Spin-lattice Relaxation Research Articles

Prediction of postoperative residual primary ovarian neoplasm or metastatic lesion close to rectum of serous ovarian carcinoma based on clinical and MR T1-DEI features.

The optimal primary debulking surgery outcome of serous ovarian carcinoma (SOC) is greatly affected by primary ovarian neoplasm or metastatic lesion close to the rectum. To study the risk factors affecting postoperative residual primary ovarian neoplasm or metastatic lesion close to the rectum of SOC. The clinical and MRI data of 164 patients with SOC eligible from institution A (training and test groups) and 36 patients with SOC eligible from institution B (external validation group) were collected and retrospectively analyzed. The clinical data included age, serum carbohydrate antigen 125 (CA-125), human epididymis protein 4, and neutrophil-to-lymphocyte ratio (NLR). Magnetic resonance imaging (MRI) data included ovarian mass distribution, maximum diameter of ovarian mass, ovarian mass features, degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion, and amount of ascites. A model was established using multivariate logistic regression. By univariate and multivariate logistic regressions, CA-125 (P = 0.024, odds ratio [OR] = 3.798, 95% confidence interval [CI] = 1.24-13.32), NLR (P = 0.037, OR = 3.543, 95% CI = 1.13-12.72), and degree of rectal invasion of the primary ovarian neoplasm or metastatic lesion (P < 0.001, OR = 37.723, 95% CI = 7.46-266.88) were screened as independent predictors. The area under the curve values of the model in the training, test, and external validation groups were 0.860, 0.764, and 0.778, respectively. The clinical-radiological model based on T1-weighted dual-echo MRI can be used non-invasively to predict postoperative residual ovarian neoplasm or metastasis close to SOC in the rectum.

Perovskite-structured ultrasensitive CaMnO3 nanoenzyme enables highly efficient ultrasound-amplified and catalysis-involved synergistic tumor therapy

Catalytic nanoenzyme-mediated therapy with reactive oxygen species generation feature and tumor microenvironment regulation characteristic has demonstrated high therapeutic efficacy in oncological field, whereas its low enzyme-mimicking reaction efficacy typically results in dissatisfied therapeutic effect and outcome. Herein, an ultrasensitive CaMnO3 (CMO) nanoenzyme has been rationally designed and engineered for enzyodynamic-calcium overload synergistic tumor therapy. CMO nanoenzyme with oxidase (OXD)-, peroxidase (POD)- and glutathione peroxidase (GPx)-mimic activities is responsible for the generation of reactive oxygen species, the consumption of intra-tumor glutathione and mitochondrial damage. For the evaluation of OXD and POD-mimicking activities, CMO with ultra low concentration (1 μg ml−1) can rapidly catalyze the color reaction of 3,3′,5,5′-tetramethylbenzidine (TMB) hydrochloride and reach the reaction termination in a very short time (1 s). Moreover, the release and overload of Ca2+ strengthen the tumor cell death. Exogenous ultrasound stimulation further amplifies the enzyodynamic-Ca2+ overload synergistic tumor-therapeutic efficacy. In vitro and in vivo results affirm the transcendent antineoplastic outcomes of CMO. Meanwhile, the existence of Mn2+ endows nanoenzyme with T1-weighted MR imaging capacity. This work provides new strategies for efficient enzyme-mimicking activity and tumor synergistic treatment based on new perspectives on the engineering of multifunctional therapeutic nanomedicine.

Impact of MRI radiomic feature normalization for prognostic modelling in uterine endometrial and cervical cancers

Widespread clinical use of MRI radiomic tumor profiling for prognostication and treatment planning in cancers faces major obstacles due to limitations in standardization of radiomic features. The purpose of the current work was to assess the impact of different MRI scanning- and normalization protocols for the statistical analyses of tumor radiomic data in two patient cohorts with uterine endometrial-(EC) (n = 136) and cervical (CC) (n = 132) cancer. 1.5 T and 3 T, T1-weighted MRI 2 min post-contrast injection, T2-weighted turbo spin echo imaging, and diffusion-weighted imaging were acquired. Radiomic features were extracted from within manually segmented tumors in 3D and normalized either using z-score normalization or a linear regression model (LRM) accounting for linear dependencies with MRI acquisition parameters. Patients were clustered into two groups based on radiomic profile. Impact of MRI scanning parameters on cluster composition and prognostication were analyzed using Kruskal–Wallis tests, Kaplan–Meier plots, log-rank test, random survival forests and LASSO Cox regression with time-dependent area under curve (tdAUC) (α = 0.05). A large proportion of the radiomic features was statistically associated with MRI scanning protocol in both cohorts (EC: 162/385 [42%]; CC: 180/292 [62%]). A substantial number of EC (49/136 [36%]) and CC (50/132 [38%]) patients changed cluster when clustering was performed after z-score-versus LRM normalization. Prognostic modeling based on cluster groups yielded similar outputs for the two normalization methods in the EC/CC cohorts (log-rank test; z-score: p = 0.02/0.33; LRM: p = 0.01/0.45). Mean tdAUC for prognostic modeling of disease-specific survival (DSS) by the radiomic features in EC/CC was similar for the two normalization methods (random survival forests; z-score: mean tdAUC = 0.77/0.78; LRM: mean tdAUC = 0.80/0.75; LASSO Cox; z-score: mean tdAUC = 0.64/0.76; LRM: mean tdAUC = 0.76/0.75). Severe biases in tumor radiomics data due to MRI scanning parameters exist. Z-score normalization does not eliminate these biases, whereas LRM normalization effectively does. Still, radiomic cluster groups after z-score- and LRM normalization were similarly associated with DSS in EC and CC patients.

Reliability and changes in knee cartilage T2 relaxation time from 6 to 24 months after anatomic anterior cruciate ligament reconstruction.

The objectives of this study were to evaluate the reliability of cartilage T2 relaxation time measurements and to identify focal changes in T2 relaxation on the affected knee from 6 to 24 months after anatomic anterior cruciate ligament reconstruction (ACLR). Data from 41 patients who received anatomic ACLR were analyzed. A bilateral 3.0-T MRI was acquired 6 and 24 months after ACLR. T2 relaxation time was measured in subregions of the femoral condyle and the tibial plateau. The root-mean-square coefficient of variation (RMSCV) was calculated to evaluate the reliability of T2 relaxation time in the contralateral knee. Subregion changes in the affected knee T2 relaxation time were identified using the contralateral knee as a reference. The superficial and full thickness layers of the central and inner regions showed good reliability. Conversely, the outer regions on the femoral side and regions in the deep layers showed poor reliability. T2 relaxation time increased in only 3 regions on the affected knee when controlling for changes in the contralateral knee, while changes in T2 relaxation time were identified in 14 regions when not using the contralateral knee as a reference. In conclusion, evaluation of cartilage degeneration by T2 relaxation time after ACLR is most reliable for central and inner cartilage regions. Cartilage degeneration occurs in the central and outer regions of the lateral femoral condyle from 6 to 24 months after anatomic ACLR.

Validation of the hotspot for dorsolateral subthalamic nucleus targeting in deep brain stimulation surgery for Parkinson’s disease: a post hoc analysis of a randomised controlled trial

BackgroundVisualisation of the dorsolateral subthalamic nucleus (STN) remains challenging on 1.5 and 3Tesla T2-weighted MRI. Our previously defined hotspot, relative to the well-visualised medial STN border, serves as an MRI...

Exploring deep learning strategies for intervertebral disc herniation detection on veterinary MRI

Intervertebral Disc Herniation (IVDH) is a common spinal disease in dogs, significantly impacting their health, mobility, and overall well-being. This study initiates an effort to automate the detection and localization of IVDH lesions in veterinary MRI scans, utilizing advanced artificial intelligence (AI) methods. A comprehensive canine IVDH dataset, comprising T2-weighted sagittal MRI images from 213 pet dogs of various breeds, ages, and sizes, was compiled and utilized to train and test the IVDH detection models. The experimental results showed that traditional two-stage detection models reliably outperformed one-stage models, including the recent You Only Look Once X (YOLOX) detector. In terms of methodology, this study introduced a novel spinal localization module, successfully integrated into different object detection models to enhance IVDH detection, achieving an average precision (AP) of up to 75.32%. Additionally, transfer learning was explored to adapt the IVDH detection model for a smaller feline dataset. Overall, this study provides insights into advancing AI for veterinary care, identifying challenges and exploring potential strategies for future development in veterinary radiology.

Multi-planar dual adversarial network based on dynamic 3D features for MRI-CT head and neck image synthesis

Objective. Head and neck radiotherapy planning requires electron densities from different tissues for dose calculation. Dose calculation from imaging modalities such as MRI remains an unsolved problem since this imaging modality does not provide information about the density of electrons. Approach. We propose a generative adversarial network (GAN) approach that synthesizes CT (sCT) images from T1-weighted MRI acquisitions in head and neck cancer patients. Our contribution is to exploit new features that are relevant for improving multimodal image synthesis, and thus improving the quality of the generated CT images. More precisely, we propose a Dual branch generator based on the U-Net architecture and on an augmented multi-planar branch. The augmented branch learns specific 3D dynamic features, which describe the dynamic image shape variations and are extracted from different view-points of the volumetric input MRI. The architecture of the proposed model relies on an end-to-end convolutional U-Net embedding network. Results. The proposed model achieves a mean absolute error (MAE) of 18.76±5.167 in the target Hounsfield unit (HU) space on sagittal head and neck patients, with a mean structural similarity (MSSIM) of 0.95±0.09 and a Frechet inception distance (FID) of 145.60±8.38 . The model yields a MAE of 26.83±8.27 to generate specific primary tumor regions on axial patient acquisitions, with a Dice score of 0.73±0.06 and a FID distance equal to 122.58±7.55 . The improvement of our model over other state-of-the-art GAN approaches is of 3.8%, on a tumor test set. On both sagittal and axial acquisitions, the model yields the best peak signal-to-noise ratio of 27.89±2.22 and 26.08±2.95 to synthesize MRI from CT input. Significance. The proposed model synthesizes both sagittal and axial CT tumor images, used for radiotherapy treatment planning in head and neck cancer cases. The performance analysis across different imaging metrics and under different evaluation strategies demonstrates the effectiveness of our dual CT synthesis model to produce high quality sCT images compared to other state-of-the-art approaches. Our model could improve clinical tumor analysis, in which a further clinical validation remains to be explored.

The clinical application value of 3.0T magnetic resonance T2 mapping imaging in evaluating the degree of acetabular cartilage degeneration in joint replacement surgery running title: MRI and acetabular cartilage degeneration

BackgroundTo explore and compare the values of 3.0T magnetic resonance imaging (MRI) T2 mapping in evaluating the degree of acetabular cartilage degeneration in hip replacement surgery.MethodsA total of 26 elderly patients with femoral neck fractures who were scanned in 3.0T MRI T2 mapping quantification technique were included. Basing on MRI images, the degree of acetabular cartilage degeneration was classified into Grade 0, 1, 2, 3 and 4, according to the International Cartilage Repair Society (ICRS) scores. In addition, 8 healthy volunteers were included for control group.ResultsBy comparison with health population, T2 relaxation values in the anterior, superior, and posterior regions of acetabular cartilage in patients with femoral neck fracture were obviously increased (P < 0.001). Among the patients with femoral neck fractures, there were 16 hip joint with Grade 1–2 (mild degeneration subgroup) and 10 hip joints with Grade 3–4 (severe degeneration subgroup), accounting for 61.54% and 38.46%, respectively. Additionally, T2 relaxation values in the anterior and superior bands of articular cartilage were positively related to the MRI-based grading (P < 0.05); while there was no significant difference of T2 relaxation values in the posterior areas of articular cartilage between severe degeneration subgroup and mild degeneration subgroup (P > 0.05). Importantly, acetabular cartilage degeneration can be detected through signal changes of T2 mapping pseudo-color images.Conclusion3.0T MRI T2 mapping technology can be used to determine the degree of acetabular cartilage degeneration, which can effectively monitor the disease course.

Continuous Monitoring of Pseudopolymorphic Transition in Ezetimibe Using T1 Relaxation with Time-Domain NMR.

The purpose of this study was to continuously monitor the pseudopolymorphic transition from anhydrate to monohydrate by measuring the NMR relaxation using time-domain NMR (TD-NMR). Taking advantage of the simplicity of the low-field NMR instrument configuration, which is an advantage of TD-NMR, the NMR instrument was connected to a humidity controller to monitor the pseudopolymorphic transition. First, ezetimibe (EZT) monohydrate was prepared from its anhydrate using a saturated salt solution method, and T1 relaxation of EZT monohydrate and anhydrate was measured without a humidity controller. The T1 relaxation results confirmed that EZT anhydrate and monohydrate could be distinguished using T1 relaxation measurement. Next, continuous monitoring was conducted by TD-NMR and connected to a humidity controller. Anhydrous EZT was placed in an NMR glass tube and the T1 relaxation measurement was repeated while maintaining the humidity on the side entering the NMR tube at 80% relative humidity. The T1 relaxation became gradually faster from the initial to middle monitoring phases. The final T1 relaxation was then recovered fully and these T1 relaxation times were the same as the T1 relaxation of EZT monohydrate. This study successfully monitored the pseudopolymorphic transition from EZT anhydrate to monohydrate via NMR relaxation.

Detrimental Increase of Spin-Phonon Coupling in Molecular Qubits on Substrates.

Molecular qubits are a promising platform for quantum information systems. Although single molecule and ensemble studies have assessed the performance of S = 1/2 molecules, it is understood that to function in devices, regular arrays of addressable qubits supported by a substrate are needed. The substrate imposes mechanical and electronic boundary conditions on the molecule; however, the impact of these effects on spin-lattice relaxation times is not well understood. Here we perform electronic structure calculations to assess the effects of a graphene (Cgr) substrate on the molecular qubit copper phthalocyanine (CuPc). We use a progressive Hessian approach to efficiently calculate and separate the substrate contributions. We also use a simple thermal model to predict the impact of these changes on the spin-phonon coupling from 0 to 200 K. Further analysis of the individual vibrational modes with and without Cgr shows that an overall increase in SPC between the vibrations modes of CuPc with the surface reduces the spin-lattice relaxation time T1. We explain these changes by examining how the substrate lifts symmetries of CuPc in the absorbed configuration. Our work shows that a surface can have a large unintentional impact on SPC and that ways to reduce this coupling need to be found to fully exploit arrays of molecular qubits in device architectures.

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Abstract Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a “meta-matching” framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants (“meta-matching finetune” and “meta-matching stacking”) from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.

No sex difference in maturation of brain morphology during the perinatal period.

Accumulating evidence have documented sex differences in brain anatomy from early childhood to late adulthood. However, whether sex difference of brain structure emerges in the neonatal brain and how sex modulates the development of cortical morphology during the perinatal stage remains unclear. Here, we utilized T2-weighted MRI from the Developing Human Connectome Project (dHCP) database, consisting of 41 male and 40 female neonates born between 35 and 43 postmenstrual weeks (PMW). Neonates of each sex were arranged in a continuous ascending order of age to capture the progressive changes in cortical thickness and curvature throughout the developmental continuum. The maturational covariance network (MCN) was defined as the coupled developmental fluctuations of morphology measures between cortical regions. We constructed MCNs based on the two features, respectively, to illustrate their developmental interdependencies, and then compared the network topology between sexes. Our results showed that cortical structural development exhibited a localized pattern in both males and females, with no significant sex differences in the developmental trajectory of cortical morphology, overall organization, nodal importance, and modular structure of the MCN. Furthermore, by merging male and female neonates into a unified cohort, we identified evident dependencies influences in structural development between different brain modules using the Granger causality analysis (GCA), emanating from high-order regions toward primary cortices. Our findings demonstrate that the maturational pattern of cortical morphology may not differ between sexes during the perinatal period, and provide evidence for the developmental causality among cortical structures in perinatal brains.

Improved myelin water imaging using B1+ correction and data-driven global feature extraction: application on people with MS

Abstract The predominant technique for quantifying myelin content in the white matter is multi-compartment analysis of MRI’s T2 relaxation times (mcT2 analysis). The process of resolving the T2 spectrum at each voxel, however, is highly ill-posed and remarkably susceptible to noise and to inhomogeneities of the transmit field (B1+). To address these challenges, we employ a preprocessing stage wherein a spatially global data-driven analysis of the tissue is performed to identify a set of mcT2 configurations (motifs) that best describe the tissue under investigation, followed by using this basis set to analyze the signal in each voxel. This procedure is complemented by a new algorithm for correcting B1+ inhomogeneities, lending the overall fitting process with improved robustness and reproducibility. Successful validations are presented using numerical and physical phantoms vs. ground truth, showcasing superior fitting accuracy and precision compared to conventional (non data-driven) fitting. In vivo application of the technique is presented on 26 healthy subjects and 29 people living with multiple sclerosis (MS), revealing substantial reduction in myelin content within normal-appearing white matter regions of people with MS (i.e., outside obvious lesions), and confirming the potential of data-driven myelin values as a radiologic biomarker for MS.

Disordered magnetic ground state in a quasi-1-D d4columnar iridate Sr3LiIrO6.

Spin-orbit coupling (SOC) offers a large variety of novel and extraordinary magnetic and electronic properties in otherwise 'ordinary pool' of heavy ion oxides. Here we present a detailed study on an apparently isolated hexagonal 2Hspin-chain d4iridate Sr3LiIrO6(SLIO) with geometric frustration. Our structural studies reveal Li-Ir chemical order with desired stoichiometry in this compound, while x-ray absorption together with x-ray photoemission spectroscopic characterizations establish pure 5+ valence of Ir. We have established a magnetic ground state with finite Ir5+magnetic moments in this compound, contrary to the anticipated nonmagnetic Jeff= 0 state, through combined dc susceptibility,7Li nuclear magnetic resonance (NMR), muon spin relaxation (μSR) and ab-initio electronic structure studies. These investigations together with ac magnetic susceptibility and specific heat measurements reveal that despite having noticeable antiferromagnetic correlation among the Ir5+local moments, this system does not magnetically order down to at least 0.05 K, possibly due to geometrical exchange frustration, arising from the comparable nearest- and next-nearest-neighbor interchain Ir-O-O-Ir superexchange interaction strengths with opposite signs. However, the zero-field (ZF) μSR analysis shows emergence of a considerable proportion of spin-freezing on top of a spin-fluctuating dynamic magnetic background down to the lowest measured&#xD;temperature of 1.7 K, possibly due to some inhomogeneity and/or the much stronger intra-column Ir-Ir magnetic exchange interaction&#xD;strength relative to the inter-column Ir-Ir ones. The linear temperature dependence of the magnetic specific heat (Cm) in both zero and applied magnetic fields, plus the power-law behavior of the NMR spin-lattice relaxation rate suggest a gapless spinon density of states in this charge gapped disordered magnetic ground state of Sr3LiIrO6.

Nuclear magnetic resonance investigation of superconducting and normal state Nb3Sn

The superconductor Nb3Sn has important applications for construction of very high-field superconducting magnets. In this work we investigate its microscopic electronic structure with 93Nb nuclear magnetic resonance (NMR). The high-quality Nb3Sn powder sample was studied in both 3.2 T and 7 T magnetic fields in the temperature range from 4 K to 300 K. From measurement of the spectrum and its theoretical analysis, we find evidence for anisotropy despite its cubic crystal structure. Magnetic alignment of the powder grains in the superconducting state was also observed. The Knight shift and spin-lattice relaxation rate, T1−1 , were measured and the latter compared with BCS theory for the energy gap Δ(0)=2.7±0.3kBTc at 3.2 T and Δ(0)=2.33±0.07kBTc at 7 T, indicating suppression of the order parameter by magnetic field.

Anthracycline therapy induces an early decline of cardiac contractility in low-risk patients with breast cancer

AimsCancer therapy-related cardiac dysfunction (CTRCD) is a dreaded complication of anthracycline therapy. CTRCD most frequently appears in patients with cardiovascular risk factors (CVR) or known cardiovascular disease. However, limited data exist on incidence and course of anthracycline-induced CTRCD in patients without preexisting risk factors.We therefore aimed to longitudinally investigate a cohort of young women on anthracycline treatment due to breast cancer without cardiovascular risk factors or known cardiovascular disease (NCT03940625).Methods and resultsWe enrolled 59 women with primary breast cancer and scheduled anthracycline-based therapy, but without CVR or preexisting cardiovascular disease. We conducted a longitudinal assessment before, immediately and 12 months after cancer therapy with general laboratory, electrocardiograms, echocardiography and cardiovascular magnetic resonance (CMR), including myocardial relaxometry with T1, T2 and extracellular volume mapping.Every single patient experienced a drop in CMR-measured left ventricular ejection fraction (LVEF) of 6 ± 3% immediately after cancer therapy. According to the novel definition 32 patients (54.2%) developed CTRCD after 12 months defined by reduction in LVEF, global longitudinal strain (GLS) and/or biomarkers elevation, two of them were symptomatic. Global myocardial T2 relaxation times as well as myocardial mass increased coincidently with a decline in wall-thickening. While T2 values and myocardial mass normalized after 12 months, LVEF and GLS remained impaired.ConclusionIn every single patient anthracyclines induce a decline of myocardial contractility, even among patients without pre-existing risk factors for CTRCD. Our data suggest to thoroughly evaluate whether this may lead to an increased risk of future cardiovascular events.Graphical Reduced myocardial contractility in low-risk patients receiving anthracycline-based cancer therapy. This study included 59 otherwise healthy women with primary breast cancer undergoing anthracycline-based chemotherapy. CMR was performed at baseline, directly and 12 months after cancer therapy. A decline in left ventricular function was observed in every single patient accompanied by transient edema. More than 50% were diagnosed with cancer therapy related cardiovascular dysfunction. LVEF: left ventricular function, CTRCD: cancer therapy related cardiovascular dysfunction, GLS = Global longitudinal strain, hs-TnT= high sensitive Troponin T, NT-pro BNP = NT-pro brain natriuretic peptide

Extent of traumatic spinal cord injury is lesion level dependent and predictive of recovery: a multicenter neuroimaging study.

Assessing the extent of the intramedullary lesion after spinal cord injury (SCI) might help to improve prognostication. However, since the neurological level of injury (NLI) impacts the recovery potential of SCI patients, the question arises whether lesion size parameters and predictive models based on those parameters are affected as well. In this retrospective observational study, the extent of the intramedullary lesion between individuals who sustained cervical and thoracolumbar SCI was compared and its relation to clinical recovery was assessed. 154 patients with sub-acute SCI (89 individuals with cervical lesions and 65 individuals with thoracolumbar lesions) underwent conventional clinical magnetic resonance imaging (MRI) 1 month after injury and clinical examination at 1 and 12 months. The morphology of the focal lesion within the spinal cord was manually assessed on the midsagittal slice of T2-weighted MR images and compared between cervical and thoracolumbar SCI patients as well as between patients who improved at least one American Spinal Injury Association Impairment Scale (AIS) grade (converters) and patients without AIS grade improvement (non-converters). The predictive value of lesion parameters including lesion length, lesion width, and preserved tissue bridges for predicting AIS grade conversion was assessed using regression models (conditional inference tree analysis). Lesion length was two times longer in thoracolumbar compared to cervical SCI patients (F = 39.48, p < 0.0001), while lesion width and tissue bridges' width did not differ. When comparing AIS grade converters and non-converters, converters showed a smaller lesion length (F = 5.46, p = 0.021), a smaller lesion width (F = 13.75, p = 0.0003) and greater tissue bridges (F = 12.87, p = 0.0005). Using regression models, tissue bridges allowed more refined subgrouping of the heterogenous patient population according to individual recovery profiles between 1 month and 12 months after SCI, while lesion length added no additional information for further subgrouping. This study characterizes differences in the anteroposterior and craniocaudal lesion extent after SCI. The two times greater lesion length in thoracolumbar compared to cervical SCI might be related to differences in the anatomy, biomechanics, and perfusion between the cervical and thoracic spine. Preserved tissue bridges were less influenced by the lesion level, while closely related to the clinical impairment. These results highlight the robustness and utility of tissue bridges as a neuroimaging biomarker for predicting clinical outcome after SCI in heterogeneous patient populations and for patient stratification in clinical trials.

Quantitative muscle MRI in sporadic inclusion body myositis (sIBM): A prospective cohort study.

Sporadic inclusion body myositis (sIBM) is the predominant idiopathic inflammatory myopathy (IIM) in older people. Limitations of classical clinical assessments have been discussed as possible explanations for failed clinical trials, underlining the need for more sensitive outcome measures. Quantitative muscle MRI (qMRI) is a promising candidate for evaluating and monitoring sIBM. Longitudinal assessment of qMRI in sIBM patients. We evaluated fifteen lower extremity muscles of 12 sIBM patients (5 females, mean age 69.6, BMI 27.8) and 12 healthy age- and gender-matched controls. Seven patients and matched controls underwent a follow-up evaluation after one year. Clinical assessment included testing for muscle strength with Quick Motor Function Measure (QMFM), IBM functional rating scale (IBM-FRS), and gait analysis (6-minute walking distance). 3T-MRI scans of the lower extremities were performed, including a Dixon-based sequence, T2 mapping and Diffusion Tensor Imaging. The qMRI-values fat-fraction (FF), water T2 relaxation time (wT2), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λ1), and radial diffusivity (RD) were analysed. Compared to healthy controls, significant differences for all qMRI parameters averaged over all muscles were found in sIBM using a MANOVA (p < 0.001). In low-fat muscles (FF < 10% ), a significant increase of wT2 and FA with an accompanying decrease of MD, λ1, and RD was observed (p≤0.020). The highest correlation with clinical assessments was found for wT2 values in thigh muscles (r≤-0.634). Significant changes of FF (+3.0% ), wT2 (+0.6 ms), MD (-0.04 10-3mm2/s), λ1 (-0.05 10-3mm2/s), and RD (-0.03 10-3mm2/s) were observed in the longitudinal evaluation of sIBM patients (p≤0.001). FA showed no significant change (p = 0.242). qMRI metrics correlate with clinical findings and can reflect different ongoing pathophysiological mechanisms. While wT2 is an emerging marker of disease activity, the role of diffusion metrics, possibly reflecting changes in fibre size and intracellular deposits, remains subject to further investigations.

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks.

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

The relationship between the duration and the retraction and atrophy grades in traumatic isolated full-thickness supraspinatus tears in young patients

BackgroundThe study aimed to determine the grade of retraction and atrophy according to the time elapsed in traumatic isolated full-thickness supraspinatus (SS) tears in young patients.MethodsOne thousand twenty-six patients, who underwent arthroscopic shoulder surgery, were retrospectively reviewed. Pre-operative magnetic resonance imaging (MRI) of 69 patients aged 18 to 40 years with isolated traumatic full-thickness SS lesions remaining after exclusion criteria were evaluated for tendon retraction and atrophy grades. SS retraction was determined from a T2-weighted oblique coronal MRI slice, and the atrophy grade was determined from the T1-weighted oblique sagittal MRI slice. The patients were divided into four groups 0–1 month, 1–3 months, 3–6 months, and 6–12 months according to the time between trauma and MRI. The relationship of tendon retraction and muscle atrophy with elapsed time was evaluated, in addition, comparisons between groups were made.ResultsThirty-one (45%) of the patients were female and their mean age was 30 ± 7.3 (18–40) years. The mean age of men was 30.5 ± 6.9 (18–39) years (p = 0.880). The time between rupture and MRI was moderately correlated with retraction and strongly correlated with atrophy grades (r = 0.599, 0.751, respectively). It was observed that there was a statistically significant difference between the 1st (0–1 month) and 2nd (1–3 months) groups (p = 0.003, 0.001, respectively), and between the 2nd and 3rd (3–6 months) groups (p = 0.032, 0.002, respectively), but there was no significant difference between the 3rd and 4th (6–12 months) groups (p = 0.118, 0.057, respectively). In addition, there was a moderate correlation between tendon retraction and atrophy grades (r = 0.668). Power (1- b) in post hoc analysis was calculated as 0.826.ConclusionsThe current study, supported by arthroscopy, showed that there is a moderate and strong positive correlation between the time elapsed after trauma and the level of retraction and degree of atrophy in traumatic full-thickness SS tears, and demonstrated the importance of early surgical intervention in young patients.