MRI Scans Research Articles

Direct visualization of microwires in hybrid depth electrodes using high-resolution photon-counting CT.

Hybrid depth electrodes are increasingly being used for epilepsy monitoring and human neurophysiology research. Microwires extending from the tip of the Behnke-Fried (BF) electrode into (sub)cortical areas allow to isolate single neurons and perform microstimulation. Conventional CT or MRI visualize the entire microwire bundle as an artifact extending from the BF electrode tip with low resolution, without proper identification of individual microwires. We illustrate the first direct visualization method of individual microwires using high-resolution photon-counting CT (PCCT). Coregistration of the PCCT scan with a preoperative MRI can visualize individual wires directly in cortex, which is an advantage as it provides feedback on the accuracy of the implantation method and can guide future implantations. This PCCT technique allows for accurately depicting individual microwires which could be relevant for neuroscientific research through improved visualization and implantation of specific cortical and subcortical brain areas. PLAIN LANGUAGE SUMMARY: Researchers are using hybrid depth electrodes to study epilepsy and brain activity. These electrodes, called Behnke-Fried (BF) electrodes, have microwires at the tip that can record single neurons and stimulate brain areas. Regular CT or MRI scans do not show the individual microwires clearly. The authors use a new high-resolution photon-counting CT (PCCT) technique, which can show each individual microwire in the brain. By combining PCCT with MRI, the authors can precisely see where the microwires are located. This could improve future implantation surgeries and brain research.

Preliminary Exploration of T1ρ and T2 Mapping in Porcine Articular Cartilage Using Very-Low-Field Magnetic Resonance Imaging.

The high prevalence of osteoarthritis emphasizes the need for a cost-effective and accessible method for its early diagnosis. Recently, the portability and affordability of very-low-field (VLF) magnetic resonance imaging (MRI, 10-100 mT) have caused it to gain popularity. Nevertheless, there is insufficient evidence to quantify early degenerative changes in cartilage using VLF MRI. This study assessed the potential of T1ρ and T2 mapping for detecting degenerative changes in porcine cartilage specimens using a 50 mT MRI scanner. T2- and T1ρ-weighted images were acquired using a 50 mT MRI scanner with 2D spin-echo and triple-refocused T1ρ preparation sequences. MRI scans of porcine cartilage were also acquired using a 3 T MRI scanner for comparison. A mono-exponential algorithm was applied to fit a series of T2- and T1ρ-weighted images. T2 values for CuSO4·5H2O solutions measured via Carr-Purcell-Meiboom-Gill (CPMG) and spin-echo sequences were compared to verify the algorithm's reliability. The nonparametric Kruskal-Wallis statistical test was used to compare T2 and T1ρ values. Experimental repeatability was assessed using the root-mean-square of the coefficient of variation (rmsCV). T2 values of the CuSO4·5H2O solutions obtained using the spin-echo sequence showed differences within 2.3% of those obtained using the CPMG sequence, indicating the algorithm's reliability. The T1ρ values for varying concentrations of agarose gel solutions were higher than the T2 values. Furthermore, 50 mT and 3 T MRI results showed that both the T1ρ and T2 values were significantly higher for porcine cartilage degraded for 6 h vs intact cartilage, with p-values of 0.006 and 0.01, respectively. Our experimental results showed good reproducibility (rmsCV < 8%). We demonstrated the feasibility of quantitative cartilage imaging via T2 and T1ρ mapping at 50 mT MRI for the first time.

Isolated laparoscopic extra-vesical ureteric reimplantation (I-LEVUR) for upper moiety ectopic ureter without lower moiety vesicoureteric reflux.

To evaluate the feasibility and outcomes of isolated laparoscopic extra-vesical ureteric reimplantation (I-LEVUR) for upper moiety ectopic ureter in patients with non-refluxing lower moiety ureter and avoid reimplanting normal lower moiety orifice. Between 2013 and 2024, 12 patients (8 females, 4 males) with ectopic ureter associated with a duplex system underwent I-LEVUR. Preoperative assessment included ultrasonography, voiding cystourethrography (VCUG), and magnetic resonance urography (MRU)/retrograde pyelogram (RGP). The procedure involved laparoscopic extravesical approach dissecting only the ectopic ureter in lower most part, preserving vascularity, and performing an isolated reimplantation without manipulating lower moiety ureter. Twelve patients, with a median age of 2.8years, underwent I-LEVUR. The mean operative time was 127.5min. No intraoperative complications occurred. Postoperative follow-up (median: 78.5months) showed no cases of ureteral obstruction or significant complications. Urinary continence improved in all patients. Two patients developed mild vesicoureteral reflux, managed conservatively, and one patient had minor urine leak which resolved spontaneously. I-LEVUR is a viable and effective alternative to traditional en bloc reimplantation for upper moiety ectopic ureter. It preserves the normal ureteric orifice, reduces surgical trauma, and offers excellent outcomes in terms of renal function and urinary continence. Further studies with larger cohorts, control group of common sheath reimplantation, randomization, robust statistical validation and longer follow-up are recommended.

Characterizing T1 in the fetal brain and placenta over gestational age at 0.55T.

T1 mapping and T1-weighted contrasts have a complimentary but currently under utilized role in fetal MRI. Emerging clinical low field scanners are ideally suited for fetal T1 mapping. The advantages are lower T1 values which results in higher efficiency and reduced field inhomogeneities resulting in a decreased requirement for specialist tools. In addition the increased bore size associated with low field scanners provides improved patient comfort and accessibility. This study aims to demonstrate the feasibility of fetal brain T1 mapping at 0.55T. An efficient slice-shuffling inversion-recovery echo-planar imaging (EPI)-based T1-mapping and postprocessing was demonstrated for the fetal brain at 0.55T in a cohort of 38 fetal MRI scans. Robustness analysis was performed and placental measurements were taken for validation. High-quality T1 maps allowing the investigation of subregions in the brain were obtained and significant correlation with gestational age was demonstrated for fetal brain T1 maps ( ) as well as regions-of-interest in the deep gray matter and white matter. Efficient, quantitative T1 mapping in the fetal brain was demonstrated on a clinical 0.55T MRI scanner, providing foundations for both future research and clinical applications including low-field specific T1-weighted acquisitions.

Efficient brain tumor grade classification using ensemble deep learning models

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental to the diagnosis because it contains detailed structural views of the brain, which is vital in identifying any of its abnormalities. The other option of performing an invasive biopsy is very painful and uncomfortable, which is not the case with MRI as it is free from surgically invasive margins and pieces of equipment. This helps patients to feel more at ease and hasten the diagnostic procedure, allowing physicians to formulate and practice action plans quicker. It is very difficult to locate a human brain tumor by manual because MRI scans produce large numbers of three-dimensional images. Complete applicability of pre-written computerized diagnostics, affords high possibilities in providing areas of interest earlier through the application of machine learning techniques and algorithms. The proposed work in the present study was to develop a deep learning model which will classify brain tumor grade images (BTGC), and hence enhance accuracy in diagnosing patients with different grades of brain tumors using MRI. A MobileNetV2 model, was used to extract the features from the images. This model increases the efficiency and generalizability of the model further. In this study, six standard Kaggle brain tumor MRI datasets were used to train and validate the developed and tested model of a brain tumor detection and classification algorithm into several types. This work consists of two key components: (i) brain tumor detection and (ii) classification of the tumor. The tumor classifications are conducted in both three classes (Meningioma, Pituitary, and glioma) and two classes (malignant, benign). The model has been reported to detect brain tumors with 99.85% accuracy, to distinguish benign and malignant tumors with 99.87% accuracy, and to type meningioma, pituitary, and glioma tumors with 99.38% accuracy. The results of this study indicate that the described technique is useful in the detection and classification of brain tumors.

Choroid Plexus Volume in Pediatric-Onset Multiple Sclerosis.

Recent studies suggest that the choroid plexus (CP) may function as a site of access of inflammatory cells into the CNS in multiple sclerosis (MS). Pediatric-onset MS (POMS) is characterized by a high inflammatory burden, as evidenced by a high relapse rate and volume of T2 lesions, making patients with POMS an informative population to evaluate choroid plexus volume (CPV). The objectives of the study were (1) to evaluate CPV at symptom onset in participants with POMS compared with healthy controls (HCs); (2) to evaluate changes in CPV in the first year of disease in participants with POMS; and (3) to evaluate associations between CPV, brain volumes, relapse activity, and disability in participants with POMS. Baseline 1.5T MRI scans were acquired from 23 participants with POMS and 23 age-matched and sex-matched HCs; 18 participants with POMS also had 12-month follow-up MRI scans. The CP of the lateral ventricles was segmented manually. CP and brain structure volumes were normalized for total intracranial volume. The number of relapses, T2 and gadolinium-enhancing T1 lesion counts, and Expanded Disability Status Scale (EDSS) scores at 12 months were also analyzed. Baseline CPVs were compared between groups using the Wilcoxon exact test, and CPV change from baseline to 12 months in participants with POMS was compared using the Wilcoxon signed-rank test. The relationship between CPV and brain volumetric measures, T2 lesion volumes, lesion count, number of relapses, and EDSS scores was assessed through Spearman correlation. The median normalized CPV was 1.51 × 10-3 (interquartile range [IQR]: 1.32-1.76) in POMS baseline scans and 1.21 × 10-3 (IQR: 1.1-1.47) in HC scans (p = 0.001). CPV did not significantly change at 12 months in the 18 participants with POMS with follow-up scans (p = 0.352). CPV in participants with POMS and HCs correlated with lateral ventricular volume (p = 0.012 for both groups) but did not correlate with brain and T2 lesion volumes or lesion count at baseline, nor with relapse activity or EDSS scores at 12 months in participants with POMS. CPV measured at baseline is greater in participants with POMS than in HCs. Baseline CPV did not predict higher disease activity or worse neurologic outcomes over 1 year. While higher CPV may be an early feature of inflammation in MS, its strong correlation with ventricular volumes could also reflect enlargement secondary to the mechanical attachment of CP to the ventricular wall.

Feasibility of Acoustic Features of Vowel Sounds in Estimating the Upper Airway Cross Sectional Area during Wakefulness: A Pilot Study

Assessment of upper airway dimensions has shown great promise in understanding the pathogenesis of obstructive sleep apnea (OSA). However, the current screening system for OSA does not have an objective assessment of the upper airway. The assessment of the upper airway can accurately be performed by MRI or CT scans, which are costly and not easily accessible. Acoustic pharyngometry or Ultrasonography could be less expensive technologies, but these require trained personnel which makes these technologies not easily accessible, especially when assessing the upper airway in a clinic environment or before surgery. In this study, we aimed to investigate the utility of vowel articulation in assessing the upper airway dimension during normal breathing. To accomplish that, we measured the upper airway cross-sectional area (UA-XSA) by acoustic pharyngometry and then asked the participants to produce 5 vowels for 3 seconds and recorded them with a microphone. We extracted 710 acoustic features from all vowels and compared these features with UA-XSA and developed regression models to estimate the UA-XSA. Our results showed that Mel frequency cepstral coefficients (MFCC) were the most dominant features of vowels, as 7 out of 9 features were from MFCC in the main feature set. The multiple regression analysis showed that the combination of the acoustic features with the anthropometric features achieved an R2 of 0.80 in estimating UA-XSA. The important advantage of acoustic analysis of vowel sounds is that it is simple and can be easily implemented in wearable devices or mobile applications. Such acoustic-based technologies can be accessible in different clinical settings such as the intensive care unit and can be used in remote areas. Thus, these results could be used to develop user-friendly applications to use the acoustic features and demographical information to estimate the UA-XSA.

Neuro-Immune Communication at the Core of Craving-Associated Brain Structural Network Reconfiguration in Methamphetamine Users

Methamphetamine (MA) use disorder is a chronic neurotoxic brain disease characterized by a high risk of relapse driven by intense cravings. However, the neurobiological signatures of cravings remain unclear, limiting the effectiveness of various treatment methods. Diffusion MRI (dMRI) scans from 62 MA users and 57 healthy controls (HC) were used in this study. The MA users were longitudinally followed up during their period of long-term abstinence (duration of long-term abstinence: 347.52±99.25 days). We systematically quantified the control ability of each brain region for craving-associated state transitions using network control theory from a causal perspective. Craving-associated structural alterations (CSA) were investigated through multivariate group comparisons and biological relevance analysis. The neural mechanisms underlying CSA were elucidated using transcriptomic and neurochemical analyses. We observed that long-term abstinence-induced structural alterations significantly influenced the state transition energy involved in the cognitive control response to external information, which correlated with changes in craving scores (r ∼ 0.35, P <0.01). Our causal network analysis further supported the crucial role of the prefrontal cortex (PFC) in craving mechanisms. Notably, while the PFC is central to the craving, the CSAs were distributed widely across multiple brain regions (PFDR<0.05), with strong alterations in somatomotor regions (PFDR<0.05) and moderate alterations in high-level association networks (PFDR<0.05). Additionally, transcriptomic, chemical compounds, cell-type analyses, and molecular imaging collectively highlight the influence of neuro-immune communication on human craving modulation. Our results offer an integrative, multi-scale perspective on unraveling the neural underpinnings of craving and suggest that neuro-immune signaling may be a promising target for future human addiction therapeutics.

Risk of late radiation necrosis more than 5 years after stereotactic radiosurgery.

Radiation necrosis (RN) is a well-recognized late complication most commonly occurring within 2 years of stereotactic radiosurgery (SRS); however, late RN (LRN), RN occurring or recurring > 5 years after SRS, has been poorly described. This study analyzes the incidence of and risk factors for LRN occurring > 5 years after SRS. This retrospective analysis included patients treated with linear accelerator-based SRS for tumors or arteriovenous malformations with > 5 years of clinical and serial MRI follow-up. LRN was defined as new neurological symptoms with neuroanatomically correlated imaging findings without disease recurrence. Univariate and multivariate analyses for LRN were performed using the Cox proportional hazards model. The authors identified a cumulative 297 lesions in 219 patients treated to a median dose of 17 Gy with a median follow-up of 7.4 years. In total, 290 (97.6%) lesions were treated in a single fraction, and 64 (21.5%) were treated after resection. The LRN occurred in 19 (8.7%) patients and in 23 (7.7%) lesions at a median of 6.1 years (range 5.1-13.9 years) after SRS. Fifteen of the 23 (65.2%) lesions were managed with steroids, bevacizumab, and/or antiepileptic drugs. The remaining 8 (34.8%) were resected; histopathology confirmed necrosis without disease recurrence in each. On multivariate analysis, only > 5-cm3 volume of the brain receiving 12 Gy (brain V12Gy) (HR 6.01, 95% CI 1.77-20.48; p = 0.004) and a history of early, previously resolved RN (HR 9.53, 95% CI 2.00-45.61; p = 0.005) remained significantly associated with LRN. RN risk persists well beyond 5 years after SRS, and recognizing LRN as an entity has important implications in managing these patients. LRN risk was highest in those with a brain V12Gy > 5 cm3 and a history of early RN after SRS, warranting close follow-up in perpetuity for select patients.

CSF Parvalbumin Levels at Multiple Sclerosis Diagnosis Predict Future Worse Cognition, Physical Disability, Fatigue, and Gray Matter Damage.

Cognitive impairment (CI) in multiple sclerosis (MS) is frequent and determined by a complex interplay between inflammatory and neurodegenerative processes. We aimed to investigate whether CSF parvalbumin (PVALB), measured at the time of diagnosis, may have a prognostic role in patients with MS. In this cohort study, CSF analysis of PVALB and Nf-L levels was performed on all patients at diagnosis (T0) and combined with physical, cognitive, and MRI assessment after an average of 4 years of follow-up (T4) from diagnosis. Cognitive performance was evaluated with a comprehensive neuropsychologic battery: both global (cognitively normal, CN, mildly CI, mCI, and severely CI, sCI) and domain cognitive status (normal/impaired in memory, attention/information processing speed, and executive functions) were considered. Cortical thickness and gray matter volume data were acquired using 3T MRI scanner. A total of 72 patients with MS were included. At diagnosis, PVALB levels were higher in those patients who showed a worsening physical disability after 4 years of follow-up (p = 0.011). CSF PVALB levels were higher in sCI patients than in CN (p = 0.033). Moreover, higher PVALB levels significantly correlated with worse global cognitive (p = 0.024) and memory functioning (p = 0.044). A preliminary clinical threshold for PVALB levels at diagnosis was proposed (2.57 ng/mL), which maximizes the risk of showing CI (in particular, sCI) at follow-up, with a sensitivity of 91% (specificity 30%). No significant results were found for these associations with Nf-L. In addition, patients with higher levels of PVALB at diagnosis showed higher cognitive (p = 0.024) and global fatigue (p = 0.043) at follow-up. Finally, higher PVALB levels also correlated significantly with more pronounced CTh/volume at T4 in the inferior frontal gyrus (p = 0.044), postcentral gyrus (p = 0.025), frontal pole (p = 0.042), transverse temporal gyrus (p = 0.008), and cerebellar cortex (p = 0.041) and higher atrophy (change T0-T4) in the right thalamus (p = 0.038), pericalcarine cortex (p = 0.009), lingual gyrus (p = 0.045), and medial frontal gyrus (p = 0.028). The significant association found between parvalbumin levels in the CSF at diagnosis and cognitive, clinical, and neuroradiologic worsening after 4 years of follow-up support the idea that parvalbumin, in addition to Nf-L, might represent a new potential prognostic biomarker, reflecting MS neurodegenerative processes occurring since early disease stages.

External Validation of a Previously Developed Deep Learning-based Prostate Lesion Detection Algorithm on Paired External and In-House Biparametric MRI Scans.

Purpose To evaluate the performance of an artificial intelligence (AI) model in detecting overall and clinically significant prostate cancer (csPCa)-positive lesions on paired external and in-house biparametric MRI (bpMRI) scans and assess performance differences between each dataset. Materials and Methods This single-center retrospective study included patients who underwent prostate MRI at an external institution and were rescanned at the authors' institution between May 2015 and May 2022. A genitourinary radiologist performed prospective readouts on in-house MRI scans following the Prostate Imaging Reporting and Data System (PI-RADS) version 2.0 or 2.1 and retrospective image quality assessments for all scans. A subgroup of patients underwent an MRI/US fusion-guided biopsy. A bpMRI-based lesion detection AI model previously developed using a completely separate dataset was tested on both MRI datasets. Detection rates were compared between external and in-house datasets with use of the paired comparison permutation tests. Factors associated with AI detection performance were assessed using multivariable generalized mixed-effects models, incorporating features selected through forward stepwise regression based on the Akaike information criterion. Results The study included 201 male patients (median age, 66 years [IQR, 62-70 years]; prostate-specific antigen density, 0.14 ng/mL2 [IQR, 0.10-0.22 ng/mL2]) with a median interval between external and in-house MRI scans of 182 days (IQR, 97-383 days). For intraprostatic lesions, AI detected 39.7% (149 of 375) on external and 56.0% (210 of 375) on in-house MRI scans (P < .001). For csPCa-positive lesions, AI detected 61% (54 of 89) on external and 79% (70 of 89) on in-house MRI scans (P < .001). On external MRI scans, better overall lesion detection was associated with a higher PI-RADS score (odds ratio [OR] = 1.57; P = .005), larger lesion diameter (OR = 3.96; P < .001), better diffusion-weighted MRI quality (OR = 1.53; P = .02), and fewer lesions at MRI (OR = 0.78; P = .045). Better csPCa detection was associated with a shorter MRI interval between external and in-house scans (OR = 0.58; P = .03) and larger lesion size (OR = 10.19; P < .001). Conclusion The AI model exhibited modest performance in identifying both overall and csPCa-positive lesions on external bpMRI scans. Keywords: MR Imaging, Urinary, Prostate Supplemental material is available for this article. © RSNA, 2024.

Multi-parameter MRI radiomics model in predicting postoperative progressive cerebral edema and hemorrhage after resection of meningioma

BackgroundPostoperative progressive cerebral edema and hemorrhage (PPCEH) are major complications after meningioma resection, yet their preoperative predictive studies are limited. The aim is to develop and validate a multiparametric MRI machine learning model to predict PPCEH after meningioma resection.MethodsThis retrospective study included 148 patients with meningioma. A stratified three-fold cross-validation was used to split the dataset into training and validation sets. Radiomics features from the tumor enhancement (TE) and peritumoral brain edema (PTBE) regions were extracted from T1WI, T2WI, and ADC maps. Support vector machine constructed different radiomics models, and logistic regression explored clinical risk factors. Prediction models, integrating clinical and radiomics features, were evaluated using the area under the curve (AUC), visualized in a nomogram.ResultsThe radiomics model based on TE and PTBE regions (training set mean AUC: 0.85 (95% CI: 0.78–0.93), validation set mean AUC: 0.77 (95%CI: 0.63–0.90)) outperformed the model with TE region solely (training set mean AUC: 0.83 (95% CI: 0.76–0.91), validation set mean AUC: 0.73 (95% CI: 0.58–0.87)). Furthermore, the combined model incorporating radiomics features, and clinical features of preoperative peritumoral edema and tumor boundary adhesion, had the best predictive performance, with AUC values of 0.87 (95% CI: 0.80–0.94) and 0.84 (95% CI: 0.72–0.95) for the training and validation set.ConclusionsWe developed a novel model based on clinical characteristics and multiparametric radiomics features derived from TE and PTBE regions, which can accurately and non-invasively predict PPCEH after meningioma resection. Additionally, our findings suggest the crucial role of PTBE radiomics features in understanding the potential mechanisms of PPCEH.

Self-supervised learning for denoising of multidimensional MRI data.

To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image. Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability. The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images. The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

Brain Tumor Detection and Classification Using MRI Images

Abstract: Brain tumors pose a serious health risk, and prompt and precise identification is essential for successful treatment. This article describes an automated method employing convolutional neural networks (CNNs) to identify and categorize brain cancers in MRI images. The dataset that was used consists of 7,023 MRI scans that were obtained from three different sources: the Br35H dataset, the SARTAJ dataset, and the fig share repository. Four classes are identified from the images: pituitary tumors, gliomas, meningioma’s, and no tumor. With the use of this improved dataset, the CNN model was created and trained, ultimately attaining a 97.5% test accuracy. Moreover, class-wise analysis showed that the precision values for pituitary tumors were 96.1%, 98.8%, 96.1%, and 98.6%, respectively, with equivalent recall rates of 94.7%, 95.4%, 100%, and 99% for gliomas, meningioma’s, and no tumors. With values of 0.966 for gliomas, 0.957 for meningioma’s, 0.994 for no tumor, and 0.975 for pituitary tumors, the F1-scores demonstrate the general resilience of the model. These findings show that the suggested CNNbased method is effective in assisting with brain tumor diagnosis from MRI scans, providing radiologists and other medical practitioners with a useful tool. In order to improve diagnostic accuracy, future study may entail enhancing the model even further and investigating the integration of other imaging modalities

Association among raised intraventricular pressure, clinical signs, and magnetic resonance imaging findings in dogs with congenital internal hydrocephalus.

Dogs with internal hydrocephalus do not necessarily have high intraventricular pressure (IVP). Not all reported MRI findings indicate high IVP and some clinical signs might be associated with elevated IVP and syringomyelia. Fifty-three dogs. Cross-sectional study. Clinical signs and MRI findings were evaluated for an association of IVP >12 mm Hg and syringomyelia. High IVP was associated with obtundation OR 4.64 (95% CI 1.27-16.93) (P = .02), head tilt OR 6.42 (95% CI 1.08-37.97) (P = .04) and nystagmus OR 8.24 (95% CI 1.44-47.07) (P = .02). Pain was associated with syringomyelia OR 3.4 (95% CI 0.98-11.78) (P = .05). The number of affected ventricles was associated with high IVP OR 2.85 (95% CI 0.97-8.33) (P = .05) and syringomyelia OR 12.74 (95% CI 2.93-55.4) (P = .0007). Periventricular edema OR 24.46 (95% CI 4.54-131.77), OR 7.61 (95% CI 1.91-30.32) (P < .0002, P = .004) and signal void sign OR 17.34 (95% CI 4.01-74.95), OR 4.18 (95% CI 1.16-15.02) (P < .0001, P = .03) were associated with high IVP and syringomyelia. The probability for syringomyelia is lower with disruption of the internal capsule OR 0.19 (95% CI 0.05-0.72) (P = .01) and higher VBR OR 0.25 (95% CI 0.1-0.63) (P = .004). Previously reported MRI findings are not predictive of high IVP. Clinical signs and MRI findings should be used to make a diagnosis of internal hydrocephalus in dogs with or without high IVP.

Comparison between 1.5 and 3-Tesla MRI findings in Ménière’s disease

Introduction3 T-MRI of the inner ear has been used to identify the endolymphatic hydrops (EH) phenomenon, and less frequently 1.5 T-MRI. The aim of this study was to assess whether there was agreement between findings of EH at 1.5 T MRI and those obtained at 3.0 T MRI in patients clinically diagnosed with definite Meniere disease (MD).MethodsCross-sectional, blinded study was conducted in a tertiary neurotology ambulatory practice. Thirty patients with clinical diagnosis of unilateral definite MD was included. Two MRI exams (1.5 T and 3.0 T) were performed for each patient and were evaluated by two examiners (E1, E2) who were blinded to the symptomatic ear. An analysis of intra-and inter-examiner agreement was performed. It was determined whether there was an association between MRI findings and disease duration, symptom severity, and MD clinical stage.ResultsE1 found EH at 3 T-MRI in 26 (86.66%) patients and at 1.5 T-MRI in 25 (83.33%). E2 found EH in 25 (83.33%) patients in 3 T-MRI and in 22 (73.33%) at 1.5 T-MRI. The agreement between the examiners’ assessments in relation to the EH was high (0.844) for the 3 T MRI and substantial for the 1.5 T, both statistically significant. There was no statistically significant relationship between EH imaging findings and clinical disease severity and course.Discussion1.5 T and 3.0 T MRI images agreed regarding the findings of absence or presence of cochlear hydrops (CH) and vestibular hydrops (VH). The degrees of CH and VH found at 3.0 T MRI in symptomatic ear were not associated with clinical aspects and the stage of disease.

Experimental study on mechanical and durability properties of concrete incorporating various polyvinyl alcohol fiber lengths and dosages

To inquire about the properties of concrete reinforced with polyvinyl alcohol (PVA) fiber (various fiber lengths and dosages), different experimental tests including mechanical property, cracking resistance, and chloride resistance were investigated. The overall performance of PVA fiber-reinforced concrete (FRC) was innovatively analyzed integrating mechanical indicators and crack resistance parameters. Furthermore, nuclear magnetic resonance (NMR) and scanning electron microscope (SEM) were selected to analyze the causes and mechanisms underlying the alterations in the performance of PVA-FRC. The experimental results demonstrate that the flexural strength, the crack resistance characteristic and chloride ion penetration resistance of PVA-FRC are significantly improved compared to ordinary concrete. Increasing fiber length plays a key role in flexural strength, compared with fiber dosage. Considering both mechanical properties and durability, PVA-FRC containing 0.25% volume fraction of 12 mm PVA fibers (F12-0.25) demonstrated optimal performance.

Investigation of intrafractional spinal cord and spinal canal movement during stereotactic MR-guided online adaptive radiotherapy for kidney cancer.

This study aimed to investigate the intrafractional movement of the spinal cord and spinal canal during MR-guided online adaptive radiotherapy (MRgART) for kidney cancer. All patients who received stereotactic MRgART for kidney cancer between February 2022 and February 2024 were included in this study. Patients received 30-42 Gy in 3-fraction MRgART for kidney cancer using the Elekta Unity, which is equipped with a linear accelerator and a 1.5 Tesla MRI. MRI scans were performed at three points during each fraction: for online planning, position verification, and posttreatment assessment. The spinal cord was contoured from the upper edge of Th12 to the medullary cone, and the spinal canal was contoured from Th12 to L3, using the first MRI. These contours were adjusted to the second and third MR images via deformable image registration, and movements were measured. Margins were determined via the formula "1.3×Σ+0.5×σ" and 95% prediction intervals. A total of 22 patients (66 fractions) were analyzed. The median interval between the first and third MRI scans were 38 minutes. The mean ± standard deviation of the spinal cord movements after this interval were -0.01 ± 0.06 for the x-axis (right-left), 0.01 ± 0.14 for the y-axis (caudal-cranial), 0.07 ± 0.05 for the z-axis (posterior-anterior), and 0.15 ± 0.08 for the 3D distance, respectively. The correlation coefficients of the 3D distance between the spinal cord and the spinal canal was high (0.92). The calculated planning organ at risk volume margin for all directions was 0.11 cm for spinal cord. The 95% prediction intervals for the x-axis, y-axis, and z-axis were -0.11-0.09 cm, -0.23-0.25 cm and -0.14-0.03 cm, respectively. Margins are necessary in MRgART to compensate for intrafractional movement and ensure safe treatment delivery.

Machine learning techniques for diagnosis of rare diseases from medical images

Introduction/Background: Rare diseases are life-threatening or chronically debilitating conditions that affect a small percentage of the population. Early and accurate diagnosis of rare diseases is challenging due to their complexity and limited understanding. Conventional diagnostic methods are often inadequate, time-consuming, and expensive. Machine learning (ML) has emerged as a promising technique for the analysis of medical images to support the diagnosis of various diseases including rare disorders. ML algorithms can learn complex patterns from large medical imaging datasets to aid clinicians in disease diagnosis, prognosis, and detection of complications. Materials and Methods: A structured search was conducted in electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar to identify peer-reviewed articles published between 2013 to 2023 related to ML-based diagnosis of rare diseases from medical images. A total of 187 articles were identified after removing duplicates. The titles and abstracts of retrieved articles were screened to determine their relevance based on the research objective. Finally, 51 full-text articles were selected for the final review. The key data extracted from selected studies included diseases, imaging modalities, ML algorithms, performance metrics, datasets, and limitations. Results: Most studies evaluated deep learning-based ML techniques, with convolutional neural networks (CNN) being the most applied algorithm. CNNs were mainly used to classify rare diseases based on specific imaging features in X-rays, CT, and MRI scans. Diseases frequently investigated included cardiovascular, neurological, and rare genetic disorders. Publicly available datasets such as Deep Lesion, MSD, and ChestX-ray14 were commonly utilized. Most studies reported high classification accuracy ranging from 80-95% on test datasets. However, limited generalizability due to small private datasets and lack of external validation were limitations. Discussion: The results demonstrate the potential of deep learning for the image-based diagnosis of rare diseases. Features learned from large imaging data sets enabled CNNs to distinguish subtle abnormalities. Hybrid models combining CNNs with other techniques like recurrent neural networks further improved performance. Overall, ML showed promise as a decision support tool. However, more multi-center validation studies are needed using larger and diverse datasets before clinical adoption. Standardization of performance metrics and reporting is also required to establish reliability and generalizability. Conclusion: This comprehensive review provided insights into ongoing research applying ML to medical images for rare disease diagnosis. While initial results are encouraging, further work is still necessary before ML can be reliably used in clinical decision making. Larger collaborative efforts and open-source datasets are needed to advance the field. Standardization of ML frameworks can also promote reproducible research and comparison of different methodologies.

Measurement of Healthy Adult Brain Temperature Using 1HMagnetic Resonance Spectroscopy Thermometry.

The purpose of this study is to measure the brain temperature (Tbr) by using 1Hmagnetic resonance spectroscopy (1HMRS) thermometry and investigate its age and gender differences in healthy adults. The brain temperature was further compared with the body temperature (Tbo) to investigate the possible existence of brain-body temperature gradient (∆T). Atotal of 80subjects were included in this study. 1HMRS data were collected on a3.0TMR scanner using Point Resolved Selective Spectroscopy (PRESS) sequence. Voxels were positioned in the right frontal (RF) lobe and left frontal (LF) lobe, respectively. The temperature of each voxel was calculated by chemical shift difference (∆δ) between H2O and NAA which was obtained by LCModel software. The average temperature of bilateral frontal lobe voxels was defined as Tbr for each subject. The average forehead temperature was acquired before MR scanning, defined as Tbo, in this study. The difference between Tbr and Tbo, denoted as the brain-body temperature gradient (∆T), was calculated. Age and gender characteristics of Tbr, ∆T and Tbo were analyzed. Tbr (38.51 ± 0.59℃) was higher than Tbo (36.47 ± 0.26℃) (P < 0.05). Negative correlations were observed between Tbr and age (r = -0.49, P < 0.05) and between ∆T and age (r = -0.44, P < 0.05), whereas no correlation existed between Tbo and age (r = -0.03, P = 0.79). Our observation demonstrated that the brain temperature, derived from 1HMRS thermometry, is significantly higher than the body temperature, indicating the existence of abrain-body temperature gradient, and the brain temperature gradually decreases with age.