Spin-lattice Relaxation Research Articles

Mathematical modeling of meningioma volume change after radiation treatment

ObjectiveMeningiomas are the most common primary central nervous tumor and are often treated with radiation therapy. This study examines the long-term volumetric changes of intracranial meningiomas in response to radiation therapy. The objective is to analyze and model the volumetric changes following treatment. MethodsData from a retrospective single-institution database (2005–2015) were used, with inclusion criteria being patients with a diagnosis of meningiomas, along with additional inclusion criteria consisting of treatment with radiation, having at least three magnetic resonance imaging (MRI) scans with one or more before and after radiation treatment, and the patients following up for at least eighteen months. Exclusion criteria consisted of patients less than 18 years old, patients receiving surgery and/or adjuvant chemotherapy following radiation, and patients without any available details regarding radiation treatment parameters. Tumor volumes were measured via T1-weighted post-contrast MRI and calculated using the ABC/2 ellipsoidal approximation, a method allowing for the measurement of non-linear growth volume reduction. ResultsOf 48 meningioma patients considered, 10 % experienced post-radiation growth, while 75 % witnessed a ≥50 % decrease in volume over a follow-up period of 0.3–14.9 years. Median decay rate was 0.81, and within 1.17 years, 90 % achieved the predicted volume reduction. Predicted vs. actual volumes showed a mean difference of 0.009 ± 0.347 cc. Initial tumor volumes strongly correlated (Pearson’s R=0.98, R-squared=0.96) with final asymptotic volumes, which had a median of 1.50 cc, with interquartile range (IQR) = [0.39, 3.67]. Conclusion90 % of patients achieved tumor-volume reduction at 1.17 years post-treatment, reaching a non-zero asymptote strongly correlated with initial tumor volume, and 75 % experienced at least a 50 % volume decrease. Individual volume changes for responsive meningiomas can be modeled and predicted using exponential decay curves.

Effects of strategic white matter hyperintensities of cholinergic pathways on basal forebrain volume in patients with amyloid-negative neurocognitive disorders

BackgroundThe cholinergic neurotransmitter system is crucial to cognitive function, with the basal forebrain (BF) being particularly susceptible to Alzheimer’s disease (AD) pathology. However, the interaction of white matter hyperintensities (WMH) in cholinergic pathways and BF atrophy without amyloid pathology remains poorly understood.MethodsWe enrolled patients who underwent neuropsychological tests, magnetic resonance imaging, and 18F-florbetaben positron emission tomography due to cognitive impairment at the teaching university hospital from 2015 to 2022. Among these, we selected patients with negative amyloid scans and additionally excluded those with Parkinson’s dementia that may be accompanied by BF atrophy. The WMH burden of cholinergic pathways was quantified by the Cholinergic Pathways Hyperintensities Scale (CHIPS) score, and categorized into tertile groups because the CHIPS score did not meet normal distribution. Segmentation of the BF on volumetric T1-weighted MRI was performed using FreeSurfer, then was normalized for total intracranial volume. Multivariable regression analysis was performed to investigate the association between BF volumes and CHIPS scores.ResultsA total of 187 patients were enrolled. The median CHIPS score was 12 [IQR 5.0; 24.0]. The BF volume of the highest CHIPS tertile group (mean ± SD, 3.51 ± 0.49, CHIPSt3) was significantly decreased than those of the lower CHIPS tertile groups (3.75 ± 0.53, CHIPSt2; 3.83 ± 0.53, CHIPSt1; P = 0.02). In the univariable regression analysis, factors showing significant associations with the BF volume were the CHIPSt3 group, age, female, education, diabetes mellitus, smoking, previous stroke history, periventricular WMH, and cerebral microbleeds. In multivariable regression analysis, the CHIPSt3 group (standardized beta [βstd] = -0.25, P = 0.01), female (βstd = 0.20, P = 0.04), and diabetes mellitus (βstd = -0.22, P < 0.01) showed a significant association with the BF volume. Sensitivity analyses showed a negative correlation between CHIPS score and normalized BF volume, regardless of WMH severity.ConclusionsWe identified a significant correlation between strategic WMH burden in the cholinergic pathway and BF atrophy independently of amyloid positivity and WMH severity. These results suggest a mechanism of cholinergic neuronal loss through the dying-back phenomenon and provide a rationale that strategic WMH assessment may help identify target groups that may benefit from acetylcholinesterase inhibitor treatment.

Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.

Determining brain atrophy is crucial for the diagnosis of neurodegenerative diseases. Despite detailed brain atrophy assessments using three-dimensional (3D) T1-weighted magnetic resonance imaging, their practical utility is limited by cost and time. This study introduces deep learning algorithms for quantifying brain atrophy using a more accessible two-dimensional (2D) T1, aiming to achieve cost-effective differentiation of dementia of the Alzheimer's type (DAT) from cognitively unimpaired (CU), while maintaining or exceeding the performance obtained with T1-3D individuals and to accurately predict AD-specific atrophy similarity and atrophic changes [W-scores and Brain Age Index (BAI)]. Involving 924 participants (478 CU and 446 DAT), our deep learning models were trained on cerebrospinal fluid (CSF) volumes from 2D T1 images and compared with 3D T1 images. The performance of the models in differentiating DAT from CU was assessed using receiver operating characteristic analysis. Pearson's correlation analyses were used to evaluate the relations between 3D T1 and 2D T1 measurements of cortical thickness and CSF volumes, AD-specific atrophy similarity, W-scores, and BAIs. Our deep learning models demonstrated strong correlations between 2D and 3D T1-derived CSF volumes, with correlation coefficients r ranging from 0.805 to 0.971. The algorithms based on 2D T1 accurately distinguished DAT from CU with high accuracy (area under the curve values of 0.873), which were comparable to those of algorithms based on 3D T1. Algorithms based on 2D T1 image-derived CSF volumes showed high correlations in AD-specific atrophy similarity (r = 0.915), W-scores for brain atrophy (0.732 ≤ r ≤ 0.976), and BAIs (r = 0.821) compared with those based on 3D T1 images. Deep learning-based analysis of 2D T1 images is a feasible and accurate alternative for assessing brain atrophy, offering diagnostic precision comparable to that of 3D T1 imaging. This approach offers the advantage of the availability of T1-2D imaging, as well as reduced time and cost, while maintaining diagnostic precision comparable to T1-3D.

Dominant hippocampus segmentation with brain atrophy analysis-based AD subtype classification using KLW-RU-Net and T1FL

A progressive neurodegenerative disorder that leads to cognitive decline and memory loss is known as Alzheimer's Disease (AD). Researchers propose an integrated framework centered on brain atrophy analysis and hippocampus segmentation to predict AD and its subtypes. This approach augments the accurate prediction of AD subtype classification, thus addressing the lack of emphasis in prevailing works. Primarily, the T1-weighted brain Magnetic Resonance Imaging (MRI) images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database undergo preprocessing. After that, for spatiotemporal evaluation, region segmentation, tau accumulation analysis, and Feature Extraction (FE), the image is processed. By employing the Fisher-Kolmogorov (FK) model, the essential parameters are extracted in spatio-temporal evaluation. In the meantime, for tissue segmentation, the Knowledge Partitioned Clustering (KPC) is utilized, and for hippocampus segmentation, the Kullback-Leibler Within-layer Regularized UNet (KLW-RU-Net) is employed. Moreover, by binarizing the image, the tau accumulation is analyzed; in addition, Principal Component Analysis (PCA) is utilized for FE. After PCA, the Adaptive Step-sized Gauss Rabbits Optimization (ASGRO) is employed for feature selection. Subsequently, to classify AD, the Dilated Derivative Sigmoid-Weighted DenseNet (DD-SWDN) is employed. Additionally, by employing Type-1 Fuzzy Logic (T1FL), the subtype of AD is classified, thus achieving higher accuracy for AD subtype classification. Therefore, as per these outcomes, the proposed work performs better than the other conventional approaches.

Temperature Dependence of the Electron Spin–Lattice Relaxation Time of Vanadyl Porphyrins in Asphaltenes from the Ashalcha Oilfield

Temperature Dependence of the Electron Spin–Lattice Relaxation Time of Vanadyl Porphyrins in Asphaltenes from the Ashalcha Oilfield

(Invited) Core-Multi-Shell Design: Unlocking Multimodal Capabilities in Lanthanide-Based Nanoparticles

The remarkable optomagnetic properties of the lanthanides (Ln) make Ln-based materials ideal for biomedical applications, including diagnostic (e.g., imaging, nanothermometry) and therapeutic (e.g., drug delivery, photodynamic therapy) approaches. This is due the unique electronic properties of the f-elements allowing for upconversion and near-infrared emission under near-infrared excitation as well as high magnetic moments. Yet, challenges remain, including low emission intensity and efficiency of small nanoparticles (NPs), and reliable, fast synthesis routes. As material chemists, we tackle these challenges with new designs of Ln-NPs by chemically controlled synthesis, application-oriented surface chemistry, and understanding of structure-property-relationships. We developed a straightforward and rapid microwave-assisted synthesis approach towards core-multi-shell Ln-NPs. Careful architecture design and choice of RE3+ dopant ions allow to tune the optical properties of the resultant nanoparticles, i.e., upconversion and near-infrared emission. This presentation will shed light on recent results with respect to microwave-assisted synthesis of Ln-NPs as well as structural and optomagnetic properties, seeking biomedical application.For instance, multimodal bioimaging probes merging optical imaging, magnetic resonance imaging (MRI), and X-ray computed tomography (CT) capabilities have attracted considerable attention due to their potential biomedical applications. This includes nanoparticles that combine upconverting Er3+/Yb3+ and magnetic NaDyF4 for optical/T2-weighted MRI/CT multimodal capabilities. A known drawback of multimodal probes that merge the upconverting Er3+/Yb3+ ion pair with magnetic Dy3+ ions for T2-weighted MRI is the loss of upconversion emission due to Dy3+ poisoning. The synthesis of controlled nanoparticle architectures with tuned inner NaGdF4 shell thicknesses separating Dy3+ and Er3+/Yb3+ allow to shed light on this distance-dependent loss of upconversion due to Yb3+ → Dy3+ energy transfer. As a result, the Ln-NP architecture was tuned to not only restore but enhanced the upconversion emission. In addition to multimodal imaging, Ln-NPs are promising candidates for application in nanothermometry due to the distinct, temperature-induced changes in their spectral features. We seek the development of novel Ln-NP nanothermometers for new opportunities in the near-infrared biological transparency windows (1000 to 2000 nm), by doping RE3+ ions, such as Er3+ and Tm3+ as well as unconventional Pr3+, into core-multi-shell lanthanide-based nanoparticles.

Magnetic Field Driven Dynamic Reorganization of Electrocatalytic Interface for Sustainable Enhancement in Oxygen Evolution

Hydrogen with high calorific content (150 MJ/kg) and environment-friendly combustion product (H2O) becomes the alternative fossil fuel. Catalytic electrolysis of water is one of the attractive routes for generating H2. Energy-efficient and cost-effective electrolytic production of H2 has predominantly focused on lowering the overpotential for water splitting process by developing newer non-Pt group-based catalysts.This work demonstrates a new direction towards sustainable oxygen evolution reaction (OER) by exploiting the fundamental interplay of electromagnetic forces. Introducing an external magnetic field (Hext = 100 mT) perpendicular to the heterogeneous electrocatalytic interface produces an unprecedented increase in rate of electrocatalytic OER in phosphate buffer with pH=7. Such electromagnetic catalysis is achieved with metal phosphides (NiCoP and NiCoFeP) with different morphology, leading 2.5 fold increase in the OER current density and concomitant lowering of overpotential by 10% (Hext = 100 mT). Thereby, 50% lowering of charge transfer resistance (Rct) is achieved. This is substantiated by the lowering of the Tafel slope (by 5%) in the presence of Hext. Thus, we report two important observations influenced by the magnetic field: (a) direct effect of magnetic field on the catalyst surface to reduce the charge-transfer resistance, (b) spin-dependant OER process to get influenced under magnetic field and (c) indirect effect of magnetic field to sustain the magneto-electrocatalytic enhancement even after removing the magnetic field. The time-scale of magnetic relaxation is million times higher than conventional spin-lattice relaxation events in para/ferromagnetic systems. Such long-term stability of magnetized states is predominantly proposed to be originating from the NiCoP and NiCoFeP interface. So, we attribute the long-lived states to be additionally contributed from the volumetric expansion and active site expansion of the catalyst upon magnetization. Catalyst interface is arising due to the dendritic structure of catalyst. Such synergism of magnetization, is expected to increase the applicability of magneto-electrocatalysis as a more sustainable electrocatalytic approach towards a hydrogen-driven economy. Figure 1

15O]H2O PET/MRI for Assessment of Complete Response to Neoadjuvant or Induction Chemotherapy in Patients with Muscle-Invasive Bladder Cancer: A Pilot Study.

Background: Accurate assessment of therapy response to chemotherapy could possibly offer a bladder-sparing approach in selected patients with localized muscle-invasive bladder cancer (MIBC). The aim of this study was to evaluate whether [15O]H2O PET/MRI can be used for assessment of complete local pathological response to preoperative chemotherapy in patients with MIBC. Methods: This prospective pilot study included 13 patients with MIBC treated with neoadjuvant or induction chemotherapy and subsequent radical cystectomy. Patients underwent a [15O]H2O PET/MRI scan before chemotherapy and another scan after chemotherapy before radical cystectomy. Volumes of interest were delineated on T2-weighted MRI and transferred to parametric images for dynamic analysis. Tumor blood flow (TBF) was estimated by [15O]H2O PET. Changes in TBF were compared with histopathology. The Wilcoxon matched-pairs signed-ranks test was used for comparing pre- and post-chemotherapy measurements. Results: Mean TBF decreased by 49%. Mean TBF in complete responders (ypT0N0/ypTis) was not significantly different from non-complete responders (≥ypT1) (p = 0.52). Conclusions: Despite a measurable decrease in TBF after chemotherapy treatment, we were not able to estimate a TBF threshold for identifying complete responders to chemotherapy for MIBC patients. Further studies are needed to elucidate the potential of [15O]H2O PET/MRI in assessing therapy response in MIBC.

Reproducibility for carotid wall segmentation using T1-weighted DANTE-SPACE sequence on high-resolution 3-T carotid MRI.

T1-weighted (T1W) magnetic resonance imaging (MRI) using the delay alternating with nutation for excitation-sampling perfection with application-optimized contrasts using different flip angle evolution (DANTE-SPACE) is the preferred imaging technique for evaluation of the vessel wall. To evaluate the intra- and inter-rater reproducibility of carotid wall segmentation on T1W DANTE-SPACE in patients with symptomatic (acute stroke or transient ischemic attack) internal carotid artery (ICA) stenosis. This prospective study included 25 patients with acute (≤3 months) stroke or transient ischemic attack and 50%-99% stenosis of the ICA. All patients underwent 3.0-T high-resolution carotid MRI. Two radiologists independently performed the manual segmentation of the vessel wall and inner lumen of the bilateral carotid artery on DANTE-SPACE. The intraclass correlation coefficient (ICC), Dice similarity coefficient (DSC), and Hausdorff distance (HD) were calculated. The ICCs for intra-rater reproducibility of carotid wall volume, inner lumen volume, and normalized wall index were 0.965, 0.990, and 0.962, respectively. The ICCs for inter-rater reproducibility of carotid wall volume, inner lumen, and normalized wall index were 0.856, 0.981, and 0.904. DSC and HD for intra- and inter-rater reproducibility of carotid wall segmentation were as follows: 0.873 and 0.809 (DSC); and 0.079 and 0.118 (HD), respectively. For evaluation of reproducibility only in the carotid artery with symptomatic stenosis, the ICCs for intra- and inter-rater reproducibility indicated all perfect agreement. T1W DANTE-SPACE is a reproducible sequence for evaluation of the carotid wall using carotid MRI in patients with symptomatic ICA stenosis.

DCE-Qnet: deep network quantification of dynamic contrast enhanced (DCE) MRI.

Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in ten healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5 and 51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1 and 47%. The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 min per scan and more accurate quantification.

Hyperbranched Polymeric 19F MRI Contrast Agents with Long T2 Relaxation Time Based on β-Cyclodextrin and Phosphorycholine.

19F magnetic resonance imaging (19F MRI) is gaining attention as an emerging diagnostic technology. Effective 19F MRI contrast agents (CAs) for in vivo applications require a long transverse (or spin-spin) relaxation time (T2), short longitudinal (or spin-lattice) relaxation time (T1), high fluorine content, and excellent biocompatibility. Here, we present a novel hyperbranched polymeric 19F MRI CA based on β-cyclodextrin and phosphorylcholine. The influence of the branching degree and fluorine content on T2 was thoroughly investigated. Results demonstrated a maximum fluorine content of 11.85% and a T2 of 612 ms. This hyperbranched polymeric 19F MRI CA exhibited both great biocompatibility against cells and organs of mice and high-performance imaging capabilities both in vitro and in vivo. The research provides positive insights into the synthesis strategies, topological design, and selection of fluorine tags for 19F MRI CAs.

Integrative radiomics clustering analysis to decipher breast cancer heterogeneity and prognostic indicators through multiparametric MRI

Breast cancer diagnosis and treatment have been revolutionized by multiparametric Magnetic Resonance Imaging (mpMRI), encompassing T2-weighted imaging (T2WI), Diffusion-weighted imaging (DWI), and Dynamic Contrast-Enhanced MRI (DCE-MRI). We conducted a retrospective analysis of mpMRI data from 194 breast cancer patients (September 2019 to October 2023). Using ‘pyradiomics’ for radiomics feature extraction and MOVICS for unsupervised clustering. Interestingly, we identified two distinct patient clusters associated with significant differences in molecular subtypes, particularly in Luminal A subtype distribution (p = 0.03), estrogen receptor (ER) (p = 0.01), progesterone receptor (PR) (p = 0.04), mean tumor size (p < 0.01), lymph node metastasis (LNM) (p = 0.01), and edema (p < 0.01). Our study emphasizes mpMRI’s potential in breast cancer by using radiomics-based cluster analysis to categorize tumors, uncovering heterogeneity, and aiding in personalized treatment strategies.

A rare case report: Brucellosis as an extreme cause of shoulder subluxation and rotator cuff tendinopathy

Rationale: Shoulder subluxation and rotator cuff tendinopathy is commonly associated with degeneration or trauma, and brucellosis has been reported to be correlated with shoulder subacromial bursitis, arthritis. However, brucellosis as a cause of shoulder subluxation and rotator cuff tendinopathy has never been reported in the English medical literature. Patient concerns: A 46-year-old man complained of left shoulder weakness and limited movement ongoing for 1 month. He was primarily diagnosed with shoulder subluxation and rotator cuff tear based on magnetic resonance imaging, and was advised to undergo arthroscopic rotator cuff surgical repair. Diagnoses: Although rotator cuff tear was diagnosed in another hospital, some doubts remained. First, a tendon profile with a relative high signal in T2-weighted magnetic resonance images in the supraspinatus tear area still existed. The findings were clearly different from the usual full-thickness rotator cuff tear, and no tendon profile could be found in the tear area. Second, degenerative rotator cuff tear mainly occurs in the aged population and this patient was relatively young, without a history of trauma. His left shoulder was painless but the weakness and the limited movement were extreme and all of these factors did not support the diagnosis of rotator cuff tear. Some infectious disease was suspected to be the cause and relevant laboratory tests were conducted. This patient was eventually diagnosed as having brucellosis-associated shoulder subluxation and rotator cuff tendinopathy based on positive serum standard tube agglutination test and buffer plate agglutination test for brucellosis. Interventions: Oral drug therapy with rifampicin and doxycycline was used for 8 weeks. Outcomes: A complete response with normal shoulder strength and movement was achieved at 8 weeks. At the one-year follow-up no signs of recurrence were detected. Lessons: Brucellosis-associated shoulder subluxation and rotator cuff tendinopathy may be misdiagnosed as a rotator cuff tear in magnetic resonance imaging and be further managed with indeterminate arthroscopic repair. A comprehensive understanding of the patient’s previous medical history, imaging and laboratory data and a careful physical examination are of great clinic value for the accurate diagnosis of the condition and the subsequent prognosis of the patient.

MRI T1-Weighted Fat Segmentation Based on Active Contour for Osteosarcoma Patients

Osteosarcoma (OS) is the most common and frequent primary bone cancer in children and adolescents. This type of bone cancer often developed at the long bones’ extremities close to the metaphyseal growth plates. To address these issues, OS quantitative analysis is performed using Magnetic Resonance Imaging (MRI) to plan surgical procedures and track the effectiveness of treatment. Fat suppression is commonly employed to eliminate the fat signal from T1-Weighted MRI sequence. The suppressed fat signal in T1-Weighted is important and often used to identify abnormalities in other MRI sequences. In clinical, MRI images must be interpreted by a radiologist. Manually outlining the tumour location is laborious and subjective. Image processing involves segmentation to separate information from the desired target region of the image. Active Contour (AC) is an algorithm often used in medical images for segmentation. Nevertheless, the process of mask initialization can be challenging and requires careful attention. This study proposes a method to define the mask initialization so that the AC has a starting point to extract fat in T1-Weighted for the purpose of fat suppression. From the results obtained, AC with the proposed mask initialization method successfully segmented the fat in T1-Weighted MRI images with accuracy, precision, recall and F1-score of 0.92, 0.86, 0.88 and 0.89 respectively.

Photodynamic Therapy of Atherosclerotic Plaque Monitored by T1 and T2 Relaxation Times of Magnetic Resonance Imaging

Atherosclerosis, marked by plaque accumulation within arteries, results from lipid dysregulation, inflammation, and vascular remodeling. Plaque composition, including lipid-rich cores and fibrous caps, determines stability and vulnerability. Photodynamic therapy (PDT) has emerged as a promising treatment, leveraging photosensitizers to induce localized cytotoxicity upon light activation. PDT targets plaque components selectively, reducing burden and inflammation. Challenges remain in optimizing PDT parameters and translating preclinical success to clinical efficacy. Nonetheless, PDT offers a minimally invasive strategy for atherosclerosis management, promising personalized interventions for cardiovascular health. The objective of the current study was to present the findings from quantitative non-contrast MRI of atherosclerosis post-PDT by assessing relaxation times. The study aimed to utilize and optimize a 1.5T MRI system. Clinical scanners were used for MRI examinations. The research involved analyzing T1 and T2 relaxation times. Following treatment of the samples with Rose Bengal and exposure to pure oxygen, PDT irradiation was administered. The results indicated that the therapy impacted the crus, evidenced by a significant decrease in relaxation times in the MRI data.

Enhancing the reliability of deep learning-based head and neck tumour segmentation using uncertainty estimation with multi-modal images

Objective. Deep learning shows promise in autosegmentation of head and neck cancer (HNC) primary tumours (GTV-T) and nodal metastases (GTV-N). However, errors such as including non-tumour regions or missing nodal metastases still occur. Conventional methods often make overconfident predictions, compromising reliability. Incorporating uncertainty estimation, which provides calibrated confidence intervals can address this issue. Our aim was to investigate the efficacy of various uncertainty estimation methods in improving segmentation reliability. We evaluated their confidence levels in voxel predictions and ability to reveal potential segmentation errors. Approach. We retrospectively collected data from 567 HNC patients with diverse cancer sites and multi-modality images (CT, PET, T1-, and T2-weighted MRI) along with their clinical GTV-T/N delineations. Using the nnUNet 3D segmentation pipeline, we compared seven uncertainty estimation methods, evaluating them based on segmentation accuracy (Dice similarity coefficient, DSC), confidence calibration (Expected Calibration Error, ECE), and their ability to reveal segmentation errors (Uncertainty-Error overlap using DSC, UE-DSC). Main results. Evaluated on the hold-out test dataset (n = 97), the median DSC scores for GTV-T and GTV-N segmentation across all uncertainty estimation methods had a narrow range, from 0.73 to 0.76 and 0.78 to 0.80, respectively. In contrast, the median ECE exhibited a wider range, from 0.30 to 0.12 for GTV-T and 0.25 to 0.09 for GTV-N. Similarly, the median UE-DSC also ranged broadly, from 0.21 to 0.38 for GTV-T and 0.22 to 0.36 for GTV-N. A probabilistic network—PhiSeg method consistently demonstrated the best performance in terms of ECE and UE-DSC. Significance. Our study highlights the importance of uncertainty estimation in enhancing the reliability of deep learning for autosegmentation of HNC GTV. The results show that while segmentation accuracy can be similar across methods, their reliability, measured by calibration error and uncertainty-error overlap, varies significantly. Used with visualisation maps, these methods may effectively pinpoint uncertainties and potential errors at the voxel level.

Extracorporeal shockwave therapy for degenerative meniscal tears results in a decreased T2 relaxation time and pain relief: An exploratory randomized clinical trial.

The optimal management of degenerative meniscal tears remains controversial. Extracorporeal shockwave therapy (ESWT) has been shown to promote tissue repair in both preclinical and clinical studies; however, its effect on degenerative meniscal tears remains unknown. This study aimed to examine whether ESWT improves meniscal degeneration. This randomized trial was conducted between 2020 and 2022 and involved patients with degenerative medial meniscal tears. Patients were allocated to receive either focused ESWT (0.25 mJ/mm2, 2000 impulses, 3 sessions with a 1-week interval) or sham treatment. Patients were evaluated using magnetic resonance imaging (MRI) before treatment and at 12 months after treatment. The primary endpoint was improvement in meniscal degeneration, as assessed by the change in T2 relaxation time from baseline on MRI T2 mapping. Knee pain and clinical outcomes were also examined at the same time. Of 29 randomized patients, 27 patients (mean age 63.9 ± 8.7 years; females 37%; ESWT group 14 patients; control group 13 patients) were included in the final analysis. At 12 months postintervention, patients in the ESWT group showed a greater decrease in the T2 relaxation time (ESWT group -2.9 ± 1.7 ms vs. control group 1.0 ± 1.9 ms; p < 0.001) and had less knee pain (p = 0.04). The clinical outcomes at 12 months post-treatment were not statistically significant. No adverse events were reported. ESWT decreased the T2 relaxation time in the meniscus at 12 months post-treatment. ESWT also provided pain relief, but no differences were observed in clinical outcomes. Level II.

T1 relaxation and axon fibre configuration in human white matter.

Understanding the effects of white matter (WM) axon fibre microstructure on T1 relaxation is important for neuroimaging. Here, we have studied the interrelationship between T1 and axon fibre configurations at 3T and 7T. T1 and S0 (=signal intensity at zero TI) were computed from MP2RAGE images acquired with six inversion recovery times. Multishell diffusion MRI images were analysed for fractional anisotropy (FA); MD; V1; the volume fractions for the first (f1), second (f2) and third (f3) fibre configuration; and fibre density cross-section images for the first (fdc1), second (fdc2) and third (fdc3) fibres. T1 values were plotted as a function of FA, f1, f2, f3, fdc1, fdc2 and fdc3 to examine interrelationships between the longitudinal relaxation and the diffusion MRI microstructural measures. T1 values decreased with increasing FA, f1 and f2 in a nonlinear fashion. At low FA values (from 0.2 to 0.4), a steep shortening of T1 was followed by a shallow shortening by 6%-10% at both fields. The steep shortening was associated with decreasing S0 and MD. T1 also decreased with increasing fdc1 values in a nonlinear fashion. Instead, only a small T1 change as a function of either f3 or fdc3 was observed. In WM areas selected by fdc1 only masks, T1 was shorter than in those with fdc2/fdc3. In WM areas with high single fibre populations, as delineated by f1/fdc1 masks, T1 was shorter than in tissue with high complex fibre configurations, as segmented by f2/fdc2 or f3/fdc3 masks. T1 differences between these WM areas are attributable to combined effects by T1 anisotropy and lowered FA. The current data show strong interrelationships between T1, axon fibre configuration and orientation in healthy WM. It is concluded that diffusion MRI microstructural measures are essential in the effort to interpret quantitative T1 images in terms of tissue state in health and disease.

Fully Automated Hippocampus Segmentation using T2-informed Deep Convolutional Neural Networks

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine.We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures.We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data.In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.

Quantitative characterization of the crosslinking degree of hydroxypropyl guar gum fracturing fluid by low-field NMR

As a widely used water-based fracturing fluid, the performance of hydroxypropyl guar gum fracturing fluid is closely related to the degree of crosslinking, the quantitative characterization of which can reveal a detailed crosslinking mechanism and guide the preparation of fracturing fluid gels with an excellent performance. However, the commonly used high-temperature rheology method for evaluating the performance of fracturing fluids only qualitatively reflects the degree of crosslinking. In this study, low-field nuclear magnetic resonance (LF-NMR) was used to characterize the degree of crosslinking in guar gum fracturing fluid gels. The spin-spin relaxation time of the H proton in guar gum was molecularly analyzed using LF-NMR. The viscoelastic properties met the requirements when the crosslinking degree of the gel was 88–94 %. The transformation of the linear structure into a membrane structure during the crosslinking process of the guar gum fracturing fluid was confirmed by freeze-drying and scanning electron microscopy (SEM) from a microscopic perspective. The changing trend of the microstructure and viscoelastic properties of the fracturing fluid gel under different crosslinker dosages was consistent with changes in the degree of crosslinking. The LF-NMR test process is non-destructive to the gel structure, and the test results demonstrate good accuracy and repeatability.