Routine Magnetic Resonance Imaging Research Articles

Magnetic resonance imaging phantom-based S1 vertebral scores are indicators of fat-water-like osteoporotic changes in postmenopausal women: a pilot study.

A prospective study. To assess fat-water-like tissue changes on the 1st sacral vertebra using novel magnetic resonance imaging (MRI) phantombased F- and W-scores and evaluate their diagnostic performances in osteoporosis detection. Using an uncommonly advanced MRI technique, previous studies have found that fat-water changes were consistent with osteoporosis. The role of routine MRI sequences can be extended in this regard. The S1 vertebra is considered a crucial anatomical site in spine surgeries because it seldom suffers from fractures. Thus, S1 could indicate osteoporotic fat-water changes. Forty-two female volunteers (aged 62.3±6.3 years) underwent spine examination with both MRI (including a phantom) and dual-energy X-ray absorptiometry (DXA) following ethical approval. MRI phantom-based F- and W-scoreS1 were defined by normalizing S1 vertebral signal intensities (SIs) by coconut oil and water SIs of the phantom on T1- and T2-weighted imaging, respectively. Using receiver operating characteristic analysis, the diagnostic performances of the new scores for evaluating osteoporosis and vertebral fractures were investigated against standard areal bone mineral density measured with DXA (DXA-aBMD). The F-scoreS1 and W-scoreS1 were greater (4.11 and 2.43, respectively) in patients with osteoporosis than those without osteoporosis (3.25 and 1.92, respectively) and achieved areas under the curve (AUCs) of 0.82 and 0.76 (p<0.05), respectively, for osteoporosis detection. Similarly, the mean F-scoreS1 and W-scoreS1 were higher (4.11 and 2.63, respectively) in patients with vertebral fractures than in those without fractures (3.30 and 1.82, respectively) and had greater AUCs (0.90 for W-scoreS1 and 0.74 for F-scoreS1) than DXA-aBMD (AUC, 0.26; p<0.03). In addition, the F- and W-scoreS1 demonstrated a strong correlation (r=0.65, p<0.001). The new S1 vertebral-based MRI scores were developed to detect osteoporotic changes and demonstrated improvements over DXA-aBMD in differentiating patients with vertebral fractures.

Cardiac Amyloidosis Masquerading as Hypertrophic Obstructive Cardiomyopathy: Co-existence or Coincidence?

Abstract A 59-year-old male presented with syncope and nonsustained ventricular tachycardia and was diagnosed to have hypertrophic obstructive cardiomyopathy. He underwent dual-chamber implantable converter defibrillator (ICD) insertion. Following ICD insertion, in view of some red flags, routine cardiac magnetic resonance imaging was performed, which showed biventricular hypertrophy, diffuse subendocardial gadolinium enhancement of both the ventricles, prolonged T1 and T2, and left ventricular extracellular volume fraction of 50%. A pyrophosphate scan of the heart was strongly positive both at 1 and 3 h intervals. M-band was observed on plasma protein electrophoresis. Serum and urinary immunofixation studies suggested lambda light-chain gammopathy. Multiparametric flow cytometry of the bone marrow aspirate revealed 20% plasma cells immunophenotyped to produce only light-chain immunoglobulins. The patient is on specific chemotherapy for the past 7 months and is asymptomatic.

Rare case of tentorial cavernous malformation mimicking a meningioma: illustrative case.

Cavernous malformations of the dura, especially of the tentorium, are exceedingly rare. In the available literature, only 10 cases have been described to date. The authors present the case of a 46-year-old male patient with a 1-cm infratentorial lesion suspicious for meningioma that was found on routine magnetic resonance imaging (MRI) performed for vertigo. The lesion was followed for 1.5 years with no change in signal and size. Nevertheless, the patient was concerned about the lesion and requested removal. The removal was successful and without any neurological sequelae. However, histological evaluation demonstrated a cavernous malformation. Postoperative computed tomography and MRI showed complete removal. Preoperative MRI characteristics, intraoperative images, and a video, as well as histological evaluation, are shown. The case is discussed with respect to the literature. Cavernous malformations of the tentorium are extremely rare and mimic meningiomas; thus, they need to be taken into account. DOTATOC positron emission tomography may help to differentiate in these cases. Considering the cases reported in the literature, in cases of large tumors, preoperative angiography and possibly embolization may be helpful. https://thejns.org/doi/10.3171/CASE24168.

Brain and Ventricle Volume Alterations in Idiopathic Normal Pressure Hydrocephalus Determined by Artificial Intelligence-Based MRI Volumetry.

The aim of this study was to employ artificial intelligence (AI)-based magnetic resonance imaging (MRI) brain volumetry to potentially distinguish between idiopathic normal pressure hydrocephalus (iNPH), Alzheimer's disease (AD), and age- and sex-matched healthy controls (CG) by evaluating cortical, subcortical, and ventricular volumes. Additionally, correlations between the measured brain and ventricle volumes and two established semi-quantitative radiologic markers for iNPH were examined. An IRB-approved retrospective analysis was conducted on 123 age- and sex-matched subjects (41 iNPH, 41 AD, and 41 controls), with all of the iNPH patients undergoing routine clinical brain MRI prior to ventriculoperitoneal shunt implantation. Automated AI-based determination of different cortical and subcortical brain and ventricular volumes in mL, as well as calculation of population-based normalized percentiles according to an embedded database, was performed; the CE-certified software mdbrain v4.4.1 or above was used with a standardized T1-weighted 3D magnetization-prepared rapid gradient echo (MPRAGE) sequence. Measured brain volumes and percentiles were analyzed for between-group differences and correlated with semi-quantitative measurements of the Evans' index and corpus callosal angle: iNPH patients exhibited ventricular enlargement and changes in gray and white matter compared to AD patients and controls, with the most significant differences observed in total ventricular volume (+67%) and the lateral (+68%), third (+38%), and fourth (+31%) ventricles compared to controls. Global ventriculomegaly and marked white matter reduction with concomitant preservation of gray matter compared to AD and CG were characteristic of iNPH, whereas global and frontoparietally accentuated gray matter reductions were characteristic of AD. Evans' index and corpus callosal angle differed significantly between the three groups and moderately correlated with the lateral ventricular volumes in iNPH patients [Evans' index (r > 0.83, p ≤ 0.001), corpus callosal angle (r < -0.74, p ≤ 0.001)]. AI-based MRI volumetry in iNPH patients revealed global ventricular enlargement and focal brain atrophy, which, in contrast to healthy controls and AD patients, primarily involved the supratentorial white matter and was marked temporomesially and in the midbrain, while largely preserving gray matter. Integrating AI volumetry in conjunction with traditional radiologic measures could enhance iNPH identification and differentiation, potentially improving patient management and therapy response assessment.

Poster 105: MRI Demonstrates Similar Diagnostic Value to MR Arthrogram for Determining Labral Tear Extent up to 2 Weeks Following Shoulder Dislocation

Objectives: The aim of this study was to determine the comparative accuracy and precision of routine Magnetic Resonance Imaging (MRI) versus magnetic resonance (MR) arthrogram in measuring labral tear size as function of time from shoulder dislocation, and to identify a time threshold after which MR arthrogram supersedes MRI in diagnostic utility. Methods: We retrospectively evaluated consecutive patients who underwent primary arthroscopic labral repair between 2012 and 2021 and completed a preoperative MRI or MR arthrogram of the shoulder within 60 days of injury, and subsequently underwent arthroscopic repair within 6 months of imaging. Arthroscopic labral tear size and location was used as standard comparison. Three musculoskeletal radiologists independently interpreted tear extent using clock-face convention. Accuracy and precision of MR labral tear measurements were defined by location and size of the tear, respectively. Accuracy and precision were compared between MRI and MR arthrogram, as a function of time from dislocation. Results: Thirty-two MRI and 65 MR arthrogram (total n=97) were independently assessed by radiologists. Multivariate analysis demonstrated that intraoperative tear size, early imaging, and arthrogram status were associated with increased MR accuracy and precision (p&lt;0.05). Ordering surgeons preferred arthrogram for delayed imaging (p=0.018). For routine MRI, error in accuracy increased by 3.4° per day and error in precision increased by 2.3° per day (p&lt;0.001) from time of injury. MR arthrogram, however, was not temporally influenced (Figure 1). Significant loss of accuracy and precision of routine MRI compared to MR arthrogram occurs at 2 weeks after acute shoulder dislocation. Conclusions: For determining shoulder labral tear size after acute dislocation, routine MRI has similar diagnostic value for measuring labral tear extent as MR arthrogram up to approximately 2 weeks postacute shoulder dislocation, after which it significantly loses accuracy and precision, possibly due to resolving posttraumatic effusion. MR arthrogram accuracy and precision were not temporally influenced.

Medulloblastoma subgrouping at first sight

Recent incorporation of the four primary medulloblastoma subgroups into the WHO Classification of Central Nervous System Tumors necessitates globally accessible methods to discern subgroups. In this issue of Cancer Cell, Wang etal. develop a rapid and reliable machine learning workflow for pre-operative subgroup determination using routine magnetic resonance imaging.

An rs-fMRI based neuroimaging marker for adult absence epilepsy

ObjectiveApproximately 20–30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE. MethodsThis research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing. ResultsCompared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %. SignificanceAccording to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.

Diagnostic accuracy of ultrasound and MR imaging in peroneal neuropathy: A prospective, single-center study.

Magnetic resonance imaging (MRI) findings in peroneal neuropathy are not well documented and the prognostic value of imaging remains uncertain. Upper limits of cross-sectional area (CSA) on ultrasound (US) have been established, but uncertainty regarding generalizability remains. We aimed to describe MRI findings of the peroneal nerve in patients and healthy controls and to compare these results to US findings and clinical characteristics. We prospectively included patients with foot drop and electrodiagnostically confirmed peroneal neuropathy, and performed clinical follow-up, US and MRI of both peroneal nerves. We compared MRI findings to healthy controls. Two radiologists evaluated MRI features in an exploratory analysis after images were anonymized and randomized. Twenty-two patients and 38 healthy controls were included. Whereas significant increased MRI CSA values were documented in patients (mean CSA 20 mm2 vs. 13 mm2 in healthy controls), intra- and interobserver variability was substantial (variability of, respectively, 7 and 9 mm2 around the mean in 95% of repeated measurements). A pathological T2 hyperintense signal of the nerve was found in 52.6% of patients (50% interobserver agreement). Increased CSA measurements (MRI/US), pathological T2 hyperintensity of the nerve and muscle edema were not predictive for recovery. Imaging is recommended in all patients with peroneal neuropathy to exclude compressive intrinsic and extrinsic masses but we do not advise routine MRI for diagnosis or prediction of outcome in patients with peroneal neuropathy due to high observer variability. Further studies should aim at reducing MRI observer variability potentially by semi-automation.

Factors influencing diffusion tensor imaging of knee cartilage in children ages 6-12years: a prospective study.

Magnetic resonance diffusion tensor imaging (DTI) has recently been used to evaluate the developing cartilage of children, but the influencing factors have not been well studied. The objective of this study was to investigate the influence of the diffusion gradient strength (b value), diffusion gradient direction, age and sex on knee cartilage DTI in healthy children aged 6-12years. A total of 30 healthy child volunteers, with an average age of 8.9 ± 1.6 (mean ± standard deviation) years, were enrolled in this study. They were categorized into three groups according to their age range: 6-8years, 8-10years and 10-12years, ensuring equal sex distribution in each group (5 boys and 5 girls). These volunteers underwent routine left knee joint magnetic resonance imaging (MRI) and serial DTI scans. DTI parameters were altered as follows: when b value = 600s/mm2, diffusion gradient direction was set to 6, 15, 25, 35 and 45; and when diffusion gradient direction = 25, b value was set to 300, 600, 900 and 1200s/mm2. The values of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were separately acquired using image post-processing techniques. The correlation between various b values, diffusion gradient directions, age and sex on the one hand and FA and ADC values on the other, was investigated. (1) When diffusion gradient direction was fixed and the b value was varied, both FA and ADC exhibited a decreasing trend as the b value increased (P < 0.001). (2) When the b value was fixed and diffusion gradient direction was varied, the FA of knee cartilage showed a decreasing trend with increasing diffusion gradient direction (P < 0.001). (3) The FA value increased with age (P < 0.05). The b value, diffusion gradient direction value and age exert a significant impact on both FA and ADC values in MR DTI of knee cartilage in children aged 6-12years. In order to obtain a stable DTI, it is recommended to select a b value ≥ 600s/mm2 and a diffusion gradient direction ≥ 25 during scanning.

Meningioma grading via diagnostic imaging: A systematic review and meta-analysis

PurposeMeningioma is the most common intracranial tumor, graded on pathology using WHO criteria to predict tumor course and treatment. However, pathological grading via biopsy may not be possible in cases with poor surgical access due to tumor location. Therefore, our systematic review aims to evaluate whether diagnostic imaging features can differentiate high grade (HG) from low grade (LG) meningiomas as an alternative to pathological grading.MethodsThree databases were searched for primary studies that either use routine magnetic resonance imaging (MRI) or computed tomography (CT) to assess pathologically WHO-graded meningiomas. Two investigators independently screened and extracted data from included studies.Results24 studies met our inclusion criteria with 12 significant (p < 0.05) CT and MRI features identified for differentiating HG from LG meningiomas. Cystic changes in the tumor had the highest specificity (93.4%) and irregular tumor-brain interface had the highest positive predictive value (65.0%). Mass effect had the highest sensitivity (81.0%) and negative predictive value (90.7%) of all imaging features. Imaging feature with the highest accuracy for identifying HG disease was irregular tumor-brain interface (79.7%). Irregular tumor-brain interface and heterogenous tumor enhancement had the highest AUC values of 0.788 and 0.703, respectively.ConclusionOur systematic review highlight imaging features that can help differentiate HG from LG meningiomas.

Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

Intracranial cerebrovascular lesions on T2-weighted magnetic resonance imaging.

Magnetic resonance imaging (MRI) of the brain has been implemented to evaluate multiple intracranial pathologies. Non-contrast T2-weighted images are a routinely acquired sequence in almost all neuroimaging protocols. It is not uncommon to encounter various cerebrovascular lesions incidentally on brain imaging. Neuroradiologists should evaluate the routine T2-weighted images for incidental cerebrovascular lesions, irrespective of the primary indication of the study. Vascular structures typically demonstrate a low signal flow-void on the T2-weighted images. In our experience, large cerebrovascular abnormalities are easily visible to a typical neuroradiologist. In this article, we present the spectrum of the characteristic imaging appearance of various intracranial cerebrovascular lesions on routine non-contrast T2-weighted MRI. These include aneurysm, arteriovenous malformation, arterial occlusion, capillary telangiectasia, cavernous malformation, dural arteriovenous fistula, moyamoya, proliferative angiopathy, and vein of Galen malformation.

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency.

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de-identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re-identification protection but can significantly impact post-processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post-processing results by the price of higher re-identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1-weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de-identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re-identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post-processing results after defacing.

Automatic lesion detection at Multiple Sclerosis patients – Comparison of 2D- and 3D-FLAIR-datasets

BackgroundMultiple Sclerosis (MS) is a common autoimmune inflammatory disease of the central nervous system (CNS). Magnetic Resonance Imaging (MRI) allows a sensitive assessment of the CNS and is established for diagnostic, prognostic and (therapy-) monitoring purposes. Especially lesion counting in T2- or Fluid Attenuated Inversion Recovery (FLAIR)-weighted images plays a decisive role in clinical routine. Software-packages allowing an automatic evaluation of image data are increasingly established aiming a faster and improved workflow. These programs allow e.g. the counting, spatial attribution and volumetry of MS-lesions in FLAIR-weighted images. Research has shown that 3D-FLAIR-sequences are superior to 2D-FLAIR-sequences in visual evaluation of lesion burden in MS. An influence on the automatic analysis is expectable but not yet systematically studied. This work will therefore investigate the influence of 2D- and 3D datasets on the results of an automatic assessment. Material and methodsIn this prospective study, 80 Multiple Sclerosis patients underwent a clinically indicated routine MRI examination. The clinical routine protocol already including a 3D-FLAIR sequence was adapted by an additional 2D-FLAIR sequence also conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. To obtain a quantitative analysis for assessment of amount, dissemination and volume of the lesions, the acquired MR images were post-processed using the CE-certified Software mdbrain (mediaire, Berlin, Germany). The resulting data were statistically analysed using the paired t-test for normally distributed data and the Wilcoxon-signed-rank-test for not normally distributed data respectively. Demographic data and data such as the subtype, duration, severity and therapy of the disease were collected, pseudonymized and evaluated. ResultsThere is a significant difference concerning the total number and lesion volume with more lesions being detected (2D: 29.7, +/- 20.22 sd; 3D: 40.1 +/- 31.67 sd; p < 0.0001) but lower total volume (2D: 6.24 +/- 6.11 sd; 3D: 5.39 +/- 6.37 sd; p < 0.0001) when using the 3D- sequence. Especially significantly more small lesions in the unspecific white matter and infratentorial region were detected by using the 3D-FLAIR sequence (p < 0.0001) compared to the 2D-FLAIR image. Main reason for the lower total volume in the 3D-FLAIR sequence was the calculated volume for periventricular lesions which was significantly beneath the calculated volume from the 2D-FLAIR sequence (p < 0.0001). ConclusionAutomatic lesion counting and volumetry is feasible with both 2D- and 3D-weightend FLAIR images. Still, it leads to partly significant differences even between two sequences that both are conform to the 2021 MAGNIMS-CMSCNAIMS consensus recommendations. This study contributes valuable insights into the impact of using different input data from the same patient for automated MS lesion evaluation.

Contrast-enhanced 3D black-blood magnetic resonance imaging for diagnosis of cerebral venous thrombosis.

Cerebral venous thrombosis (CVT) is often under-recognized on routine magnetic resonance imaging (MRI) examinations without concomitant magnetic resonance venography (MRV). Contrast-enhanced black-blood MRI (BBMRI) based on a three-dimensional T1-weighted variable-flip-angle turbo spin echo sequence, one of the sequences used routinely in our practice, has the potential for detection of thrombi in patients with CVT. The aim of this study is to evaluate the diagnostic performance and enhancement patterns of contrast-enhanced three-dimensional BBMRI for the diagnosis of CVT. Contrast-enhanced BBMRI and contrast-enhanced MRV sequences of 64 patients, acquired from June 2018 to January 2021, were retrospectively reviewed by neuroradiologists for detection of CVT in each venous sinus segment. Diagnostic performance values were calculated for contrast-enhanced BBMRI based on enhancement patterns. Of 749 venous segments from 64 patients analyzed, CVT was demonstrated in 41 venous segments from 12 patients on contrast-enhanced MRV (CE MRV). Thick wall enhancement and total enhancement patterns were dominantly demonstrated in thrombosed segments. Compared with contrast-enhanced MRV, contrast-enhanced BBMRI had a patient-based sensitivity and specificity of 100% and 98.1%, respectively, and a segment-based sensitivity and specificity of 87.8% and 96.2%, respectively. The positive predictive value of contrast-enhanced BBMRI in detecting CVT was 92.3% (patient-based) and 57.1% (segment-based), and the negative predictive value was 100% (patient-based) and 99.3% (segment-based). Contrast-enhanced BBMRI has high diagnostic performance in detection and diagnosis of CVT. This sequence may be useful to recognize CVT when dedicated CE MRV was not performed in patients with nonspecific neurological symptoms.

Addition of quantitative MRI to the routine clinical care of patients with multiple sclerosis-Results from the MAGNON project.

The revised Lublin classification offers a framework for categorizing multiple sclerosis (MS) according to the clinical course and imaging results. Diagnosis of secondary progressive MS (SPMS) is often delayed by a period of uncertainty. Several quantitative magnetic resonance imaging (qMRI) markers are associated with progressive disease states, but they are not usually available in clinical practice. The MAGNON project enrolled 629 patients (early relapsing-remitting MS (RRMS), n=51; RRMS with suspected SPMS, n=386; SPMS, n=192) at 55 centers in Germany. Routine magnetic resonance imaging (MRI) scans at baseline and after 12 months were analyzed using a centralized automatic processing pipeline to quantify lesions and normalized brain and thalamic volume. Clinical measures included relapse activity, disability, and MS phenotyping. Neurologists completed questionnaires before and after receiving the qMRI reports. According to the physicians' reports, qMRI results changed their assessment of the patient in 31.8% (baseline scan) and 27.6% (follow-up scan). For ∼50% of patients with RRMS with suspected SPMS, reports provided additional information that the patient was transitioning to SPMS. In >25% of all patients, this information influenced the physicians' assessment of the patient's current phenotype. However, actual changes of treatment were reported only in a minority of these patients. The MAGNON results suggest that standardized qMRI reports may be integrated into the routine clinical care of MS patients and support the application of the Lublin classification as well as treatment decisions. The highest impact was reported in patients with suspected SPMS, indicating a potential to reduce diagnostic uncertainty.

Deep learning-based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk.

Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms. To enhance the diagnosis of CCH and non-CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision-making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes. In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1-weighted (T1WI) and T2-weighted (T2WI) sequence images for each patient. We propose CCH-Network (CCHNet), an end-to-end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state-of-the-art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency. CCHNet achieved an AUC of 91.6% (95% CI: 86.8-99.1), with an accuracy (ACC) of 85.0% (95% CI: 75.6-95.2). It demonstrated a sensitivity (SE) of 80.0% (95% CI: 71.6-95.6) and a specificity (SP) of 90.0% (95% CI: 82.3-97.8) in the testing cohort. In the validation cohort, the model demonstrated an AUC of 86.0% (95% CI: 80.3-93.0), an ACC of 84.2% (95% CI: 70.2-93.6), a SE of 83.3% (95% CI: 68.3-95.5), and a SP of 84.7% (95% CI: 70.3-96.8). The model improved the diagnostic performance of MRI with high SE and SP, providing a promising method for the diagnosis of CCH.

T1 and T2-mapping in pancreatic MRI: Current evidence and future perspectives

Conventional T1- and T2-weighted magnetic resonance imaging (MRI) of the pancreas can vary significantly due to factors such as scanner differences and pulse sequence variations. This review explores T1 and T2 mapping techniques, modern MRI methods providing quantitative information about tissue relaxation times. Various T1 and T2 mapping pulse sequences are currently under investigation. Clinical and research applications of T1 and T2 mapping in the pancreas include their correlation with fibrosis, inflammation, and neoplasms. In chronic pancreatitis, T1 mapping and extracellular volume (ECV) quantification demonstrate potential as biomarkers, aiding in early diagnosis and classification. T1 mapping also shows promise in evaluating pancreatic exocrine function and detecting glucose metabolism disorders. T2* mapping is valuable in quantifying pancreatic iron, offering insights into conditions like thalassemia major. However, challenges persist, such as the lack of consensus on optimal sequences and normal values for healthy pancreas relaxometry. Large-scale studies are needed for validation, and improvements in mapping sequences are essential for widespread clinical integration. The future holds potential for mixed qualitative and quantitative models, extending the applications of relaxometry techniques to various pancreatic lesions and enhancing routine MRI protocols for pancreatic pathology diagnosis and prognosis.

Assessing the Impact of Preoperative MRI-Based Vertebral Bone Quality Scores on 5-Year Prognosis in Lumbar Spine Surgery.

Retrospective cohort study. To assess the utility of preoperative vertebral bone quality (VBQ) scores in predicting the 5-year clinical outcomes following lumbar spine surgery. Osteoporosis poses a significant concern in older adults undergoing spinal surgery. The VBQ score, assessed through preoperative magnetic resonance imaging (MRI), is associated with subsequent osteoporotic fractures and postoperative complications. However, previous report on the impact of VBQ score on mid-term clinical outcomes after lumbar spine surgery remains lacking. A total of 189 patients who underwent lumbar surgery (≤3-disc levels) for lumbar spinal stenosis between 2010 and 2016 were enrolled. Patients were classified into high (>3.35), middle (2.75 to 3.35), and low (<2.73) VBQ score groups based on tertiles. Clinical scores, including Japanese Orthopedic Association Back Pain Evaluation Questionnaire (JOABPEQ) and Short Form-36 (SF-36), were recorded preoperatively and 1, 2, and 5 years postoperatively. Comparative analysis showed significant differences among the VBQ groups throughout the study period in low back pain (P=0.013), walking ability (P=0.005), social life function (P=0.010) of JOABPEQ, and physical component summary of the SF-36 (P=0.018) following lumbar spine surgery. A higher VBQ score was significantly correlated with worse 5-year postoperative outcomes for all domains except for lumbar function of the JOABPEQ using multiple linear regression analysis, adjusting for age, sex, BMI, hyperlipidemia, surgical procedures, and each preoperative score. A high preoperative VBQ score is a risk factor for poor 5-year clinical outcomes after lumbar spine surgery. Evaluation of the VBQ score through routine preoperative MRI facilitates osteoporotic screening in lumbar patients without radiation exposure and healthcare costs, while also demonstrating its potential as a prognostic indicator of postoperative clinical outcomes. 3.

Potential Advantage of Magnetic Resonance Imaging in Detecting Thoracic Wall Infiltration in Pleural Mesothelioma. A Retrospective Single Center Analysis.

Abstract Background Thoracic wall infiltration in pleural mesothelioma (PM) can be an important prognostic factor when determining the extent of resection. Currently, standardized imaging for restaging after neoadjuvant systemic therapy comprises contrast enhanced Computed Tomography (CT) or Positron Emission Tomography (PET/CT) scan. Aims However, thoracic Magnetic Resonance Imaging (MRI) could be a better discriminator of chest wall infiltration prior to surgery. To increase the preoperative staging accuracy, we introduced a MRI protocol at our center. Methods A retrospective analysis of this prospective new imaging protocol was performed from 07/2018 to 08/2023, including descriptive analysis for patient`s sex, age, nicotine consumption, asbestos exposure, histological subtype, TNM-stage, RECIST criteria and number of neoadjuvant therapy cycles. Preoperative restaging included routine imaging and MRI. After histological diagnosis of PM, neoadjuvant therapy was conducted, followed by partial pleurectomy or extended pleurectomy/decortication, with intraoperative biopsies of suspicious chest wall lesions. The CT/MRI results were compared to the intraoperative biopsies. Sensitivity and specificity of both modalities were analyzed. Results Twenty-five patients (mean age 65.4, 12% female) with possible chest wall infiltration were included out of the 35 patients with PM treated during the observation period. Of the 10 patients with actual chest wall infiltration, 9 (90%) had a T-Stage of 3 or higher, 9 (90%) had PM of epithelioid histologic subtype and 4 (40%) a R2-Resection. The mean overall survival of all patients was 18.88 months (8-58). In our study, thoracic MRI showed a high sensitivity (90%) and specificity (100%) for the detection of chest wall infiltration, especially when compared to the CT scan (sensitivity of 10%). Conclusion With the adjunctive use of thoracic MRI we demonstrated a higher sensitivity for detection of chest wall infiltration compared to conventional imaging prior to surgery. This may facilitate the preoperative assessment of the extent of resection. Nevertheless, larger studies are required to confirm these results.