Radiology Research Articles

Clinical Examination and Diagnosis of Nasal Bone Fracture; Is There Really a Need for X-ray Nasal Bone?

Objective: To evaluate the role of clinical examination in diagnosing nasal bone fracture. Methodology: This Prospective Observational study was carried out in the departments of Otolaryngology-Head and Neck Surgery and Radiology at the Pakistan Institute of Medical Sciences, Islamabad, from Jan 2022 to Dec 2023. About 1000 patients of nose trauma patients coming to ENT emergency were evaluated clinically by thorough nose examination by ENT surgeon and radiologically by X-ray nasal bone both lateral and anteroposterior view. Based on examination if nasal bone fracture was suspected patient was referred to radiology department for X-ray nasal bone which was reported by radiologist if the bone is fractured or not. The clinical findings were correlated with X-ray reports to find out if there really is a need for X-ray nasal bone for diagnosing nasal bone fracture. The diagnostic accuracy of clinical examination was calculated. Results: When clinical and radiological findings were compared regarding the diagnosis of nasal bone fracture a statistically significant association was found between the two (p=<0.001) showing patients with clinical suspicion of fracture actually had fracture on X-ray. The diagnostic accuracy of clinical examination was very high with Positive Predictive Value, Negative Predictive Value, Sensitivity, Specificity, False Negative Rate and False Positive Rate being 96.1%, 93%, 88.5%, 97.7%, 11.5% and 2.3% respectively. Conclusion: We concluded that almost all patients with suspected nasal bone fracture actually had fractures on X-ray. Fractured nasal bone can correctly be diagnosed clinically by ENT surgeon and there is no real need for X-ray nasal bone just for diagnosing nasal bone fracture.

Impacts of 1.5 T MRI Static Magnetic Field on Biochemical and Enzyme Activity Parameters on Radiology Department Workers.

Magnetic Resonance Imaging (MRI) is an important diagnostic technique that uses powerful magnetic fields to generate detailed images of the human body. The aim of this study is to investigate to how static magnetic fields (SMF) affect the levels of trace elements and biochemical parameters in MRI staff' blood serum. This study examines the impacts of these exposures of 18 participants (9 males and 9 females) aged between 25 and 60.on the levels of trace elements in the blood serum and the biochemical parameters of the MRI staff at Azadi Teaching Hospital in Duhok and Zakho General Hospital-Bidari in Zakho City. Eighteen participants, consisting of nine males and nine females aged between 25 and 60, were selected from these hospitals. The researchers obtained blood samples and conducted analysis to determine the presence of trace elements (sodium, potassium, calcium, chloride) as well as numerous biochemical markers. The results showed that potassium and calcium levels increased with age, and older females had considerable deviations. Chloride levels exhibited a significant increase with age in both males and females. Glucose, creatinine, uric acid, and urea levels showed an increase with age, suggesting the possible damage to kidney function caused by continuous exposure to MRI. Increased levels of liver enzymes (GPT, GOT, ALP) and thyroid-stimulating hormone (TSH) were noticed, particularly in older females, indicating potential liver and thyroid dysfunction. These results highlight the importance of applying strict safety protocols and conducting regular health assessments for MRI personnel to minimize the possible hazards.

Evaluation of junctional zone differential and ratio as possible markers of clinical efficacy in uterine artery embolisation of adenomyosis

BackgroundUterine artery embolisation is a recommended method of adenomyosis treatment with good clinical results. Changes in uterine volume and maximal junctional zone thickness (JZmax) after embolisation are thoroughly analyzed in the literature. In contrast changes in other suggested morphological diagnostic markers of adenomyosis (junctional zone differential / JZdiff—and junctional zone ratio / JZratio) are rarely evaluated. This single-centre retrospective study aimed to analyse the changes in morphological parameters used for the MR imaging diagnosis of adenomyosis (including JZdiff and JZratio) after UAE. Clinical effectiveness and safety were also analysed.Materials and methodsPatients who underwent UAE for pure adenomyosis from Jan 2008 to Dec 2021 were evaluated. Adenomyosis was diagnosed based on JZmax, JZdiff, and JZratio measured on MR imaging. To assess clinical efficacy, the numerical-analog-quality-of-life (QoL) score was routinely obtained from patients at our centre. MRI morphological data were analysed. Statistical analysis was conducted using Wilcoxon signed-rank test, uni- and multivariate regression models, Pearson product-moment correlation, and Kruskal–Wallis tests.ResultsFrom our database of 801 patients who underwent UAE between Jan 2008 to Dec 2021, preprocedural MR images were available in 577 cases and, 15 patients had pure adenomyosis (15/577, 2.6%). Uterine volume, JZmax, and JZdiff decreased significantly after UAE; QoL score increased significantly. A significant correlation was found between QoL change vs. JZmax and JZdiff change. Permanent amenorrhoea and elective hysterectomy 5 years after UAE were both 7.1%.ConclusionChange of JZdiff after UAE in adenomyosis is a potential marker of clinical success. UAE is a clinically safe and effective treatment for adenomyosis.Graphical

“Iliacus muscle abscess as an unexpected cause of posterior hip pain in a healthy young adult female”: a case report

BackgroundIliacus muscle abscess is an uncommon but potentially life-threatening condition that can present with nonspecific symptoms, posing diagnostic challenges. This case report highlights the importance of considering iliopsoas abscess in patients presenting with fever and hip pain, especially in the absence of obvious risk factors or penetrating trauma. The novelty of this case lies in its atypical presentation mimicking a respiratory viral infection and musculoskeletal injury, impeding accurate diagnosis and appropriate management.Case PresentationA previously healthy 21-year-old female who had a mechanical fall 3 weeks prior presented with fever, right hip pain, and respiratory symptoms, initially suggestive of a respiratory infection and musculoskeletal injury. However, initial investigations revealing a markedly high C-reactive protein (CRP) concentration prompted further computed tomography (CT) imaging of her abdomen and pelvis, which uncovered an iliopsoas abscess presumably stemming from antecedent trauma. Subsequent CT guided aspiration along with culture-sensitive antibiotics led to successful treatment and resolution of her symptoms.ConclusionsThis case emphasizes the importance of considering iliopsoas abscess as a possible differential, even in young patients without typical risk factors. Markedly elevated inflammatory markers such as CRP concentrations can serve as a vital indicator, directing attention towards the possibility of septicemia or the presence of an occult abscess, facilitating prompt imaging and accurate diagnosis.

Circulating tumor DNA assisting lymphoma genetic feature profiling and identification.

Lymphoma tissue biopsies cannot fully capture genetic features due to accessibility and heterogeneity. We aimed to assess the applicability of circulating tumor DNA (ctDNA) for genomic profiling and disease surveillance in classic Hodgkin lymphoma (cHL), primary mediastinal large B-cell lymphoma (PMBCL), and diffuse large B-cell lymphoma (DLBCL). Tumor tissue and/or liquid biopsies of 49 cHLs, 32 PMBCLs, and 74 DLBCLs were subject to next-generation sequencing targeting 475 genes. The concordance of genetic aberrations in ctDNA and paired tissues was investigated, followed by elevating ctDNA-based mutational landscapes and the correlation between ctDNA dynamics and radiological response/progression. ctDNA exhibited high concordance with tissue samples in cHL (78%), PMBCL (84%), and DLBCL (78%). In cHL, more unique mutations were detected in ctDNA than in tissue biopsies (P < 0.01), with higher variant allele frequencies (P < 0.01). Distinct genomic features in cHL, PMBCL, and DLBCL, including STAT6, SOCS1, BTG2, and PIM1 alterations, could be captured by ctDNA alone. Prevalent PD-L1/PD-L2 amplifications were associated with more concomitant alterations in PMBCL (P < 0.01). Moreover, ctDNA fluctuation could reflect treatment responses and indicate relapse before imaging diagnosis. Lymphoma genomic profiling by ctDNA was concordant with that by tumor tissues. ctDNA might also be applied in lymphoma surveillance.

Medical image intelligent diagnosis system based on facial emotion recognition and convolutional neural network

The facial emotional information of patients is one of the important indicators of their health status. By combining facial emotion recognition technology, medical imaging intelligent diagnostic systems can more comprehensively understand the psychological and physiological status of patients. This study first selected medical imaging datasets from the publicly available Medical Machine Learning Image Standard database and performed necessary preprocessing, including data cleaning, enhancement, and standardization. Next, a CNN model is constructed to extract key features from medical images and facial expression images through structures such as convolutional layers, activation functions, pooling layers, and fully connected layers. In addition, the system also integrates facial emotion recognition module and medical image analysis module, improving the comprehensiveness of diagnosis through feature fusion. Finally, the diagnostic results are displayed through a user interaction interface. Through comparative experiments, this study verified that the proposed CNN model outperforms traditional LSTM and SVM models in terms of accuracy, recall, and precision. The CNN model has shown significant advantages in medical imaging intelligent diagnosis systems, especially achieving a recall rate of 99%, demonstrating extremely high disease recognition ability. The results of this study not only provide new research directions for the field of intelligent diagnosis in medical imaging, but also lay a solid foundation for future clinical applications.

Medical image intelligent diagnosis system based on facial emotion recognition and convolutional neural network

The facial emotional information of patients is one of the important indicators of their health status. By combining facial emotion recognition technology, medical imaging intelligent diagnostic systems can more comprehensively understand the psychological and physiological status of patients. This study first selected medical imaging datasets from the publicly available Medical Machine Learning Image Standard database and performed necessary preprocessing, including data cleaning, enhancement, and standardization. Next, a CNN model is constructed to extract key features from medical images and facial expression images through structures such as convolutional layers, activation functions, pooling layers, and fully connected layers. In addition, the system also integrates facial emotion recognition module and medical image analysis module, improving the comprehensiveness of diagnosis through feature fusion. Finally, the diagnostic results are displayed through a user interaction interface. Through comparative experiments, this study verified that the proposed CNN model outperforms traditional LSTM and SVM models in terms of accuracy, recall, and precision. The CNN model has shown significant advantages in medical imaging intelligent diagnosis systems, especially achieving a recall rate of 99%, demonstrating extremely high disease recognition ability. The results of this study not only provide new research directions for the field of intelligent diagnosis in medical imaging, but also lay a solid foundation for future clinical applications.

A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.

A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.

Venous thromboembolism After Knee Arthroscopy: Incidence, Risk Factors, Prophylaxis, and Management.

Venous thromboembolism (VTE), comprising pulmonary embolism and deep vein thrombosis, is one of the most common complications after knee arthroscopy. Sequelae of VTE include VTE recurrence, postthrombotic syndrome, and potential for loss of limb or life. Given the increasing volume of knee arthroscopy procedures worldwide and the considerable morbidity and mortality associated with VTE, it is important to prevent, diagnose, and treat VTEs efficiently and effectively. Risk factors such as history of VTE, family history of VTE, genetic coagulopathy, oral contraceptive use, cancer history, and old age increase the risk of postoperative VTE and warrant consideration of prophylaxis. Diagnosis and treatment should be initiated rapidly in the setting of concerning symptoms and positive imaging diagnosis, respectively. The purpose of this review was to provide a framework to individualized VTE risk, weigh prophylaxis options, expedite diagnostic pathways, and implement outpatient treatment algorithms.

Stretchable Ultrasound Metalens for Biomedical Zoom Imaging and Bone Quality Assessment with Subwavelength Resolution.

Ultrasound imaging is extensively used in biomedical science and clinical practice. Imaging resolution and tunability of imaging plane are key performance indicators, but both remain challenging to be improved due to the longer wavelength compared with light and the lack of zoom lens for ultrasound. Here, the ultrasound zoom imaging based on a stretchable planar metalens that simultaneously achieves the subwavelength imaging resolution and dynamic control of the imaging plane is reported. The proposed zoom imaging ultrasonography enables precise bone fracture diagnosis and comprehensive osteoporosis assessment. Millimeter-scale microarchitectures of the cortical bones at different depths can be selectively imaged with a 0.6-wavelength resolution. The morphological features of bone fractures, including the shape, size and position, are accurately detected. Based on the extracted ultrasound information of cancellous bones with healthy matrix, osteopenia and osteoporosis, a multi-index osteoporosis evaluation method is developed. Furthermore, it provides additional biological information in aspects of bone elasticity and attenuation to access the comprehensive osteoporosis assessment. The soft metalens also features flexibility and biocompatibility for preferable applications on wearable devices. This work provides a strategy for the development of high-resolution ultrasound biomedical zoom imaging and comprehensive bone quality diagnosis system.

Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis

This research paper introduces an efficient approach for the segmentation of active and inactive plaques within Fluid-attenuated inversion recovery (FLAIR) images, employing a convolutional neural network (CNN) model known as DeepLabV3Plus SE with the EfficientNetB0 backbone in Multiple sclerosis (MS), and demonstrates its superior performance compared to other CNN architectures. The study encompasses various critical components, including dataset pre-processing techniques, the utilization of the Squeeze and Excitation Network (SE-Block), and the atrous spatial separable pyramid Block to enhance segmentation capabilities. Detailed descriptions of pre-processing procedures, such as removing the cranial bone segment, image resizing, and normalization, are provided. This study analyzed a cross-sectional cohort of 100 MS patients with active brain plaques, examining 5000 MRI slices. After filtering, 1500 slices were utilized for labeling and deep learning. The training process adopts the dice coefficient as the loss function and utilizes Adam optimization. The study evaluated the model's performance using multiple metrics, including intersection over union (IOU), Dice Score, Precision, Recall, and F1-Score, and offers a comparative analysis with other CNN architectures. Results demonstrate the superior segmentation ability of the proposed model, as evidenced by an IOU of 69.87, Dice Score of 76.24, Precision of 88.89, Recall of 73.52, and F1-Score of 80.47 for the DeepLabV3+SE_EfficientNetB0 model. This research contributes to the advancement of plaque segmentation in FLAIR images and offers a compelling approach with substantial potential for medical image analysis and diagnosis.

Diagnostic impact of diffusion weighted imaging in acute appendicitis

Objective: This study reveals the impact of using diffusion-weighted imaging (DWI) to accurately diagnose acute appendicitis as an alternative to CT. Methods: In our study, 41 patients with suspected acute appendicitis, who had undergone magnetic resonance imaging (MRI) following sonographic (US) evaluation, and been referred to the radiology department during a period of 5 months were included. Two radiologists separately evaluated diffusion-weighted images. A five-range scoring system was used in the evaluation of diffusion MRI for each B-value. Any score that is 3 or higher is considered acute appendicitis. Statistical analysis was performed, and ROC curves were used for comparison. Results: In our study, 41 patients were examined. After the exclusion of 6 patients, out of the 35 patients (13 women, 22 men; mean age: 34, age range: 16 to 80) included in the study, 19 (46%) underwent surgery, and pathology results confirmed acute appendicitis. The results of all operated patients were consistent with the ultrasonography reports. However, ultrasonography had three false-positive results. Of these patients, two were identified by two observers and one by a single observer as not having appendicitis via DWI. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy rate for DWI were measured as 52-89%, 50-87.5%, 59-80%, 68-84%, and 65-80%, respectively. Conclusion: According to our study, with the advancement of technology, we believe that MRI usage in appendicitis will possibly increase, potentially surpassing CT, especially in selected cases due to the absence of radiation dose concerns.

Integrating Artificial Intelligence into Biomedical Science Curricula: Advancing Healthcare Education.

The integration of artificial intelligence (AI) into healthcare practice has improved patient management and care. Many clinical laboratory specialties have already integrated AI in diagnostic specialties such as radiology and pathology, where it can assist in image analysis, diagnosis, and clinical reporting. As AI technologies continue to advance, it is crucial for biomedical science students to receive comprehensive education and training in AI concepts and applications and to understand the ethical consequences for such development. This review focus on the importance of integrating AI into biomedical science curricula and proposes strategies to enhance curricula for different specialties to prepare future healthcare workers. Improving the curriculum can be achieved by introducing specific subjects related to AI such as informatics, data sciences, and digital health. However, there are many challenges to enhancing the curriculum with AI. In this narrative review, we discuss these challenges and suggest mitigation strategies.

Prevalence and Classification of C-Shaped Canal and Radix in Mandibular Molars Using Cone-Beam Computed Tomography on Mexican Population.

The diverse morphological configurations in teeth present clinical challenges in root canal treatment, complicating instrumentation and irrigation processes, which can lead to treatment failure. Understanding anatomical variations, such as C-shaped canals and radix entomolaris, enhances clinical skills and improves long-term endodontic treatment success rates. Cone-beam computed tomography (CBCT) offers superior diagnostic capabilities over conventional radiography, enabling the pre-operative detection of root configurations and canal numbers, facilitating personalized endodontic treatments. A total of 2173 teeth of a Mexican population, including 1057 first mandibular molars and 1116 s mandibular molars, were studied using only CBCT to identify C-shaped canals and radix configurations of patients who were treated from 2018 to 2023 at the Department of Radiology at the Faculty of Dentistry, Juarez University of the State of Durango, Mexico. C-shaped canals were identified in 160 teeth, with a prevalence of 0.2% in first mandibular molars and 14.1% in second mandibular molars. The highest frequency was in the left second mandibular molar (3.7) at 14.8%. Gender differences were significant, with higher prevalence in females (27.3%) compared to males (13.3%). The most common C-shaped canal configuration was type C2 (39.3%). Radix entomolaris was found in 52 teeth, with a prevalence of 3.4% in first mandibular molars and 1.4% in second mandibular molars. This research on a Mexican population using cone-beam computed tomography (CBCT) highlights significant findings in the prevalence and types of C-shaped canals and radix entomolaris in mandibular molars for this population. The left second mandibular molar (3.7) showed the highest prevalence at 14.8%, followed closely by the right second mandibular molar (4.7) at 13.5%, with a significant difference (p < 0.001). We found a significant difference in the prevalence of C-shaped canals between genders (p = 0.004, OR 1.78). Additionally, radix entomolaris (p < 0.001) was more frequently identified in first mandibular molars to a significant degree. These insights underscore the importance of CBCT in diagnosing complex root anatomies, which can greatly enhance the success rates of endodontic procedures by allowing for more tailored and precise treatments for this population.

Intraoperative MRI without an intraoperative MRI suite: a workflow for glial tumor surgery

BackgroundIntraoperative MRI (iMRI) has emerged as a useful tool in glioma surgery to safely improve the extent of resection. However, iMRI requires a dedicated operating room (OR) with an integrated MRI scanner solely for this purpose. Due to physical or economical restraints, this may not be feasible in all centers. The aim of this study was to investigate the feasibility of using a non-dedicated MRI scanner at the radiology department for iMRI and to describe the workflow with special focus on time expenditure and surgical implications.MethodsIn total, 24 patients undergoing glioma surgery were included. When the resection was deemed completed, the wound was temporarily closed, and the patient, under general anesthesia, was transferred to the radiology department for iMRI, which was performed using a dedicated protocol on 1.5 or 3 T scanners. After performing iMRI the patient was returned to the OR for additional tumor resection or final wound closure. All procedural times, timestamps, and adverse events were recorded.ResultThe median time from the decision to initiate iMRI until reopening of the wound after scanning was 68 (52–104) minutes. Residual tumors were found on iMRI in 13 patients (54%). There were no adverse events during the surgeries, transfers, transportations, or iMRI-examinations. There were no wound-related complications or infections in the postoperative period or at follow-up. There were no readmissions within 30 or 90 days due to any complication.ConclusionPerforming intraoperative MRI using an MRI located outside the OR department was feasible and safe with no adverse events. It did not require more time than previously reported data for dedicated iMRI scanners. This could be a viable alternative in centers without access to a dedicated iMRI suite.

Imaging features of pediatric meningiomas: emphasis on unusual locations.

This article aims to analyze pediatric meningioma's imaging characteristics, especially those in unusual locations. Pediatric patients with pathologically confirmed meningiomas at our hospital from January 2010 to January 2024 were enrolled. Meningiomas located in the cerebral convexity, parasagittal falcine region, anterior skull base, middle skull base, sphenoid ridge, cerebellopontine angle (CPA), olfactory groove, or juxtasella were considered in usual locations. Meningiomas found in other areas were considered in unusual locations. Clinical information, pathology results, and imaging features of pediatric meningiomas in usual and usual locations were analyzed and compared. A total of 18 patients (19 meningiomas) were enrolled, including 14 males and 4 females, with an average age of 14years (ranging from 6 to 18years). A total of 12 (63.2%) meningiomas were in the unusual location, including four (33.3%) were intraparenchymal, four (33.3%) were intraventricular, two (16.7%) were intraosseous, one (8.3%) case was in the paranasal sinus, and one (8.3%) was intraspinal. The meningiomas in unusual locations usually lacked the meningeal tail sign, and the misdiagnosis rate on preoperative imaging was significantly higher than that of meningiomas in usual locations. Pediatric meningiomas are prone to occur in unusual locations. When they occur in usual locations, they often lack typical radiographic features of meningiomas, leading to potential misdiagnosis before surgery. Recognition of the imaging characteristics of meningiomas in unusual locations in children may facilitate accurate preoperative imaging diagnosis.

Robust stochastic gradient descent with momentum based framework for enhanced chest X-ray image diagnosis

Robust stochastic gradient descent with momentum based framework for enhanced chest X-ray image diagnosis

Generative adversarial networks synthetic optical coherence tomography images as an education tool for image diagnosis of macular diseases: a randomized trial.

This study aimed to evaluate the effectiveness of generative adversarial networks (GANs) in creating synthetic OCT images as an educational tool for teaching image diagnosis of macular diseases to medical students and ophthalmic residents. In this randomized trial, 20 fifth-year medical students and 20 ophthalmic residents were enrolled and randomly assigned (1:1 allocation) into Group real OCT and Group GANs OCT. All participants had a pretest to assess their educational background, followed by a 30-min smartphone-based education program using GANs or real OCT images for macular disease recognition training. Two additional tests were scheduled: one 5 min after the training to assess short-term performance, and another 1 week later to assess long-term performance. Scores and time consumption were recorded and compared. After all the tests, participants completed an anonymous subjective questionnaire. Group GANs OCT scores increased from 80.0 (46.0 to 85.5) to 92.0 (81.0 to 95.5) 5 min after training (p < 0.001) and 92.30 ± 5.36 1 week after training (p < 0.001). Similarly, Group real OCT scores increased from 66.00 ± 19.52 to 92.90 ± 5.71 (p < 0.001), respectively. When compared between two groups, no statistically significant difference was found in test scores, score improvements, or time consumption. After training, medical students had a significantly higher score improvement than residents (p < 0.001). The education tool using synthetic OCT images had a similar educational ability compared to that using real OCT images, which improved the interpretation ability of ophthalmic residents and medical students in both short-term and long-term performances. The smartphone-based educational tool could be widely promoted for educational applications.Clinical trial registration: https://www.chictr.org.cn, Chinese Clinical Trial Registry [No. ChiCTR 2100053195].

Reduction of myocardial lipid content assessed by H1 magnetic resonance spectroscopy in dyslipidemic patients after statins

BackgroundDyslipidemia is one of the main modifiable risk factors for cardiovascular diseases, which accounts for one third of total deaths worldwide. Statin is considered the cornerstone therapy for treating dyslipidemic patients. H1 Cardiac magnetic resonance spectroscopy (MRS) is a special non-invasive, non-irradiating method for assessing myocardial lipid content in vivo in both health and disease.AimTo compare dyslipidemic patients and healthy individuals, and to detect the efficacy of statin on the myocardial lipid content in dyslipidemic patients to detect if there will be changes 6 months after starting statin therapy.MethodsLaboratory lipid profile and myocardial lipid content had been measured by H1 MRS in thirty dyslipidemic patients and fifteen healthy matched age and sex individuals as a control group, then dyslipidemic patients were followed up 6 months after statin therapy at Cardiovascular Medicine and Radiology departments; Mansoura University Hospitals, Dakahlia Governorate, Egypt, during the period from January 2020 to October 2022.ResultsA total of thirty dyslipidemic patients were screened for lipid profile, myocardial lipid content by H1 MRS; 56.67% were male, with a mean age of 49 ± 9.19 years, and compared with fifteen healthy matched age and sex individuals as a control group. Laboratory lipid profile, and triglyceride lipid concentration by MRS were significantly higher in dyslipidemic group before initiating statin therapy compared to control group (p value, 0.001, 0.019 respectively). Median LDL levels were 161.10 ± 30.28 mg/dl before the start of statin therapy and were 114.27 ± 48.33 mg/dl after statin therapy (p < 0.001). There was a statistically significant reduction in triglyceride lipid concentration in dyslipidemic patients after 6 months of statin therapy: from 0.011 (0.001–0.55 (mmol/l), to 0.0025 (0.001–0.04 mmol/l) with a p value < 0.001.ConclusionsIncreased myocardial lipid content as measured by magnetic resonance spectroscopy was demonstrated in dyslipidemic patients in our study that decreased after 6 months of statin therapy.