T1 Images Research Articles

Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping.

The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.

Deep Learning Based on Enhanced MRI T1 Imaging to Differentiate Small-cell and Non-small-cell Primary Lung Cancers in Patients with Brain Metastases.

To differentiate the primary small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) for patients with brain metastases (BMs) based on a deep learning (DL) model using contrast-enhanced magnetic resonance imaging (MRI) T1 weighted (T1CE) images. Out of 711 patients with BMs of lung cancer origin (SCLC 232, NSCLC 479), the MRI datasets of 192 patients (lesions' widths and heights > 30 pixels) with BMs from lung cancer (73 SCLC and 119 NSCLC) confirmed pathologically were enrolled, retrospectively. A typical convolutional neural network ResNet18 was applied for the automatic classification of BMs lesions from lung cancer based on T1CE images, with training and testing groups randomized per patient to eliminate learning bias. A 5-fold cross-validation was performed to evaluate the classification of the model. The receiver operating characteristic (ROC) curve, accuracy, precision, recall and f1 score were calculated. For a 5-fold cross-validation test, the DL model achieved AUCs of 0.8019 and 0.8024 for SCLC and NSCLC patients with BMs, respectively, and a mean overall accuracy of 0.7515±0.04. The DL model performed well in differentiating the primary SCLC and NSCLC with BMs. The proposed DL model is feasible and effective in differentiating the pathological subtypes of SCLC and NSCLC causing BMs, which may be used as a new tool for oncologists to diagnose noninvasively BMs and guide therapy based on the imaging structure of tumors.

Endoscopic endonasal marsupialization of rathke cleft cysts: clinical outcomes and risk factors analysis of visual impairment, pituitary dysfunction, and CSF leak.

This paper assesses the clinical and imaging characteristics, histopathological findings, and treatment outcomes of patients with Rathke's cleft cyst (RCC), as well as identifies potential risk factors for preoperative visual and pituitary dysfunction, intraoperative cerebrospinal fluid (CSF) leak, and recurrence. Through analyzing these factors, the study aims to contribute to the current understanding of the management of RCCs and identify opportunities for improving patient outcomes. We performed a retrospective analysis of 45 RCC patients between ages 18-80 treated by Endoscopic Endonasal Approach (EEA) and cyst marsupialization between 2010 and 2022 at a single institution. The median patient age was 34, and 73% were female. The mean follow-up was 70 ± 43 months. Preoperative visual impairment correlated with cyst diameter (OR = 1.41, 95% CI = 1.07 to 1.85, p-value = 0.01) and older age (OR = 1.06, 95% CI = 1.01 to 1.11, p-value = 0.02). Intraoperative CSF leaks were 11 times more likely for cysts ≥ 2cm (OR = 11.3, 95% CI = 1.25 to 97.37, p-value = 0.03), with the odds of leakage doubling for every 0.1cm increase in cyst size (OR = 1.41, 95% CI = 1.08 to 1.84, p-value = 0.01). Preoperative RCC appearing hypointense on T1 images demonstrated significantly higher CSF leak rates than hyperintense lesions (OR = 122.88, 95% CI = 1.5 to 10077.54, p-value = 0.03). Preoperative pituitary hypofunction was significantly more likely in patients with the presence of inflammation on histopathology (OR = 20.53, 95% CI = 2.20 to 191.45, p-value = 0.008 ) and T2 hyperintensity on magnetic resonance imaging (MRI) sequences (OR = 23.2, 95% CI = 2.56 to 211.02, p-value = 0.005). Notably, except for the hyperprolactinemia, no postoperative improvement was observed in pituitary function. Carefully considering risk factors, surgeons can appropriately counsel patients and deliver expectations for complications and long-term results. In contrast to preoperative visual impairment, preoperative pituitary dysfunction was found to have the least improvement post-surgery. It was the most significant permanent complication, with our data indicating the link to the cyst signal intensity on T2 MR and inflammation on histopathology. Earlier surgical intervention might improve the preservation of pituitary function.

The Role of Improved Motion-Sensitized Driven Equilibrium Blood Suppression and Fat Saturation on T2 Relaxation Time, Using GraSE Sequence in Cardiac Magnetic Resonance Imaging.

T2 mapping is a valuable technique in cardiac MR imaging that offers insights into the microstructural characteristics of myocardial tissue. However, it was shown that myocardial T2 relaxation times (T2) measured vary significantly depending on sequence, sequence parameters, and field strength. To assess T2 variability and image quality in cardiac T2 maps using four variants of the gradient-spin echo (GraSE) sequence, having different methods of blood signal suppression (double inversion recovery (DIR) and improved motion-sensitized driven equilibrium (iMSDE) and with and without the addition of fat saturation (FS). Prospective. 48 healthy volunteers (46.7 +/- 21.5 years, 24 male) with no cardiac history. GraSE sequence with DIR (GraSEDIR), with iMSDE (GraSEiMSDE) and FS (GraSEDIR-FS) and with both iMSDE and FS (GraSEiMSDE-FS) at 1.5T. Global T2 from three short axis myocardial slices. and image quality assessments using a 5-point Lickert scale (1, (non-diagnostic) to 5, (excellent)) were conducted to evaluate the impact of DB and FS techniques on myocardial T2 measurements and image quality. Paired t-tests or non-parametric equivalents for comparisons between sequences. The Bland-Altmann plots and Pearson rank correlation analyses, as appropriate. A P value <0.05 was considered statistically significant. The mean global T2 values for GraSEDIR, GraSEDIR-FS, GraSEiMSDE, and GraSEiMSDE-FS, were 52.84 ± 5.72 msec, 54.98 ± 3.59 msec, 53.9 ± 4.05 msec, and 55.14 ± 4.28 msec, respectively, with no significant differences (P = 0.092). High image quality scores (>4 out of 5) were obtained for all sequence variants with no significant differences between them (P = 0.11). All GraSE sequence variants exhibited approximately the same results and variations in the DB technique and addition of FS did not have significant impact on myocardial T2 values. 2 TECHNICAL EFFICACY: Stage 1.

ROI-based analysis of diffusion indices in healthy subjects and subjects with deficit or non-deficit syndrome schizophrenia

We analyzed DTI data involving 22 healthy subjects (HC), 15 patients with deficit syndrome schizophrenia (DSZ), and 25 patients with non-deficit syndrome schizophrenia (NDSZ). We used a 1.5-T MRI scanner to collect diffusion-weighted images and T1 images, which were employed to correct distortions and deformations within the diffusion-weighted images. For 156 regions of interest (ROI), we calculated the average fractional anisotropy (FA), mean diffusion (MD), and radial diffusion (RD). Each ROI underwent a group-wise comparison using permutation F-test, followed by post hoc pairwise comparisons with Bonferroni correction. In general, we observed lower FA in both schizophrenia groups compared to HC (i.e., HC>(DSZ=NDSZ)), while MD and RD showed the opposite pattern. Notably, specific ROIs with reduced FA in schizophrenia patients included bilateral nucleus accumbens, left fusiform area, brain stem, anterior corpus callosum, left rostral and caudal anterior cingulate, right posterior cingulate, left thalamus, left hippocampus, left inferior temporal cortex, right superior temporal cortex, left pars triangularis and right lingual gyrus. Significantly, the right cuneus exhibited lower FA in the DSZ group compared to other groups ((HC=NDSZ)>DSZ), without affecting MD and RD. These results indicate that compromised neural integrity in the cuneus may contribute to the pathophysiological distinctions between DSZ and NDSZ.

Deep learning-driven MRI trigeminal nerve segmentation with SEVB-net.

Trigeminal neuralgia (TN) poses significant challenges in its diagnosis and treatment due to its extreme pain. Magnetic resonance imaging (MRI) plays a crucial role in diagnosing TN and understanding its pathogenesis. Manual delineation of the trigeminal nerve in volumetric images is time-consuming and subjective. This study introduces a Squeeze and Excitation with BottleNeck V-Net (SEVB-Net), a novel approach for the automatic segmentation of the trigeminal nerve in three-dimensional T2 MRI volumes. We enrolled 88 patients with trigeminal neuralgia and 99 healthy volunteers, dividing them into training and testing groups. The SEVB-Net was designed for end-to-end training, taking three-dimensional T2 images as input and producing a segmentation volume of the same size. We assessed the performance of the basic V-Net, nnUNet, and SEVB-Net models by calculating the Dice similarity coefficient (DSC), sensitivity, precision, and network complexity. Additionally, we used the Mann-Whitney U test to compare the time required for manual segmentation and automatic segmentation with manual modification. In the testing group, the experimental results demonstrated that the proposed method achieved state-of-the-art performance. SEVB-Net combined with the ωDoubleLoss loss function achieved a DSC ranging from 0.6070 to 0.7923. SEVB-Net combined with the ωDoubleLoss method and nnUNet combined with the DoubleLoss method, achieved DSC, sensitivity, and precision values exceeding 0.7. However, SEVB-Net significantly reduced the number of parameters (2.20 M), memory consumption (11.41 MB), and model size (17.02 MB), resulting in improved computation and forward time compared with nnUNet. The difference in average time between manual segmentation and automatic segmentation with manual modification for both radiologists was statistically significant (p < 0.001). The experimental results demonstrate that the proposed method can automatically segment the root and three main branches of the trigeminal nerve in three-dimensional T2 images. SEVB-Net, compared with the basic V-Net model, showed improved segmentation performance and achieved a level similar to nnUNet. The segmentation volumes of both SEVB-Net and nnUNet aligned with expert annotations but SEVB-Net displayed a more lightweight feature.

Relationship between Eccentricity and Volume Determined by Spectral Algorithms Applied to Spatially Registered Bi-Parametric MRI and Prostate Tumor Aggressiveness: A Pilot Study.

(1) Background: Non-invasive prostate cancer assessments using multi-parametric MRI are essential to the reliable detection of lesions and proper management of patients. While current guidelines call for the administration of Gadolinium-containing intravenous contrast injections, eliminating such injections would simplify scanning and reduce patient risk and costs. However, augmented image analysis is necessary to extract important diagnostic information from MRIs. Purpose: This study aims to extend previous work on the signal to clutter ratio and test whether prostate tumor eccentricity and volume are indicators of tumor aggressiveness using bi-parametric (BP)-MRI. (2) Methods: This study retrospectively processed 42 consecutive prostate cancer patients from the PI-CAI data collection. BP-MRIs (apparent diffusion coefficient, high b-value, and T2 images) were resized, translated, cropped, and stitched to form spatially registered BP-MRIs. The International Society of Urological Pathology (ISUP) grade was used to judge cases of prostate cancer as either clinically significant prostate cancer (CsPCa) (ISUP ≥ 2) or clinically insignificant prostate cancer (CiPCa) (ISUP < 2). The Adaptive Cosine Estimator (ACE) algorithm was applied to the BP-MRIs, followed by thresholding, and then eccentricity and volume computations, from the labeled and blobbed detection maps. Then, univariate and multivariate linear regression fittings of eccentricity and volume were applied to the ISUP grade. The fits were quantitatively evaluated by computing correlation coefficients (R) and p-values. Area under the curve (AUC) and receiver operator characteristic (ROC) curve scores were used to assess the logistic fitting to CsPCa/CiPCa. (3) Results: Modest correlation coefficients (R) (>0.35) and AUC scores (0.70) for the linear and/or logistic fits from the processed prostate tumor eccentricity and volume computations for the spatially registered BP-MRIs exceeded fits using the parameters of prostate serum antigen, prostate volume, and patient age (R~0.17). (4) Conclusions: This is the first study that applied spectral approaches to BP-MRIs to generate tumor eccentricity and volume metrics to assess tumor aggressiveness. This study found significant values of R and AUC (albeit below those from multi-parametric MRI) to fit and relate the metrics to the ISUP grade and CsPCA/CiPCA, respectively.

A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images.

To design a deep learning-based framework for automatic segmentation and detection of intracranial aneurysms (IAs) on magnetic resonance T1 images and test the robustness and performance of framework. A retrospective diagnostic study was conducted based on 159 IAs from 136 patients who underwent the T1 images. Among them, 127 cases were randomly selected for training and validation, and 32 cases were used to assess the accuracy and consistency of our algorithm. We developed and assembled three convolutional neural networks for the segmentation and detection of IAs. The segmentation and detection performance of the model were compared with the ground truth, and various metrics were calculated at the voxel level, IAs level, and patient level to show the performance of our framework. Our assembled model achieved overall Dice, voxel-level sensitivity, specificity, balanced accuracy, and F1 score of 0.802, 0.874, 0.9998, 0.937, and 0.802, respectively. A coincidence greater than 0.7 between the aneurysms predicted by the model and the ground truth was considered as a true positive. For IAs detection, the sensitivity reached 90.63% with 0.58 false positives per case. The volume of IAs segmented by our model showed a high agreement and consistency with the volume of IAs labeled by experts. The deep learning framework is achievable and robust for IAs segmentation and detection. Our model offers more clinical application opportunities compared to digital subtraction angiography (DSA)-based, CTA-based, and MRA-based methods. Our deep learning framework effectively detects and segments intracranial aneurysms using clinical routine T1 sequences, showing remarkable effectiveness and offering great potential for improving the detection of latent intracranial aneurysms and enabling early identification. •There is no segmentation method based on clinical routine T1 images. Our study shows that the proper deep learning framework can effectively localize the intracranial aneurysms. •The T1-based segmentation and detection method is more universal than other angiography-based detection methods, which can potentially reduce missed diagnoses caused by the absence of angiography images. •The deep learning framework is robust and has the potential to be applied in a clinical setting.

Amino-grafted water-soluble ferrimagnetic iron oxide nanoparticles with ultra-high relaxivity for dual-modal magnetic resonance imaging

Highly stable dispersion is an important precondition for functionalized magnetic nanoparticles used as a contrast agent. Amino-grafted ferromagnetic γ-Fe2O3 nanoparticles are synthesized by deep-eutectic-solvent electrolysis for magnetic resonance imaging. The statistical naked scale of the particle is 8.2 nm with a magnetization of 71.9 emu g−1. An ultra-high Zeta potential of + 50 mV boosts the dispersion stable for more than 600 days, which is unprecedented. The 26.4 nm hydrated nanoparticles exhibit high biocompatibility after being efficiently ingested by cells in vitro. Both longitudinal relaxation efficiency of 179.7 mM−1 s−1 and transverse relaxivity of 497.8 mM−1 s−1 are among the highest demonstrated in recent years. Whether in vitro or in vivo, the imaging mode changes from T1 to T2 with the increase of iron equivalent. At doses 0.12 and 0.91 mg Fe per kg living body, the nanoparticles are capable of prominently weighted imaging of T1 and T2 of the liver, respectively. The nanocrystals can significantly distinguish between normal tissue and the tumor site of the liver as well. Findings demonstrate that the highly stable dispersion of amino-grafted ferrimagnetic nanoparticles is capable of dual-modal magnetic resonance imaging with high relaxation efficiency and significant resolution.

Resolving heterogeneity in Alzheimer's disease based on individualized structural covariance network

The heterogeneity of Alzheimer's disease (AD) poses a challenge to precision medicine. We aimed to identify distinct subtypes of AD based on the individualized structural covariance network (IDSCN) analysis and to research the underlying neurobiology mechanisms. In this study, 187 patients with AD (age = 73.57 ± 6.00, 50% female) and 143 matched normal controls (age = 74.30 ± 7.80, 44% female) were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project database, and T1 images were acquired. We utilized the IDSCN analysis to generate individual-level altered structural covariance network and performed k-means clustering to subtype AD based on structural covariance network. Cognition, disease progression, morphological features, and gene expression profiles were further compared between subtypes, to characterize the heterogeneity in AD. Two distinct AD subtypes were identified in a reproducible manner, and we named the two subtypes as slow progression type (subtype 1, n = 104, age = 76.15 ± 6.44, 42% female) and rapid progression type (subtype 2, n = 83, age = 71.98 ± 8.72, 47% female), separately. Subtype 1 had better baseline visuospatial function than subtype 2 (p < 0.05), whereas subtype 2 had better baseline memory function than subtype 1 (p < 0.05). Subtype 2 showed worse progression in memory (p = 0.003), language (p = 0.003), visuospatial function (p = 0.020), and mental state (p = 0.038) than subtype 1. Subtype 1 often shared increased structural covariance network, mainly in the frontal lobe and temporal lobe regions, whereas subtype 2 often shared increased structural covariance network, mainly in occipital lobe regions and temporal lobe regions. Functional annotation further revealed that all differential structural covariance network between the two AD subtypes were mainly implicated in memory, learning, emotion, and cognition. Additionally, differences in gray matter volume (GMV) between AD subtypes were identified, and genes associated with GMV differences were found to be enriched in the terms potassium ion transport, synapse organization, and histone modification and the pathways viral infection, neurodegeneration-multiple diseases, and long-term depression. The two distinct AD subtypes were identified and characterized with neuroanatomy, cognitive trajectories, and gene expression profiles. These comprehensive results have implications for neurobiology mechanisms and precision medicine.

Development of neonatal-specific sequences for portable ultralow field magnetic resonance brain imaging: a prospective, single-centre, cohort study

Magnetic Resonance (MR) imaging is key for investigation of suspected newborn brain abnormalities. Access is limited in low-resource settings and challenging in infants needing intensive care. Portable ultralow field (ULF) MRI is showing promise in bedside adult brain imaging. Use in infants and children has been limited as brain-tissue composition differences necessitate sequence modification. The aim of this study was to develop neonatal-specific ULF structural sequences and test these across a range of gestational maturities and pathologies to inform future validation studies. Prospective cohort study within a UK neonatal specialist referral centre. Infants undergoing 3T MRI were recruited for paired ULF (64mT) portable MRI by convenience sampling from the neonatal unit and post-natal ward. Key inclusion criteria: 1) Infants with risk or suspicion of brain abnormality, or 2) preterm and term infants without suspicion of major genetic, chromosomal or neurological abnormality. Exclusions: presence of contra-indication for MR scanning. ULF sequence parameters were optimised for neonatal brain-tissues by iterative and explorative design. Neuroanatomic and pathologic features were compared by unblinded review, informing optimisation of subsequent sequence generations in a step-wise manner. Main outcome: visual identification of healthy and abnormal brain tissues/structures. ULF MR spectroscopy, diffusion, susceptibility weighted imaging, arteriography, and venography require pre-clinical technical development and have not been tested. Between September 23, 2021 and October 25, 2022, 102 paired scans were acquired in 87 infants; 1.17 paired scans per infant. Median age 9 days, median postmenstrual age 40+2 weeks (range: 31+3-53+4). Infants had a range of intensive care requirements. No adverse events observed. Optimised ULF sequences can visualise key neuroanatomy and brain abnormalities. In finalised neonatal sequences: T2w imaging distinguished grey and white matter (7/7 infants), ventricles (7/7), pituitary tissue (5/7), corpus callosum (7/7) and optic nerves (7/7). Signal congruence was seen within the posterior limb of the internal capsule in 10/11 infants on finalised T1w scans. In addition, brain abnormalities visualised on ULF optimised sequences have similar MR signal patterns to 3T imaging, including injury secondary to infarction (6/6 infants on T2w scans), hypoxia-ischaemia (abnormal signal in basal ganglia, thalami and white matter 2/2 infants on T2w scans, cortical highlighting 1/1 infant on T1w scan), and congenital malformations: polymicrogyria 3/3, absent corpus callosum 2/2, and vermian hypoplasia 3/3 infants on T2w scans. Sequences are susceptible to motion corruption, noise, and ULF artefact. Non-identified pathologies were small or subtle. On unblinded review, optimised portable MR can provide sufficient contrast, signal, and resolution for neuroanatomical identification and detection of a range of clinically important abnormalities. Blinded validation studies are now warranted. The Bill and Melinda Gates Foundation, the MRC, the Wellcome/EPSRC Centre for Medical Engineering, the MRC Centre for Neurodevelopmental Disorders, and the National Institute for Health Research (NIHR) Biomedical Research Centres based at Guy's and St Thomas' and South London & Maudsley NHS Foundation Trusts and King's College London.

Comparison of Contrast-Enhanced 3D Imaging with 2D Imaging in the Evaluation of Perianal Fistula at 3T: A Prospective Observational Study.

Introduction Perianal fistula and its recurrence is a challenging entity for surgeons. It is a well-establi1shed fact that magnetic resonance imaging (MRI) findings influence surgical procedures and reduce the rate of recurrence. In this study, we assessed the contrast-enhanced (CE) three dimensional T1 sequences [sampling perfection with application optimized contrast using different flip angle evolution (SPACE) and volumetric interpolated breath-hold examination (VIBE)] in a 3T MRI system to evaluate perianal fistulas and compared them with two-dimensional (2D) sequences. Materials and Methods Forty-four patients (mean age, 38.8 ± 15.3 [standard deviation]; 32 males, 12 females) with perianal fistula were included in this prospective study. The patients underwent conventional noncontrast, 2D sequences, diffusion-weighted imaging, followed by postcontrast, 2D T1 images in both axial and coronal planes, and 3D sequences at 3T. Acquisition times were recorded for each sequence. Each postcontrast sequence was evaluated in terms of image quality, presence of artifacts, fistula type, presence of an abscess, visibility, and number of internal orifices. The surgical findings were considered the gold standard. The imaging findings were compared with the surgical findings. Results In all three sequences, the fistula type and abscess were identified correctly in all patients. The sensitivity value for identification of ramifications utilizing CE 3D T1 VIBE sequence, CE 3D T1 SPACE, and CE 2D T1 images was 100, 86, and 36%, respectively. The number of internal orifices was identified by the CE 3D T1 VIBE and CE 3D T1 SPACE sequences in 100 and 92% of patients, respectively. CE 2D T1 images correctly identified internal orifices in 80% of patients. The overall scan time for each 3D sequence was shorter than for the combined postcontrast 2D sequences. Conclusion CE 3D T1 SPACE and CE 3D T1 VIBE sequences outperformed conventional CE 2D sequences in the evaluation of perianal fistulas in terms of visibility and the number of internal orifices with a shorter scanning time. Among the 3D sequences, CE 3D T1 VIBE is slightly superior to CE 3D T1 SPACE sequence.

Lateral Meniscal Allograft Transplantation Shows a Long-Term Chondroprotective Effect on Quantitative Magnetic Resonance Imaging T2 Mapping at 7-Year Minimum Follow-Up

PurposeThe purpose of this study was to assess the long-term chondroprotective effect of lateral MAT (meniscal allograft transplantation) using quantitative MRI (magnetic resonance imaging) T2 mapping. MethodsIn patients who underwent isolated lateral MAT, quantitative MRI T2 mapping was conducted preoperatively and postoperatively with at minimum follow-up of 7 years to assess the articular cartilage status. On the sagittal section image bisecting the lateral femoral condyle, the weight-bearing portions of the femoral and tibial articular cartilage were divided into three segments each—six segments in total—based on the meniscal coverage area. The regions of interest analysis were performed on the six segments to measure the mean T2 value. Then, the whole layer was divided into deep and superficial layers for further zonal analysis. The longitudinal change in T2 values was statistically analyzed using paired t-tests. Clinical outcome was evaluated using the Lysholm score. ResultsA total of 31 patients were included in the study, with the MRI follow-up period of a minimum follow-up of 7 years (mean 8.9±1.3 years; range, 7.0–11.2 years). The mean T2 value of the whole layer showed significant improvement in all segments of the femoral cartilage and the posterior segment of tibial cartilage. In the zonal analysis, the mean T2 value of the tibial cartilage showed significant improvement in the superficial layer of the mid to posterior portion, while the deep layer remained stable. In contrast, the mean T2 value of the femoral cartilage showed significant improvement in the superficial and deep layers in all segments. The mean Lysholm score significantly improved from 62.6±12.8 to 90.9±10.5 (P < .001). ConclusionThis study suggests that MAT appears to have a long-term chondroprotective effect on the articular cartilage as judged by quantitative T2 mapping. Level of EvidenceLevel Ⅳ, Case series.

Cranial MRgFUS intraprocedural diffusion and T2 imaging and comparison with postablation lesion size and location.

The ability to predict final lesion characteristics during magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy for the treatment of essential tremor remains technically challenging, yet it is essential in order to avoid off-target ablation and to ensure adequate treatment. The authors sought to evaluate the technical feasibility and utility of intraprocedural diffusion-weighted imaging (DWI) in the prediction of final lesion size and location. Lesion diameter and distance from the midline were measured on both intraprocedural and immediate postprocedural diffusion and T2-weighted sequences. Bland-Altman analysis was utilized to determine differences in measurement between intraprocedural and immediate postprocedural images with both sequences. Lesion size increased on both the postprocedural diffusion and T2-weighted sequences, although the difference was smaller on the T2-weighted sequence. There was only a small difference in intraprocedural and postprocedural lesion distance from the midline on both the diffusion and T2-weighted sequences. Intraprocedural DWI is both feasible and useful with regard to predicting final lesion size and providing an early indication of lesion location. Further research should determine the value of intraprocedural DWI in predicting delayed clinical outcomes.

Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3T MRI.

Precise delineation of hippocampus subfields is crucial for the identification and management of various neurological and psychiatric disorders. However, segmenting these subfields automatically in routine 3T MRI is challenging due to their complex morphology and small size, as well as the limited signal contrast and resolution of the 3T images. This research proposes Syn_SegNet, an end-to-end, multitask joint deep neural network that leverages ultrahigh-field 7T MRI synthesis to improve hippocampal subfield segmentation in 3T MRI. Our approach involves two key components. First, we employ a modified Pix2PixGAN as the synthesis model, incorporating self-attention modules, image and feature matching loss, and ROI loss to generate high-quality 7T-like MRI around the hippocampal region. Second, we utilize a variant of 3D-U-Net with multiscale deep supervision as the segmentation subnetwork, incorporating an anatomic weighted cross-entropy loss that capitalizes on prior anatomical knowledge. We evaluate our method on hippocampal subfield segmentation in paired 3T MRI and 7T MRI with seven different anatomical structures. The experimental findings demonstrate that Syn_SegNet's segmentation performance benefits from integrating synthetic 7T data in an online manner and is superior to competing methods. Furthermore, we assess the generalizability of the proposed approach using a publicly accessible 3T MRI dataset. The developed method would be an efficient tool for segmenting hippocampal subfields in routine clinical 3T MRI.

MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data.

Large-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is naturally extendable to more than two scanners. We also designed a set of criteria to investigate the scanner-related technical variability and evaluate the harmonization techniques. As an essential requirement of our criteria, we introduced a multi-scanner matched dataset of 3T T1 images across four scanners, which, to the best of our knowledge is one of the few datasets of this kind. We also investigated our evaluations using two popular segmentation frameworks: FSL and segmentation in statistical parametric mapping (SPM). Lastly, we compared MISPEL to popular methods of normalization and harmonization, namely White Stripe, RAVEL, and CALAMITI. MISPEL outperformed these methods and is promising for many other neuroimaging modalities.

Feature Selection Based on Unsupervised Clustering Mechanism on Multiple-Sequence MRIs for Predicting Neoadjuvant Chemoradiation Response in Locally Advanced Rectal Cancer

Accurate response prediction allows for personalized cancer management. We developed an unsupervised clustering mechanism to improve effectiveness and efficiency in feature selection operation for accurate patient stratification. Forty-three locally advanced rectal cancer (LARC) patients underwent neoadjuvant chemoradiation were included, pre-treatment T2 and ADC MRIs were acquired for each patient. An initial feature space consisting of 200 radiomic features extracted from manually delineated GTVs from two sequences of MR images. Additional 960 high-order radiomic features extracted from a 3D convolutional neural network (CNN). To remove redundant and irrelevant features, we developed an unsupervised clustering-based feature selection operation to determine the combination of features with potential best performance. The normal process of feature selection involves searching new feature combinations and training new classifiers for evaluating their performance via an iterative process based on selected feature set, the overall time cost is tremendous. To balance the computational cost and search efficiency, firstly, we proposed an unsupervised clustering analysis metric- Comprehensive Cluster Analysis Index (CCAI) through the K-means algorithm, where the average distances between the sample points and the cluster centroids and so on, to construct a multiple linear regression model. Secondly, we extracted sample points by varying the number of features and feature ratios between radiomic features and 3D-CNN features in the output of feature selection. Thirdly, we optimized the model using the sampling points to calculate the CCAI. Two typical feature combination search algorithms, the random forest recursive feature elimination (RF-RFE) and the differential evolution (DE), were used to perform feature selection with CCAI. The accuracy, area-under-curve (AUC) and specificity, based on combined 3D-CNN and radiomic features extracted from combined T2 and ADC images, were 0.852, 0.871, and 0.735, respectively. Our experiments illustrated higher predictive power (AUC = 0.846) based on high-order abstract features extracted from the CNN on ADC and T2 images, compared to the traditional radiomic model (AUC = 0.714). Additionally, the predictive models constructed based on radiomics and CNN features extracted from ADC images were more predictable in terms of treatment responses than the radiomic and CNN imaging features extracted from T2 images. The average computational time of DE and RF-RFE were 50.5s and 128.6s in one single computation, the average computational time were 24.2s and 91.3s with CCAI, respectively. We proposed an unsupervised clustering analysis mechanism to improve the effectiveness of feature selection while decreasing its time cost markedly, which highlight the correlation and complementarity between low- and high-level imaging features, achieving better predictive accuracy.

Contouring Analysis on Synthetic Contrast-Enhanced MR from GRMM-GAN and Implications on MR-Guide Radiation Therapy

MR-guided linear accelerators have been commercialized making MR-only planning and adaptation an appealing alternative circumventing MR-CT registration. However, obtaining daily contrast-enhanced MR images can be prohibitive due to the increased risk of side effects from repeated contrast injections. In this work, we evaluate the quality of contrast-enhanced multi-modal MR image synthesis network GRMM-GAN (gradient regularized multi-modal multi-discrimination sparse-attention fusion generative adversarial network) for MR-guided radiation therapy. With IRB approval, we trained the GRMM-GAN based on 165 abdominal MR studies from 65 patients. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast enhanced (T1ce) images. The two pre-contrast MR modalities, T2 and T1pre images were adopted as inputs for GRMM-GAN, and the T1ce image at the portal venous phase was used as an output. Ten MR scans containing 21 liver tumors were selected for contouring analysis. A Turing test was first given to six radiation oncologists, in which 100 real T1ce and synthetic T1ce image slices are randomly given to the radiation oncologists to determine the authenticity of the synthesis. We then invited two radiation oncologists (RadOnc 1 and RadOnc2) to manually contour the 21 liver tumors independently on the real T1ce images. RadOnc2 then performed contouring on the respective synthetic T1ce MRs. DICE coefficient (defined as the intersection over the average of two volumes) and Hausdorff distance (HD, measuring how far two volumes are from each other) were used as analysis metrics. The DICE coefficients were calculated from the two radiation oncologists' contours on the real T1ce MR for each tumor. The DICE coefficients were also calculated from RadOnc 2's contours on real and synthetic MRs. Besides, tumor center shifts were extracted. The tumor center of mass coordinates was extracted from real and synthetic volumes. The difference in the coordinates indicated the shifts in the superior-inferior (SI), right-left (RL), and anterior-posterior (AP) directions between real and synthetic tumor volumes. An average of 52.3% test score was achieved from the six radiation oncologists, which is close to random guessing. RadOnc 1 and RadOnc 2, who had participated in the contouring analysis, achieved an average DICE of 0.91±0.02 from tumor volumes drawn on the real T1ce MRs. This result sets the inter-operator uncertainty baseline in the real clinical setting. RadOnc 2 achieved an average DICE (real vs. synth) of 0.90±0.04 and HD of 4.76±1.82 mm. Only sub-millimeter (SI: 0.67 mm, RL: 0.41 mm, AP: 0.39 mm) tumor center shifts were observed in all three directions. The GRMM-GAN method has the potential for MR-guided liver radiation when contrast agents cannot be administered daily and provide synthetic contrast-enhanced MR for better tumor targeting. The network can produce synthetic MR images with satisfactory contour agreement and geometric integrity.

Investigation of Ensemble Learning Method in Predicting Early Xerostomia Using Radiomics Features from CT and MRI

There are different side effects in radiotherapy of head and neck cancer including xerostomia. Xerostomia is due to irradiation of salivary glands during treatment. In the present study, the main aim is evaluation and validation of individual and ensemble classifiers for prediction of early xerostomia in radiotherapy of head and neck cancer. Totally 62 patients diagnosed with head and neck cancer were evaluated prospectively. For this purpose, patients' demographic information, dosimetric data, and the extracted features from CT and T1 and T2 MR images before radiotherapy treatments were used as input for the predictive model. There were two main steps in this study: 1- Comparison of the features from T1 and T2 MR images for prediction of xerostomia, and 2- Evaluation of the performance of the radiomics models using individual classifiers and combination of the classifiers using ensemble learning. Feature extraction was performed using 3D Slicer software and for both parotids, 642 features were extracted from both CT, and MRI and Pearson statistical test was used for feature selection. For model development, four models were used consist of Random Tree (RT), Neural Network (NN), Linear Support Vector Machine (LSVM) and Bayesian Network (BS) classifiers. Different algorithms combined by using the ensemble learning method. For evaluation of the performance of the constructed models, sensitivity, specificity, area under curve (AUC), and receiver operating characteristic (ROC) curve were utilized. The results of the present study show that the extracted features from T1 weighted images have superior prediction ability compared to the T2 weighted ones. The RT and LSVM models which were based on T1 weighted images have better performance than those with T2 weighted images. The AUC in the test group for these models are 0.95 and 0.76, respectively, while for the T2 weighted images the corresponding values are 0.75 and 0.63. The AUC for the test group for the RT-BN, RT-LSVM-BN and RT-NN-LSVM-BN models amount to 0.96, 0.91, and 0.88, respectively. The results show that the radiomics features from images before radiotherapy can be used as personalized and unique biomarkers for prediction of xerostomia. Ensemble classifiers can be more efficient than individual classifiers in prediction of early xerostomia.

Assessment of Dynamic Contrast Enhanced (DCE) MRI for Detection of Radiotherapy Induced Alteration in Mandibular Vasculature

We aim to determine the kinetics of DCE-MRI changes in various mandibular risk volumes based on radiation (RT) dose received. Eighty-eight head and neck cancer (HNC) patients (Pts) who underwent definitive RT were enrolled in this prospective study after IRB approval and informed consent. Images were acquired at pre-RT (Baseline), 3 weeks after RT start date (Mid-RT), 3 mos post-RT (PostRT1), and 6 mos post-RT (PostRT2). Manually segmented mandibular volumes on T2-weighted images were propagated to co-registered DCE-MRIs. Planning CTs and dose grids were also co-registered to corresponding baseline T2 images to create 3-D dose subvolumes. These were used to create 3 risk subvolumes; <30 Gy, 30-50 Gy, and >50 Gy ROIs. DCE images of different timepoints (TPs) were deformably co-registered and the dose subvolumes were propagated to each TP. We used the extended-Tofts model to generate the vascular quantitative maps (Ktrans and Ve). Each subvolume histogram parameters were extracted at each TP. Wilcoxon Signed Rank test was used to compare the changes at different TPs compared to baseline. We classified Pts' delta parameters at different TPs -based on our prior extensive QA assessment- into Pts with stable vascular profile (±25% change), Pts with significant increase (>25% change) and Pts with significant decrease (<-25%). Chi-square test was used to assess the change at different TPs. For <30 Gy subvolumes, there were no significant changes (p > 0.05) in the studied DCE parameters at all TPs except a significant decrease (p < 0.001) in median Ktrans at PostRT2. For 30-50 Gy subvolumes, there was a significant increase in median Ktrans that started at MidRT (p = 0.006) and continued at PostRT1 (p = 0.04) but recovered to baseline values at PostRT2. Median Ve on the other hand only showed significant increase at PostRT1 (p = 0.001), but other TPs were not significantly different compared to baseline. Similarly, subvolumes >50 Gy showed same kinetics as in 30-50 Gy with significant increase of Ktrans at MidRT and PostRT1 and significant increase in Ve in only PostRT1 (P <0.05). For <30 Gy, there was significant increase in the number of Pts with stable or decrease in Ktrans at PostRT2 compared to earlier TPs (70% vs. 60% at PostRT1 and 54% at MidRT p = 0.003). 30-50 Gy subvolumes showed similar profile like <30 Gy with significant increase in the percentage of Pts with recovery at PostRT2. However, for >50 Gy, there was no significant increase in the number of Pts who recovered at PostRT2 (p = 0.3). Ve showed no significant increase in the percentage of Pts with recovery at different TPs (p > 0.05). Results showed that for all dose mandibular subvolumes, there is an acute vascular insult that tends to recover at +6 months post-RT except for a selective group of patients who continue to have persistence of the vascular insult at high dose subvolumes. These findings are of importance for future selection of high risk population for prophylactic intervention against osteoradionecrosis.