Spinal Imaging Research Articles

Retrospective Analysis of Paraparalysis/Paraparesis Post Aortic Aneurysm Endovascular Repair

Abstract Objective Paralysis post TEVAR is a challenging complication, this study discusses its incidence, Risk factors, clinical outcome, reversibility, protective strategy and associated mortality. Methods Retrospective study Conducted on patients underwent thoracic and thoracoabdominal aortic Aneurysm Endovascular Repair in Last 11 years (January 2012 till June 2022) in single center as regard incidence, personal history, medical and surgical risk factors, preventive strategies, reversibility of paralysis and associated mortality. Data was obtained from records; paralysis was diagnosed through clinical examination immediately after emergence from anesthesia and frequently post operatively in SICU or even word and after discharge in follow-up outpatient clinic visits. Results A study carried on 137 patients, 15 patients of them developed lower limb paralysis with incidence of 10.9%, 3 patients of them (23.1%) developed paralysis immediately post operative, 4 patients developed paralysis in day 1 post operative (30.8%). And the rest of patients developed delayed onset paralysis (more than 24 Hour post procedure), The latest one developed paralysis 26 days post operative, 8 of paralyzed patients had spinal cord imaging (MRI) 4 of them (50%) didn’t show any abnormality and 3 of them (37.5%) showed spinal cord ischemia and 1 patient didn’t tolerate the MRI machine so the study was discontinued. According to reversibility, 9 patients had complete reversal of their motor and sensory symptoms to the baseline preoperative status. 2 patients had partial reversal of their symptoms, and 4 patients had persistent symptoms without any improvement despite the routine management that included elevating MAP and draining CSF to less than 10 mmHg in order to maintain spinal cord perfusion pressure at level of 80 mmHg and giving naloxone. Data showed 12 out of 81 patients (14.8%) who had functioning spinal drain developed paralysis. Duration of procedure is the most affecting risk factor on paralysis, patients with mean duration of 270 minutes had higher risk of paralysis. Conclusion Prediction of paralysis post TEVAR is still difficult and data analysis showed that the duration of procedure is the only statistically significant variable that we can rely on.

Clinical presentation and imaging findings in juvenile-onset back pain: a ten-year hospital-based retrospective analysis in Douala (Cameroon).

Studies exploring the clinical and imaging characteristics of juvenile-onset back pain (JOBP) are scarce. The purpose of this study was to assess the clinical presentation, imaging findings, and factors associated with JOBP. A retrospective record-based study was conducted among all pediatric cases presenting to the Rheumatology unit of the Douala General Hospital, Cameroon, from January 2014 to December 2023. The study did not include children whose back pain began after 16 years of age. Of the 216 records of patients under 18 examined, 67 children (31 girls) were diagnosed with JOBP. The median age was 15 [13-16] years. More than two-third of the children included in this study had chronic pain (n = 46; 68.65%). Pain was mild to moderate in intensity in 48 children (71.6%). Radiculopathy was present in 10 children (14.92%). The most common location of back pain was the lumbar region (n = 64; 95.52%). Some children experienced pain in more than one location. The location of the pain was not associated with gender, duration of the pain, radiculopathy, or practice of a competitive sport (p > 0.05). Musculoskeletal abnormalities on imaging were found in 38 children (62.29%). In multivariate analysis, peripheral joint involvement [aOR = 0.253 (0.073-0.878); p = 0.030] and mild pain intensity [aOR = 0.077 (0.014-0.422); p = 0.003], were independently associated with the presence of musculoskeletal abnormalities on imaging. JOBP affects a third of children and adolescents of our clinic population. The common musculoskeletal abnormalities found on spine imaging are disc diseases and scoliosis.

Deep Learning-Based Intelligent Diagnosis of Lumbar Diseases with Multi-Angle View of Intervertebral Disc

The diagnosis of degenerative lumbar spine disease mainly relies on clinical manifestations and imaging examinations. However, the clinical manifestations are sometimes not obvious, and the diagnosis of medical imaging is usually time-consuming and highly relies on the doctor’s personal experiences. Therefore, a smart diagnostic technology that can assist doctors in manual diagnosis has become particularly urgent. Taking advantage of the development of artificial intelligence, a series of solutions have been proposed for the diagnosis of spinal diseases by using deep learning methods. The proposed methods produce appealing results, but the majority of these approaches are based on sagittal and axial images separately, which limits the capability of different deep learning methods due to the insufficient use of data. In this article, we propose a two-stage classification process that fully utilizes image data. In particular, in the first stage, we used the Mask RCNN model to identify the lumbar spine in the spine image, locate the position of the vertebra and disc, and complete rough classification. In the fine classification stage, a multi-angle view of the intervertebral disc is generated by splicing the sagittal and axial slices of the intervertebral disc up and down based on the key position identified in the first stage, which provides more pieces of information to the deep learning methods for classification. The experimental results reveal substantial performance enhancements with the synthesized multi-angle view, achieving an F1 score of 96.67%. This represents a performance increase of approximately 15% over the sagittal images at 84.48% and nearly 14% over the axial images at 83.15%. This indicates that the proposed paradigm is feasible and more effective in identifying spinal-related degenerative diseases through medical images.

When Euphoria Turns to Neuropathy: Vitamin B12 Deficiency Related to Nitrous Oxide

Background: Nitrous oxide is a widely abused inhalant known for its rapid onset of euphoric sensations. Chronic use, however, can result in functional vitamin B12 deficiency, causing megaloblastic anemia, peripheral neuropathy, and subacute combined degeneration (SCD) of the spinal cord. Case Presentation: This case report examines a 43-year-old male with a history of attention-deficit/hyperactivity disorder (ADHD) and chronic back pain, who presented with neurological symptoms secondary to chronic nitrous oxide use, subsequent B12 deficiency. The patient reported a one week history of lower extremity swelling, paresthesias, and vertigo. He admitted to daily nitrous oxide use for the past month. Neurological examination revealed ataxia, impaired proprioception, and a positive Romberg test. Laboratory studies showed macrocytic anemia and a severely decreased B12 level. MRI of the cervical spine revealed T2 hyperintensity in the dorsal columns, consistent with SCD. He was treated with intramuscular B12 and advised to discontinue nitrous oxide use. At a three-month follow-up, significant neurological improvement was noted, allowing him to return to work. Conclusion: Chronic nitrous oxide use can lead to functional B12 deficiency and subacute combined degeneration, a potentially reversible condition if treated early. Differential diagnoses include pernicious anemia, malabsorption syndromes, and other neurological disorders. Prompt diagnosis through serum B12, methylmalonic acid, homocysteine levels, and spinal imaging is essential to prevent permanent neurological damage. Nitrous oxide abuse is an emerging cause of B12 deficiency. Early recognition and intervention are essential for recovery. Healthcare providers should consider B12 deficiency in patients presenting with neurological symptoms and a history of nitrous oxide use.

Identification of Terminal Syrinx in a 5-day-old Infant: The Role of Magnetic Resonance Imaging in Diagnosing Back Swellings – A Clinical Review

Abstract A 5-day-old male neonate presented with a palpable lumbar region swelling, leading to the suspicion of a neural tube defect (NTD) or other spinal abnormalities. The infant was born through cesarean section and appeared well-nourished, alert, and interactive with no neurological deficits or signs of infection. Brain screening using ultrasound and head magnetic resonance imaging (MRI) ruled out hydrocephalus and Arnold–Chiari malformation. Spinal imaging using MRI revealed a terminal syrinx in the conus medullaris, which was not apparent on clinical examination. Considering the potential complications and future neurological deterioration, a syringostomy was performed. Neonatal back swellings can be challenging diagnoses due to their broad spectrum of pathologies, including NTDs and terminal syringes. Terminal syringes involve the conus medullaris and cauda equina and may present as a palpable back swelling. These infants are typically asymptomatic at birth but can develop neurological deficits over time. MRI is the gold standard for evaluating suspected NTDs or terminal syringes due to its high resolution, multiplanar imaging capabilities, and lack of ionizing radiation. Ultrasound and computed tomography scans have limitations in diagnosing terminal syringes accurately. Early diagnosis and intervention are crucial to prevent potential complications and improve outcomes for these patients. The management strategy depends on how severe the underlying condition is. For infants without symptoms, close monitoring is recommended. If the baby shows symptoms or is at high risk, surgery can be considered. Syringostomy involves placing a shunt to drain cerebrospinal fluid from the syrinx into the abdominal cavity, reducing pressure and potentially improving neurological function. In conclusion, neonatal back swellings, particularly those caused by terminal syringes, require a thorough diagnostic evaluation and close follow-up. MRI is an essential diagnostic tool due to its high resolution and ability to delineate intracranial and spinal structures effectively. Early diagnosis and intervention can improve outcomes for these patients and prevent complications. Clinicians should maintain a high index of suspicion for terminal syringes in neonates with back swellings and pursue appropriate diagnostic tests, such as MRI, to ensure accurate diagnosis and timely management.

Cerebrospinal Fluid Leaks: Challenges in Localizing Spontaneous Spinal Leak Sites and Minimally Invasive Treatment

The confirmation of cerebrospinal fluid (CSF) leaks in the setting of spontaneous intracranial hypotension (SIH) by imaging involves a growing toolset of multimodal advanced spinal and skull base imaging techniques, for which exists a unique set of challenges for each CSF leak type. Furthermore, the repertoire of minimally invasive CSF leak treatment beyond nontargeted epidural blood patch administration has grown widely, with varied practices across institutions. This review describes current diagnostic imaging and treatment modalities as they apply to the challenges of CSF leak localization and management.

Acute Clinical Events Identified as Relapses With Stable Magnetic Resonance Imaging in Multiple Sclerosis

Understanding the association between clinically defined relapses and radiological activity in multiple sclerosis (MS) is essential for patient treatment and therapeutic development. To investigate clinical events identified as relapses but not associated with new T2 lesions or gadolinium-enhanced T1 lesions on brain and spinal cord magnetic resonance imaging (MRI). This multicenter observational cohort study was conducted between January 2015 and June 2023. Data were extracted on June 8, 2023, from the French MS registry. All clinical events reported as relapses in patients with relapsing-remitting MS were included if brain and spinal cord MRI was performed within 12 and 24 months before the event, respectively, and 50 days thereafter with gadolinium injection. Events were classified as relapses with active MRI (RAM) if a new T2 lesion or gadolinium-enhanced T1 lesion appeared on brain or spinal cord MRI or as acute clinical events with stable MRI (ACES) otherwise. Factors associated with ACES were investigated; patients with ACES and RAM were compared regarding Expanded Disability Status Scale (EDSS) course, relapse rate, confirmed disability accrual (CDA), relapse-associated worsening (RAW), progression independent of relapse activity (PIRA), and transition to secondary progressive (SP) MS, and ACES and RAM rates under each disease-modifying therapy (DMT) were estimated. Among 31 885 clinical events, 637 in 608 patients (493 [77.4%] female; mean [SD] age, 35.8 [10.7] years) were included. ACES accounted for 166 (26.1%) events and were more likely in patients receiving highly effective DMTs, those with longer disease duration (odds ratio [OR], 1.04; 95% CI, 1.01-1.07), or those presenting with fatigue (OR, 2.14; 95% CI, 1.15-3.96). ACES were associated with significant EDSS score increases, lower than those found for RAM. Before the index event, patients with ACES experienced significantly higher rates of relapse (relative rate [RR], 1.21; 95% CI, 1.01-1.46), CDA (hazard ratio [HR], 1.54; 95% CI, 1.13-2.11), and RAW (HR, 1.72; 95% CI, 1.20-2.45). Patients with ACES were at significantly greater risk of SP transition (HR, 2.58; 95% CI, 1.02-6.51). Although RAM rate decreased with DMTs according to their expected efficacy, ACES rate was stable across DMTs. The findings in this study introduce the concept of ACES in MS, which accounted for one-fourth of clinical events identified as relapses.

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care.

Automatic segmentation of spine x-ray images based on multiscale feature enhancement network.

Automatic segmentation of vertebrae in spinal x-ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions. However, due to the inherent characteristics of x-ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer-aided spine image analysis and disease diagnosis applications. In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x-ray images, to aid doctors in diagnosing spinal-related diseases. To enhance feature extraction, the network incorporates a Dual-branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual-branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task-irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high-level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability. In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x-ray images compared to other advanced methods, achieving 92.61±0.431 for MIoU, 92.42±0.329 for DSC, and 99.51±0.037 for Global_accuracy. Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions.

The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images.

This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net's segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Intramedullary pediatric low-grade glioma of the spine.

Pediatric intramedullary spinal cord low-grade gliomas (pLGGs) are rare diagnoses among central nervous system (CNS) tumors in the pediatric population. The classic presentation of the patients includes some degree of neurologic deficit, although many times the symptoms are vague which leads to delayed diagnosis. The first step in the diagnosis includes special parameters in spinal imaging, particularly magnetic resonance imaging (MRI), and surgical resection remains the cornerstone for both diagnosis and treatment. Yet, recent years advancement in molecular and genetic understanding of CNS tumors allows for better adjustment of the treatment and follow-up regimens. Based on postoperative status, adjuvant therapy may provide additional therapeutic advantage for some types of tumors. Ultimately, patients have a very promising prognosis when treated appropriately in most of the cases of pediatric spinal cord LGG with continued advances arising. This manuscript summarizes the most contemporary evidence regarding clinical and treatment features of intramedullary pLGGs.

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study

BackgroundWe compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance of common degenerative pathologies.MethodsThis prospective, single-center study included 31 patients (15 males and 16 females; aged 51 ± 16 years (mean ± standard deviation)) who underwent lumbar spine exams with both TSE-SD and TSE-DL acquisitions for degenerative spine diseases. Images were analyzed by two radiologists and assessed for qualitative image quality using a 4-point Likert scale, quantitative signal-to-noise ratio (SNR) of anatomic landmarks, and detection of common pathologies. Paired-sample t, Wilcoxon, and McNemar tests, unweighted/linearly weighted Cohen κ statistics, and intraclass correlation coefficients were used.ResultsScan time for TSE-DL and TSE-SD protocols was 2:55 and 5:17 min:s, respectively. The overall image quality was either significantly higher for TSE-DL or not significantly different between TSE-SD and TSE-DL. TSE-DL demonstrated higher SNR and subject noise scores than TSE-SD. For pathology detection, the interreader agreement was substantial to almost perfect for TSE-DL, with κ values ranging from 0.61 to 1.00; the interprotocol agreement was almost perfect for both readers, with κ values ranging from 0.84 to 1.00. There was no significant difference in the diagnostic confidence or detection rate of common pathologies between the two sequences (p ≥ 0.081).ConclusionsTSE-DL allowed for a 45% reduction in scan time over TSE-SD in lumbar spine MRI without compromising the overall image quality and showed comparable detection performance of common pathologies in the evaluation of degenerative lumbar spine changes.Relevance statementDeep learning-reconstructed lumbar spine MRI protocol enabled a 45% reduction in scan time compared with conventional reconstruction, with comparable image quality and detection performance of common degenerative pathologies.Key points• Lumbar spine MRI with deep learning reconstruction has broad application prospects.• Deep learning reconstruction of lumbar spine MRI saved 45% scan time without compromising overall image quality.• When compared with standard sequences, deep learning reconstruction showed similar detection performance of common degenerative lumbar spine pathologies.Graphical

Cervical Spine Imaging in Kids - the PECARN rule

Cervical Spine Imaging in Kids - the PECARN rule

MDB-69. RADIOLOGICAL PATTERNS OF RECURRENCE IN MEDULLOBLASTOMA SUBGROUPS AND CORRELATION WITH SURVIVAL

Abstract BACKGROUND Medulloblastomas can recur in up to one-third of the patients despite optimal therapy. The knowledge of the molecular group can provide clues to the possible pattern of relapse and the course of disease, with therapeutic implications. In the present study, we have identified anatomical locations and radiological patterns of relapse. METHODS AND MATERIALS Patients with a histopathological diagnosis and available subgroups of medulloblastoma with first relapse, treated in a single tertiary care center during 2005-2021 with available magnetic resonance imaging of brain and spine were included in the retrospective study. Statistical analysis was done using Pearson chi-square test and Fisher’s exact test. RESULTS 127 patients were included in the final analysis. Median age at diagnosis was 12 years and time to first recurrence was 21 months. Tumor bed relapse was seen in 49% cases, maximum in SHH subgroup, p<0.01. Involvement of the ventricular system (including cisterns and ventricles) was seen in 37% of cases, and was significantly associated (p:<0.01) with non-WNT, non-SHH tumours. Leptomeningeal metastasis was seen in 36% of cases, with significant association (p<0.01) in group 3 (75% of all group 3 cases) and group 4 tumors (50% of all group 4 cases). Disease in spine was most common in group 3 (65%) and group 4 tumours (71%), p<0.01. Hippocampus was involved in 19 cases (15%), mostly in non-WNT, non-SHH (14 cases). Significant difference in survival after recurrence was seen with ventricular, extraventricular metastases with one year survival 83% and 73% respectively, p=0.02. Hippocampal involvement showed significantly decreased survival, one year survival of 37%, p<0.01. CONCLUSIONS Leptomeningeal dissemination and diffuse disease were mostly seen in group 3 tumours, indicating their aggressive nature. The lower rates of ventricular relapse and around hippocampus in SHH medulloblastoma provide the opportunity to explore hippocampal avoidance-craniospinal irradiation.

Hepatic myelopathy neurological complication of chronic liver disease: two case reports

BackgroundHepatic myelopathy is a very rare neurological complication of chronic liver disease. Patients habitually present with progressive pure motor spastic paraparesis. This neurological dysfunction is almost always due to cirrhosis and portocaval shunt, either surgical or spontaneous.Cases reportWe report two cases of a 57-year-old man and a 37-year-old woman with progressive spastic paraparesis linked to cirrhosis and portal hypertension. The two patients are of Tunisian origin (north Africa). Magnetic resonance imaging of the spinal cord of two patients was normal, while brain magnetic resonance imaging showed a T2 hypersignals of the pallidums. These signs, in favor of hepatic encephalopathy in the two patients with cirrhosis with isolated progressive spastic paraparesis without bladder or sensory disorders, help to retain the diagnosis of hepatic myelopathy.ConclusionHepatic myelopathy is a severe and debilitating neurological complication of chronic liver disease. The pathogenesis is misunderstood and seems to be multifactorial, including the selective neurotoxic role both of ammonia and other pathogenic neurotoxins. Usually a pathological brain magnetic resonance imaging showing a hepatic encephalopathy was documented, contrasting with a normal spinal cord magnetic resonance imaging that contributed to diagnosis of hepatic myelopathy. Conservative therapies such as ammonia-lowering measures, diet supplementation, antispastic drugs, and endovascular shunt occlusion show little benefit in improving disease symptoms. Liver transplantation performed at early stage can prevent disease progression and could probably allow for recovery.

Performance Characteristics of the NeuroEXPLORER, a Next-Generation Human Brain PET/CT Imager.

The collaboration of Yale, the University of California, Davis, and United Imaging Healthcare has successfully developed the NeuroEXPLORER, a dedicated human brain PET imager with high spatial resolution, high sensitivity, and a built-in 3-dimensional camera for markerless continuous motion tracking. It has high depth-of-interaction and time-of-flight resolutions, along with a 52.4-cm transverse field of view (FOV) and an extended axial FOV (49.5 cm) to enhance sensitivity. Here, we present the physical characterization, performance evaluation, and first human images of the NeuroEXPLORER. Methods: Measurements of spatial resolution, sensitivity, count rate performance, energy and timing resolution, and image quality were performed adhering to the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. The system's performance was demonstrated through imaging studies of the Hoffman 3-dimensional brain phantom and the mini-Derenzo phantom. Initial 18F-FDG images from a healthy volunteer are presented. Results: With filtered backprojection reconstruction, the radial and tangential spatial resolutions (full width at half maximum) averaged 1.64, 2.06, and 2.51 mm, with axial resolutions of 2.73, 2.89, and 2.93 mm for radial offsets of 1, 10, and 20 cm, respectively. The average time-of-flight resolution was 236 ps, and the energy resolution was 10.5%. NEMA sensitivities were 46.0 and 47.6 kcps/MBq at the center and 10-cm offset, respectively. A sensitivity of 11.8% was achieved at the FOV center. The peak noise-equivalent count rate was 1.31 Mcps at 58.0 kBq/mL, and the scatter fraction at 5.3 kBq/mL was 36.5%. The maximum count rate error at the peak noise-equivalent count rate was less than 5%. At 3 iterations, the NEMA image-quality contrast recovery coefficients varied from 74.5% (10-mm sphere) to 92.6% (37-mm sphere), and background variability ranged from 3.1% to 1.4% at a contrast of 4.0:1. An example human brain 18F-FDG image exhibited very high resolution, capturing intricate details in the cortex and subcortical structures. Conclusion: The NeuroEXPLORER offers high sensitivity and high spatial resolution. With its long axial length, it also enables high-quality spinal cord imaging and image-derived input functions from the carotid arteries. These performance enhancements will substantially broaden the range of human brain PET paradigms, protocols, and thereby clinical research applications.

Delayed spinal arachnoiditis with syringomyelia following aneurysmal subarachnoid haemorrhage: a case report with patient experience

Background and importanceSyringomyelia, or the formation of fluid-filled cysts within the spinal cord, associated with delayed spinal arachnoiditis is an uncommon complication of aneurysmal subarachnoid haemorrhage. To date, about 18 cases have been reported in medical literature, with just two reported in patients under the age of 35 years.Clinical presentationA 27-year-old female patient complained of sudden, severe headaches in the occipital region, nuchal rigidity, and drowsiness when she presented at our institution. A head computed tomography scan revealed intraventricular bleeding in the lateral and fourth ventricles with more extensive haemorrhaging in the frontal horns. A left posterior inferior cerebellar artery (PICA) aneurysm was confirmed via digital subtraction angiogram, and endovascular embolization was done. Two years later, the patient reported intense pain in the lower back along with symptoms suggestive of spinal cord compression. Spinal magnetic resonance imaging (MRI) showed spinal adhesions from C1 to L4, syringomyelia with some vasogenic oedema extending from T3 to T9 level, and a cyst in the lumbar region. Consequently, a right hemilaminectomy was performed along with microsurgical release of arachnoid adhesions and placement of a subdural drain. Radiological and symptomatic improvements were observed. Since then, the patient’s clinical condition has remained stable during the past three years of follow-up visits.ConclusionsLiterature on optimal treatment modalities and patient prognosis is scarce and debated. The time for symptom improvement depends on the level and extent of spinal cord involvement. Rehabilitation may be required for most patients, as complete symptomatic recovery may not be attainable.

Health technology assessment in musculoskeletal radiology: the case study of EOSedge™

ObjectivesHealth technology assessment (HTA) is a systematic process used to evaluate the properties and effects of healthcare technologies within their intended use context. This paper describes the adoption of HTA process to assess the adoption of the EOSedge™ system in clinical practice.MethodsThe EOSedge™ system is a digital radiography system that delivers whole-body, high-quality 2D/3D biplanar images covering the complete set of musculoskeletal and orthopedic exams. Full HTA model was chosen using the EUnetHTA Core Model® version 3.0. The HTA Core Model organizes the information into nine domains. Information was researched and obtained by consulting the manufacturers’ user manuals, scientific literature, and institutional sites for regulatory aspects.ResultsAll nine domains of the EUnetHTA Core Model® helped conduct the HTA of the EOSedge, including (1) description and technical characteristics of the technology; (2) health problem and current clinical practice; (3) safety; (4) clinical effectiveness; (5) organizational aspects; (6) economic evaluation; (7) impact on the patient; (8) ethical aspects; and (9) legal aspects.ConclusionsEOS technologies may be a viable alternative to conventional radiographs. EOSedge has the same intended use and similar indications for use, technological characteristics, and operation principles as the EOS System and provides significant dose reduction factors for whole spine imaging compared to the EOS System without compromising image quality. Regarding the impact of EOS imaging on patient outcomes, most studies aim to establish technical ability without evaluating their ability to improve patient outcomes; thus, more studies on this aspect are warranted.

Identification of Spinal Disorders Based on Spine X-ray Dataset Processing Using the LBP and CNN Algorithms

This research will use deep learning in conducting spinal x-ray image analysis but computational time problems are a problem of this study. Computations on deep learning across multiple nodes can increase training time and longer computation time compared to machine learning models. Based on experimental results, the best spine x-ray image classification results when using the CNN model with accuracy at the training stage, evaluation stage and test stage were 69.00%, 83.33% and 81.16% respectively. CNN models optimized with LBP get the lowest accuracy, with results at the training stage of 62.64%, validation stage of 75.00% and testing stage of 65.22%. LBP feature extraction turns out to have several drawbacks when combined with the CNN model, one major drawback is its inability to process global spatial information while retaining local texture information which causes LBP to be unable to capture the entire structure or context of the image, focusing only on local patterns so that many features of the image are lost. Another issue is the sensitivity of CNNs to image data, which can affect classification accuracy.

Resting-state brain plasticity is associated with the severity in cervical spondylotic myelopathy

ObjectiveTo investigate the brain mechanism of non-correspondence between imaging presentations and clinical symptoms in cervical spondylotic myelopathy (CSM) patients and to test the utility of brain imaging biomarkers for predicting prognosis of CSM.MethodsForty patients with CSM (22 mild-moderate CSM, 18 severe CSM) and 25 healthy controls (HCs) were recruited for rs-fMRI and cervical spinal cord diffusion tensor imaging (DTI) scans. DTI at the spinal cord (level C2/3) with fractional anisotropy (FA) and degree centrality (DC) were recorded. Then one-way analysis of covariance (ANCOVA) was conducted to detect the group differences in the DC and FA values across the three groups. Pearson correlation analysis was then separately performed between JOA with FA and DC.ResultsAmong them, degree centrality value of left middle temporal gyrus exhibited a progressive increase in CSM groups compared with HCs, the DC value in severe CSM group was higher compared with mild-moderate CSM group. (P < 0.05), and the DC values of the right superior temporal gyrus and precuneus showed a decrease after increase. Among them, DC values in the area of precuneus in severe CSM group were significantly lower than those in mild-moderate CSM and HCs. (P < 0.05). The fractional anisotropy (FA) values of the level C2/3 showed a progressive decrease in different clinical stages, that severe CSM group was the lowest, significantly lower than those in mild-moderate CSM and HCs (P < 0.05). There was negative correlation between DC value of left middle temporal gyrus and JOA scores (P < 0.001), and the FA values of dorsal column in the level C2/3 positively correlated with the JOA scores (P < 0.001).ConclusionStructural and functional changes have taken place in the cervical spinal cord and brain of CSM patients. The Brain reorganization plays an important role in maintaining the symptoms and signs of CSM, aberrant DC values in the left middle temporal gyrus may be the possible mechanism of inconsistency between imaging findings and clinical symptoms. Degree centrality is a potentially useful prognostic functional biomarker in cervical spondylotic myelopathy.