (MEM) and delayed enhancement (DE) magnetic resonance imaging (MRI) are suffering to identify drivers and their substrates and require rigorous validation. Define AF driving mechanism in vivo and ex vivo using near-infrared optical mapping (NIOM) in a perAF canine model and correlate driver locations with fibrosis architecture detected by histology validated 3D DE MRI. Canines (n=5) were imaged via DE MRI prior to perAF (induced by rapid atrial pacing) and after 4 months of self-sustained perAF. Atria were mapped with NIOM and MEM in vivo and ex vivo (Figure). Regional histology sections validated chamber-specific fibrosis thresholds on DE MRI. In all animals, AF was maintained by 2 localized temporally competing reentrant drivers in the left atrium (LA) visualized both in vivo and ex vivo. Ex vivo targeted ablation terminated or converted AF to slower tachycardia. MEM and NIOM showed comparable AF cycle length in vivo and ex vivo with driver regions faster than LA non-drivers and right atria (p<0.05). Histology validated DE MRI showed AF induced heterogeneous biatrial fibrosis upregulation, with higher fibrotic content in driver regions. In canine perAF model, integration of in vivo to ex vivo NIOM, MEM and DE MRI revealed that localized AF reentrant drivers within fibrotic substrates maintain AF. The higher imaging acuity of in vivo NIOM and histology validated DE MRI could allow to define fingerprints of arrhythmogenic fibrotic driver hubs for patient-specific targeted AF ablation.

B-PO02-096 PREDICTION OF ATRIAL FIBRILLATION RECURRENCE POST-ABLATION BASED ON ATRIAL FIBROSIS BY LATE GADOLINIUM ENHANCEMENT: A META-ANALYSIS

The degree of left atrial fibrosis has been identified as one of the independent predictors of the recurrence of atrial fibrillation (AF) post-ablation. Late gadolinium enhancement magnetic resonance imaging (LGE-MRI) has been known to predict the recurrence of AF post-ablation based on left atrial fibrosis in various observational studies. In this paper, we have systematically combined the studies performed that used LGE-MRI to quantify atrial fibrosis based on the different stages of fibrosis before ablation to predict AF's recurrence. This meta-analysis aims to investigate the recurrence of atrial AF post-ablation based on the various stages of fibrosis seen in the late gadolinium enhancement magnetic resonance imaging (LGE-MRI). Electronic databases were searched nine studies that met the inclusion criteria were identified. A total of 1,787 patients underwent LGE-MRI to assess atrial fibrosis before catheter ablation for AF. We performed three sets of analyses: first, compared stage IV versus stage I (reference group). The second set examined the combined stages III and IV versus stages I and II (reference group). The third set compared stage IV versus combined stages I, II, and III. The metanalysis relied on a random-effects model to pool the odds ratios (OR) and 95% confidence intervals (CI) using the DerSimonian and Laird method. The data was analyzed using StatsDirect software, England. The study showed a higher rate of AF recurrence after ablation in stage IV atrial fibrosis compared with those in stage I (OR, 9.54; 95% CI, 3.81 to 28.89; P<00001). Also, in patients with combined stages III & IV of atrial fibrosis, AF recurrence was significantly higher after ablation than stages I & II groups (OR, 2.37; 95% CI, 1.61 to 3.50; P<00001). Similarly, patients with stage IV compared to combined stages I, II, and III have higher odds of recurrence post-ablation (OR, 4.24; 95% CI, 2.39- 7.52, P < 0.001). This metanalysis demonstrates the strong association of left atrial fibrosis seen in LGE-MRI and AF post-ablation recurrence. The finding of this study will further assist clinicians in predicting the rate of recurrence of AF based on the amount of fibrosis and tailor therapeutic decisions for further management.

Intraoperative Indocyanine Green Angiography for Assessing Flap Perfusion in Skull Base Reconstruction: A Systematic Review.

Objective This study was aimed to study the current use of intraoperative indocyanine green (ICG) angiography during skull base reconstruction and understand its efficacy in predicting postoperative magnetic resonance imaging (MRI) enhancement and flap. Study Design The Embase, the Cochrane Central Register of Controlled Trials (CENTRAL), Web of Science, and Google Scholar databases were searched from the date of inception until August 2020 for studies of ICG flap perfusion assessment during skull base reconstruction. The primary outcome of interest was the development of cerebrospinal fluid (CSF) leak after skull base reconstruction. Secondary outcomes of interest included postoperative meningitis, flap MRI enhancement, flap necrosis, flap perfusion measurements, and total complications. Results Search results yielded 189 studies, from which seven studies with a total of 104 patients were included in the final analysis. There were 44 nasoseptal flaps (NSF), two lateral nasal wall flaps (LNWF), 14 pericranial flaps (PCF), and 44 microvascular free flaps. The rates of CSF leak and postoperative MRI enhancement were 11 and 94%, respectively. There was one case of postoperative meningitis. Pooled analysis of the available data showed that intraoperative ICG flap perfusion was associated with flap enhancement on postoperative MRI ( p = 0.008) and CSF leak ( p = 0.315) by Fisher's exact test. Conclusion The available literature suggests intraoperative ICG enhancement is associated with postoperative MRI enhancement. Given the small sample sizes in the literature and the rarity of complications associated with skull base reconstruction, intraoperative ICG enhancement has not been predictive of flap necrosis or postoperative complications such as CSF leak or meningitis. Level of Evidence This study presents level 3 evidence as a systematic review of case studies, case reports, and retrospective and prospective trials with no blinding, controls, and inconsistently applied reference standards.

Atrial fibrosis progression in patients with no history of atrial fibrillation.

Unexpected high levels of atrial fibrosis are found in individuals with no history of atrial fibrillation (AF). The temporal behavior of atrial fibrosis in this population is still unknown. We sought to investigate the progression and predictors of atrial fibrosis in non-AF individuals. Non-AF individuals at baseline who underwent late gadolinium enhancement magnetic resonance imaging (LGE-MRI) for assessment of left atrial (LA) fibrosis at least twice were retrospectively included in this study. The incidence of AF was assessed using review of medical records. In 42 non-AF patients (15 females, 65.9 ± 8.6 years old), all patients had a detectable level of LA fibrosis at baseline, ranging from 4.5% to 28.8%, with a mean of 12.9 ± 5.9%. LA fibrosis in the second LGE-MRI was significantly higher in all patients compared to the first measurement (mean value of 12.9 ± 5.9% vs. 17.34 ± 6.8%; p < .05). Congestive heart failure was a significant clinical predictor of atrial fibrosis progression. The seven patients (16.6%) who developed new-onset AF during follow-up showed a significantly higher degree of LA fibrosis on their second MRI, compared to individuals who stayed in sinus rhythm (20.5 ± 6.9% vs. 16.7 ± 6.7%, p < .05). Atrial fibrotic remodeling is a dynamic process that is progressively increasing in non-AF patients, accentuated by congestive heart failure. The higher extent of LA remodeling observed in patients who developed AF could highlight either the fact that AF is an expression of a highly dynamic left atrial substrate, or that remodeling processes are accelerated by AF.

Automated Left Ventricle Ischemic Scar Detection in CT Using Deep Neural Networks.

Objectives: The aim of this study is to develop a scar detection method for routine computed tomography angiography (CTA) imaging using deep convolutional neural networks (CNN), which relies solely on anatomical information as input and is compatible with existing clinical workflows.Background: Identifying cardiac patients with scar tissue is important for assisting diagnosis and guiding interventions. Late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) is the gold standard for scar imaging; however, there are common instances where it is contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is faster than Cardiovascular magnetic resonance imaging but is unable to reliably image scar.Methods: A dataset of LGE MRI (200 patients, 83 with scar) was used to train and validate a CNN to detect ischemic scar slices using segmentation masks as input to the network. MRIs were segmented to produce 3D left ventricle meshes, which were sampled at points along the short axis to extract anatomical masks, with scar labels from LGE as ground truth. The trained CNN was tested with an independent CTA dataset (25 patients, with ground truth established with paired LGE MRI). Automated segmentation was performed to provide the same input format of anatomical masks for the network. The CNN was compared against manual reading of the CTA dataset by 3 experts.Results: Note that 84.7% cross-validated accuracy (AUC: 0.896) for detecting scar slices in the left ventricle on the MRI data was achieved. The trained network was tested against the CTA-derived data, with no further training, where it achieved an 88.3% accuracy (AUC: 0.901). The automated pipeline outperformed the manual reading by clinicians.Conclusion: Automatic ischemic scar detection can be performed from a routine cardiac CTA, without any scar-specific imaging or contrast agents. This requires only a single acquisition in the cardiac cycle. In a clinical setting, with near zero additional cost, scar presence could be detected to triage images, reduce reading times, and guide clinical decision-making.

Experimental Validation of Enhanced Magnetic Resonance Imaging (EMRI) Using Particle Image Velocimetry (PIV).

Flow-sensitive four-dimensional Cardiovascular Magnetic Resonance Imaging (4D Flow CMR) has increasingly been utilised to characterise patients' blood flow, in association with patiens' state of health and disease, even though spatial and temporal resolutions still constitute a limit. Computational fluid dynamics (CFD) is a powerful tool that could expand these information and, if integrated with experimentally-obtained velocity fields, would enable to derive a large variety of the flow descriptors of interest. However, the accuracy of the flow parameters is highly influenced by the quality of the input data such as the anatomical model and boundary conditions typically derived from medical images including 4D Flow CMR. We previously proposed a novel approach in which 4D Flow CMR and CFD velocity fields are integrated to obtain an Enhanced 4D Flow CMR (EMRI), allowing to overcome the spatial-resolution limitation of 4D Flow CMR, and enable an accurate quantification of flow. In this paper, the proposed approach is validated in a U bend channel, an idealised model of the human aortic arch. The flow patterns were studied with 4D Flow CMR, CFD and EMRI, and compared with high resolution 2D PIV experiments obtained in pulsatile conditions. The main strengths and limitations of 4D Flow CMR and CFD were illustrated by exploiting the accuracy of PIV by comparing against PIV velocity fields. EMRI flow patterns showed a better qualitative and quantitative agreement with PIV results than the other techniques. EMRI enables to overcome the experimental limitations of MRI-based velocity measurements and the modelling simplifications of CFD, allowing an accurate prediction of complex flow patterns observed experimentally, while satisfying mass and momentum balance equations.