T2w Magnetic Resonance Imaging Research Articles

Comparative analysis of Voxel-based morphometry using T1 and T2-weighted magnetic resonance imaging to explore the relationship between brain structure and cognitive abilities.

Voxel-based morphometry (VBM) of T1-weighted (T1-w) magnetic resonance imaging (MRI) is primarily used to study the association of brain structure with cognitive functions. However, in theory, T2-weighted (T2-w) MRI could also be used in VBM studies because of its sensitivity to pathology and tissue changes. We aimed to compare the T1-w and T2-w images to study brain structures in association with cognitive abilities. VBM analysis was applied to T1-w and T2-w MRI data of 120 healthy participants aged 20 to 40. The MRI data was collected using a 3T machine, and it was analyzed with CAT12 to extract maps of Gray matter(GM). We used six cognitive tasks to assess cognitive abilities, including the balloon analog risk task (BART), block design, forward and backward digit span (FDST and BDST), and trail-making tasks A and B. Compared to T2-w, T1-w data showed more brain voxels in the BART, block design, FDST, TMT-A, and TMT-B tasks. However, T2-w imaging identified a greater number of voxels in the BDST. T1-w images identified more correlated brain regions with cognitive scores in the FDST, TMT-A, and B tasks than T2-w. In BART and Block design tasks, both methods revealed the same number of correlated regions, and T2-w just showed more regions than T1-w in the BDST. Findings revealed distinct patterns of sensitivity between T1-w and T2-w imaging in detecting brain regions associated with cognition. The two approaches demonstrated different strengths in identifying areas correlated with cognitive abilities. This study provides valuable guidance for selecting appropriate methods for identifying the optimal approach for detecting brain regions that exhibit the strongest correlations with cognitive abilities.

Facet joint tropism in degenerative lumbar scoliosis: a retrospective case-control study.

To investigate the association between lumbar degenerative scoliosis and the dural sac cross-sectional area (DSCA), the lumbar canal anterior-posterior (LCAP) diameter, and the neural foraminal cross-sectional area (NFCA) in relation to facet joint tropism (FJT). In a retrospective case-control study, we analyzed data from 160 patients referred for lumbar magnetic resonance imaging (MRI) between January 2020 and December 2022. Cobb's angle on anteroposterior lumbosacral X-ray is served to identify the presence of degenerative lumbar scoliosis-Cobb's angle exceeding 10 degrees-, and axial T2W MRI is implemented to evaluate facet joint angles and tropism-defined as a difference exceeding 10 degrees between the facet joint angles at each level-, DSCA, LCAP, and NFCA. FJT was much more common in patients with degenerative lumbar scoliosis (69%) than in controls (14%). The frequency of FJT also incremented with the increasing severity of the scoliotic curve. We observed that LCAP and NFCA were significantly smaller in cases with FJT. However, no statistically significant difference was found in DSCA related to FJT. Age and gender did not exhibit significant associations with degenerative lumbar scoliosis, and no correlation was detected between different Cobb's angle groups and age. These findings underscore the importance of considering FJT as a potential contributing factor to degenerative lumbar scoliosis and may have implications for clinical diagnosis and management. Prospective research with larger and more diverse cohorts is needed to further investigate this complex relationship and its impact on lumbar spinal health.

Boundary Complexity of (Sub-) Cortical Areas Predict Deep Brain Stimulation Outcomes in Parkinson's Disease.

While deep brain stimulation (DBS) remains an effective therapy for Parkinson's disease (PD), sources of variance in patient outcomes are still not fully understood, underscoring a need for better prognostic criteria. Here we leveraged routinely collected T1-weighted (T1-w) magnetic resonance imaging (MRI) data to derive patient-specific measures of brain structure and evaluate their usefulness in predicting changes in PD medications in response to DBS. Preoperative T1-w MRI data from 231 patients with PD were used to extract regional measures of fractal dimension (FD), sensitive to the structural complexities of cortical and subcortical areas. FD was validated as a biomarker of Parkinson's disease (PD) progression through comparison of patients with PD and healthy controls (HCs). This analysis revealed significant group differences in FD across nine brain regions which supports its utility as a marker of PD. We evaluated the impact of adding imaging features (FD) to a clinical model that included demographics and clinical parameters-age, sex, total number and location of DBS electrodes, and preoperative motor response to levodopa. This model aimed to explain variance and predict changes in medication following DBS. Regression analysis revealed that inclusion of the FD of distributed brain areas correlated with post-DBS reductions in medication burden, explaining an additional 13.6% of outcome variance (R 2 =0.388) compared to clinical features alone (R 2 =0.252). Hypergraph-based classification learning tasks achieved an area under the receiver operating characteristic curve of 0.64 when predicting with clinical features alone, versus 0.76 when combining clinical and imaging features. These findings demonstrate that PD effects on brain morphology linked to disease progression influence DBS outcomes. The work also highlights FD as a potentially useful imaging biomarker to enhance DBS candidate selection criteria for optimized treatment planning.

Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction Methods in Neonatal MRI

Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume (RBV) measurements of preterm neonates’ T2w MRI at term-equivalent age (TEA). Five BE methods (Manual, BET2, SWS, HD-BET, SynthStrip) were applied together with two BFC methods (SPM-BFC and N4ITK), before segmenting the neonatal brain into eight tissue classes (cortical grey matter, white matter, cerebral spinal fluid, deep nuclear grey matter, hippocampus, amygdala, cerebellum, and brainstem) using an automated segmentation software (MANTiS). Quantitative assessments were conducted, including the coefficient of variation (CV), coefficient of joint variation (CJV), Dice coefficient (DC), and RBV. HD-BET, together with N4ITK, showed the highest performance (mean ± standard deviation) regarding CV of 0.047 ± 0.005 (white matter) and 0.070 ± 0.005 (grey matter), CJV of 0.662 ± 0.095, DC of 0.942 ± 0.063, and RBV without significant differences (except in the brainstem) from the manual segmentation. Therefore, such combination of methods is recommended for improved skull-stripping accuracy, intensity homogeneity, and reproducibility of RBV of T2w MRI at TEA.

OVERTURE Phase 2 MRI Analysis Demonstrates Reduced Corpus Callosum Atrophy in Alzheimer's Disease

AbstractBackgroundRecent research by Da et al. (2023) has demonstrated that non‐invasive gamma sensory stimulation can reduce brain white matter atrophy and myelin content loss. The impact on the Corpus Callosum (CC), the brain's largest commissural white matter tract essential for hemispheric connectivity, remains unexplored. Diffusion Tensor Imaging (DTI) studies (Delvenne et al. 2023), have identified variations and alterations in the CC's tract bundles in Alzheimer's disease (AD). We conducted an analysis of CC changes in patients along the Alzheimer’s continuum targeting both the overall and regional structural alterations.MethodIn this study, we used neuroimaging data from the OVERTURE clinical trial (NCT03556280). Participants were randomized 2:1 (Active: Sham) to receive daily, 1‐hour, audio and visual stimulation at gamma frequency or sham stimulation for 6 months. Magnetic Resonance Imaging (MRI) assessments were performed at baseline, month 3, and month 6. T1w MRI images were processed using the Yuki module within the Automated Registration Toolbox (ART) (Ardekani et al., 2014) with CC segmentation in the midsagittal plane (MSP) and its five subregions (Hampel et al., 1998). The analysis was conducted on participants with valid data for the total MSP CC and its subregions (N=50). Using Bayesian linear mixed‐effects modeling, we examined the changes in the area of the total MSP CC and its subregions.ResultThe active group demonstrated preservation of the total CC area and its subregions after six months of treatment. Differences in percentage changes between active and sham groups were: Total CC area (2.28±0.87%, p<.02), genu/rostrum (2.36±0.90%, p<.02), anterior‐body (2.64±1.26%, p<.04), mid‐body (2.79±1.18%, p<.03), posterior‐body (2.87±1.41%, p<.05) and splenium (1.58±0.73%, p<.04). Significant differences between the two groups were also observed in total CC area, genu/rostrum, and splenium at month 3.ConclusionOVERTURE Phase 2 MRI outcomes demonstrate that six months of non‐invasive gamma sensory stimulation may reduce the progression of CC atrophy in individuals with mild‐moderate AD, potentially preserving the integrity of interhemispheric communication. We are currently conducting a phase 3 clinical trial titled HOPE (NCT05637801) to evaluate the efficacy and safety of Cognito’s medical device.

T1- and T2-weighted MRI signal and histology findings in suboptimally fixed human brains

Neuroscientific research that requires brain tissue depends on brain banks that provide very small tissue samples fixed by immersion in neutral-buffered formalin (NBF), while anatomy laboratories could provide full brain specimens. However, these brains are generally fixed by perfusion of the full body with solutions other than NBF generally used by brain banks, such as an alcohol-formaldehyde solution (AFS) that is typically used for dissection and teaching. Therefore, fixation quality of these brains needs to be assessed to determine their usefulness in post-mortem investigations through magnetic resonance imaging (MRI) and histology, two common neuroimaging modalities. Here, we report the characteristics of five brains fixed by full body perfusion of an AFS from our Anatomy Laboratory suspected of being poorly fixed, given the altered signal seen on T1w MRI scans in situ. We describe 1- the characteristics of the donors; 2- the fixation procedures applied for each case; 3- the tissue contrast characteristics of the T1w and T2w images; 4- the macroscopic tissue quality after extraction of the brains; 5- the macroscopic arterial characteristics and presence or absence of blood clots; and 6- four histological stains of the areas that we suspected were poorly fixed. We conclude that multiple factors can affect the fixation quality of the brain. Nevertheless, cases in which brain fixation is suboptimal, consequently altering the T1w signal, still have T2w of adequate gray-matter to white-matter contrast and may also be used for histology stains with sufficient quality.

Spiral volumetric optoacoustic tomography of reduced oxygen saturation in the spinal cord of M83 mouse model of Parkinson’s disease

PurposeMetabolism and bioenergetics in the central nervous system play important roles in the pathophysiology of Parkinson’s disease (PD). Here, we employed a multimodal imaging approach to assess oxygenation changes in the spinal cord of the transgenic M83 murine model of PD overexpressing the mutated A53T alpha-synuclein form in comparison with non-transgenic littermates.MethodsIn vivo spiral volumetric optoacoustic tomography (SVOT) was performed to assess oxygen saturation (sO2) in the spinal cords of M83 mice and non-transgenic littermates. Ex vivo high-field T1-weighted (T1w) magnetic resonance imaging (MRI) at 9.4T was used to assess volumetric alterations in the spinal cord. 3D SVOT analysis and deep learning-based automatic segmentation of T1w MRI data for the mouse spinal cord were developed for quantification. Immunostaining for phosphorylated alpha-synuclein (pS129 α-syn), as well as vascular organization (CD31 and GLUT1), was performed after MRI scan.ResultsIn vivo SVOT imaging revealed a lower sO2SVOT in the spinal cord of M83 mice compared to non-transgenic littermates at sub-100 μm spatial resolution. Ex vivo MRI-assisted by in-house developed deep learning-based automatic segmentation (validated by manual analysis) revealed no volumetric atrophy in the spinal cord of M83 mice compared to non-transgenic littermates at 50 μm spatial resolution. The vascular network was not impaired in the spinal cord of M83 mice in the presence of pS129 α-syn accumulation.ConclusionWe developed tools for deep-learning-based analysis for the segmentation of mouse spinal cord structural MRI data, and volumetric analysis of sO2SVOT data. We demonstrated non-invasive high-resolution imaging of reduced sO2SVOT in the absence of volumetric structural changes in the spinal cord of PD M83 mouse model.

The role of humerus cortical thickness in predicting osteoporosis in MR imaging.

This study aims to evaluate the efficacy of humerus cortical thickness on coronal T1-weighted images of the humerus in distinguishing patients with normal vs. abnormal bone mineral density (BMD). Patients (n:138) with shoulder magnetic resonance imaging (MRI) and dual-energy x-ray absorptiometry (DXA) scans were evaluated. Patients were grouped into normal and low BMD (osteopenia and osteoporosis) according to DXA. An average cortical bone thickness (CBTavg) and gauge cortical bone thickness (CBTg) were calculated from the proximal humerus on coronal T1W MRI. Sensitivity and specificity for the prediction of osteoporosis were determined for several cortical bone thickness thresholds. Proximal humerus average cortical bone thickness measurements strongly correlated with DXA femur and lumbar scores (p < 0.01). Gauge cortical thickness measurements also correlated with DXA femur and lumbar scores (p < 0.01). Average cortical bone thickness measurement of 4.52 mm was determined to be a potential marker for predicting osteoporosis, with a sensitivity of 92.3% and a specificity of 84.9%. Average cortical bone thickness measurements obtained from shoulder MRI are correlated with DXA. It appears to be effective in differentiating patients with normal and abnormal BMD and may help to opportunistically predict patients with osteoporosis in a rapid, simple and practical way, potentially guiding further diagnostic assessments.

Rare radiological manifestation of enchondromatosis in children: Columnar pattern: A retrospective cohort study.

The columnar cartilage pattern is characterized by parallel aligned cartilage tissue columns related to the physis without matrix calcification separated by the surrounding osseous tissue. Usually, it is seen in patients with multiple enchondromas. The objective of this study was to elucidate the clinical and radiological features of this rare radiological pattern in the physis, which remains unfamiliar to most physician. We retrospectively evaluated the clinical features and imaging findings of 15 patients (9 men and 6 women) who have a columnar pattern with varied spectrum of enchondromatosis. On X-ray and computed tomography (CT) examination, all these lesions were seen as vertical or oblique oriented tubular zones, which have relatively low radiologic density compared with normal bone. The lesions have similar signal characteristics relative to epiphyseal cartilage plates, on T1W and T2W magnetic resonance images. Columnar pattern was observed in different appearances from one single column in one physis to multiple columns in multiple physis. The mean follow-up was 62 months (range: 36-96 months). The mean age was 9.7 (range: 4-14) years at the initial admission. Eight patients had 3 or less affected physis. Five patients had only one affected physis. We defined these patients' group who had up to 3 affected physis as "limited enchondromatosis with columnar pattern (LE-CP)." We observed that most of the columnar cartilage was turning into the normal bone via endochondral ossification. Based on our observations, the columnar pattern is a rare manifestation of the enchondromas. Columnar pattern, along with the related physis, acts as a normal endochondral ossification process, and surgery is not necessary unless there is a risk of fracture or severe deformity. Further awareness of this unique subset of patients may improve our understanding of the disease and lead to better patient outcomes. We have modified non-hereditarily enchondromatosis into 2 categories: limited enchondromatosis with the columnar pattern and multiple enchondromatosis. We believe that LE-CM reflects a developmental anomaly of the physis rather than a true neoplasia, and it acts as a normal endochondral ossification process. Level IV (case series).

Cervical spinal cord morphometrics in degenerative cervical myelopathy: quantification using semi-automated normalized technique and correlation with neurological dysfunctions

Degenerative cervical myelopathy (DCM) is characterized by spinal cord atrophy. Accurate estimation of spinal cord atrophy is key to the understanding of neurological diseases, including DCM. However, its clinical application is hampered by difficulties in its precise and consistent estimation due to significant variability in spinal cord morphometry along the cervical spine, both within and between individuals. To characterize morphometrics of the compressed spinal cord in DCM patients. We employed our semiautomated analysis framework that incorporates the Spinal Cord Toolbox (SCT) and a normalization approach to effectively address the challenges posed by cord compression in these patients. Additionally, we examined the clinical relevance of these morphometric measures to enhance our understanding of DCM pathophysiology. Prospective study. This study investigated 36 DCM patients and 31 healthy controls (HCs). Clinical scores including 9-hole peg test for hand dexterity, hand grip strength, balance, gait speed, modified Japanese Orthopaedic Association (mJOA) score, and imaging-based spinal cord morphometrics. Using the generic spine acquisition protocol and our semiautomated analysis pipeline, spinal cord morphometrics, including cross-sectional area (CSA), anterior-posterior (AP) and transverse (RL) diameters, eccentricity, and solidity, were estimated from sagittal T2w magnetic resonance imaging (MRI) images using the Spinal Cord Toolbox (SCT). Normalized metrics were extracted from the C1 to C7 vertebral levels and compared between DCM patients and HC. Morphometric data at regions of maximum spinal cord compression (MSCC) were correlated with the clinical scores. A subset of participants underwent follow-up scans at 6 months to monitor longitudinal changes in spinal cord atrophy. Spinal cord morphometric data were normalized against the healthy population morphometry (PAM50 database) and extracted for all participants. DCM patients showed a notable reduction in CSA, AP, and RL diameter across all vertebral levels compared to HC. MSCC metrics correlated significantly with clinical scores like dexterity, grip strength, and mJOA scores. Longitudinal analysis indicated a decrease in CSA and worsening clinical scores in DCM patients. Our processing pipeline offers a reliable method for assessing spinal cord compression in DCM patients. Normalized spinal cord morphometrics, particularly the CSA could have potential for monitoring DCM disease severity and progression, guiding treatment decisions. Furthermore, to our knowledge our study is the first to apply the generic spinal cord acquisition protocol, ensuring consistent imaging across different MRI scanners and settings. Coupled with our semiautomated analysis pipeline, this protocol is key for the detailed morphometric characterization of compressed spinal cords in patients with DCM, a disease that is both complex and heterogenous. This study was funded by the National Institute of Neurological Disorders and Stroke (NINDS) (K23:NS091430) and (R01: NS129852-01A1).

High baseline perivascular space volume in basal ganglia is associated with attention and executive function decline in Parkinson's disease.

Pathologic perivascular spaces (PVS), the fluid-filled compartments surrounding brain vasculature, may underlie cognitive decline in Parkinson's disease (PD). However, whether this impacts specific cognitive domains has not been investigated. This study examined the relationship of PVS volume at baseline with domain-specific and global cognitive change over 2 years in PD individuals. A total of 39 individuals with PD underwent 3T T1w magnetic resonance imaging to determine PVS volume fraction (PVS volume normalized to total regional volume) within (i) centrum semiovale, (ii) prefrontal white matter (medial orbitofrontal, rostral middle frontal, and superior frontal), and (iii) basal ganglia. A neuropsychological battery included assessment of cognitive domains and global cognitive function at baseline and after 2 years. Higher basal ganglia PVS at baseline was associated with greater decline in attention, executive function, and global cognition scores. While previous reports have associated elevated PVS volume in the basal ganglia with decline in global cognition in PD, our findings show such decline may affect the attention and executive function domains.

Unsupervised MRI motion artifact disentanglement: introducing MAUDGAN

Objective. This study developed an unsupervised motion artifact reduction method for magnetic resonance imaging (MRI) images of patients with brain tumors. The proposed novel design uses multi-parametric multicenter contrast-enhanced T1W (ceT1W) and T2-FLAIR MRI images. Approach. The proposed framework included two generators, two discriminators, and two feature extractor networks. A 3-fold cross-validation was used to train and fine-tune the hyperparameters of the proposed model using 230 brain MRI images with tumors, which were then tested on 148 patients’ in-vivo datasets. An ablation was performed to evaluate the model’s compartments. Our model was compared with Pix2pix and CycleGAN. Six evaluation metrics were reported, including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale-SSIM (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). Artifact reduction and consistency of tumor regions, image contrast, and sharpness were evaluated by three evaluators using Likert scales and compared with ANOVA and Tukey’s HSD tests. Main results. On average, our method outperforms comparative models to remove heavy motion artifacts with the lowest NMSE (18.34±5.07%) and MS-GMSD (0.07 ± 0.03) for heavy motion artifact level. Additionally, our method creates motion-free images with the highest SSIM (0.93 ± 0.04), PSNR (30.63 ± 4.96), and VIF (0.45 ± 0.05) values, along with comparable MS-SSIM (0.96 ± 0.31). Similarly, our method outperformed comparative models in removing in-vivo motion artifacts for different distortion levels except for MS- SSIM and VIF, which have comparable performance with CycleGAN. Moreover, our method had a consistent performance for different artifact levels. For the heavy level of motion artifacts, our method got the highest Likert scores of 2.82 ± 0.52, 1.88 ± 0.71, and 1.02 ± 0.14 (p-values ≪ 0.0001) for our method, CycleGAN, and Pix2pix respectively. Similar trends were also found for other motion artifact levels. Significance. Our proposed unsupervised method was demonstrated to reduce motion artifacts from the ceT1W brain images under a multi-parametric framework.

Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics.

Malignant peripheral nerve sheath tumors (MPNSTs) are aggressive soft-tissue tumors prevalent in neurofibromatosis type 1 (NF1) patients, posing a significant risk of metastasis and recurrence. Current magnetic resonance imaging (MRI) imaging lacks decisiveness in distinguishing benign peripheral nerve sheath tumors (BPNSTs) and MPNSTs, necessitating invasive biopsies. This study aims to develop a radiomics model using quantitative imaging features and machine learning to distinguish MPNSTs from BPNSTs. Clinical data and MRIs from MPNST and BPNST patients (2000-2019) were collected at a tertiary sarcoma referral center. Lesions were manually and semi-automatically segmented on MRI scans, and radiomics features were extracted using the Workflow for Optimal Radiomics Classification (WORC) algorithm, employing automated machine learning. The evaluation was conducted using a 100× random-split cross-validation. A total of 35 MPNSTs and 74 BPNSTs were included. The T1-weighted (T1w) MRI radiomics model outperformed others with an area under the curve (AUC) of 0.71. The incorporation of additional MRI scans did not enhance performance. Combining T1w MRI with clinical features achieved an AUC of 0.74. Experienced radiologists achieved AUCs of 0.75 and 0.66, respectively. Radiomics based on T1w MRI scans and clinical features show some ability to distinguish MPNSTs from BPNSTs, potentially aiding in the management of these tumors.

Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

AbstractThe timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.This article is categorized under: Technologies &gt; Classification Technologies &gt; Machine Learning Application Areas &gt; Health Care

MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models.

Radiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature (RS) predicting response to standard chemotherapy. Mice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX (RS_O), and subsequently applied on the earlier study timepoints (RS_O at baseline, and week 1-3). For external validation, the radiomic model was tested in a separate T2w-MRI dataset on segmented whole-volume subcutaneous tumors (RS_S) from the same O-PDX model, imaged at three timepoints (baseline, day 3 and day 10/endpoint) after start of chemotherapy (n=8 tumors) or saline/control (n=8 tumors). The RS_O yielded rapidly increasing area under the receiver operating characteristic (ROC) curves (AUCs) for predicting treatment response from baseline until endpoint; AUC=0.38 (baseline); 0.80 (week 1), 0.85 (week 2), 0.96 (week 3) and 1.0 (endpoint). In comparison, vMRI yielded AUCs of 0.37 (baseline); 0.69 (w1); 0.83 (week 2); 0.92 (week 3) and 0.97 (endpoint). When tested in the external validation dataset, RS_S yielded high accuracy for predicting treatment response at day10/endpoint (AUC=0.85) and tended to yield higher AUC than vMRI (AUC=0.78, p=0.18). Neither RS_S nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both). We have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.

High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.

7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI. The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition, RESULTS: The results are as follows: PSNR: 27.0±0.9dB, SSIM: 0.945±0.010, and MSE: 2.0E-3±0.4E-3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state-of-the-art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min. We propose a novel method to predict high-resolution 7T ADC maps from low-resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.

The role of diffusion-weighted MRI in the evaluation of imaging for prostate carcinoma: A systematic review

Abstract Background: Prostate cancer is one of the most common cancers in men. In recent times, diffusion-weighted imaging (DWI) sequence magnetic resonance imaging (MRI) techniques have been used in the diagnosis of prostate cancer. The present systematic review aims to analyze the effectiveness of DWI in diagnosing prostate cancer. Methodology and Results: Publicly available English databases, such as PubMed and Google Scholar, were searched until 2020. We reviewed 965 research articles, and 10 were selected based on inclusion and exclusion criteria. The study found that the apparent diffusion coefficient (ADC) value decreases as the total diffusion volume increases. Tumor cells have significantly lower ADC values compared to normal or non-tumor cells. DWI was found to have higher sensitivity and accuracy than the T2W MRI technique. However, Choline-PET/CT was slightly more effective in diagnosing prostate cancer compared to DWI. Conclusion: DWI is a promising MRI technique that can enhance diagnostic performance in conjunction with other conventional MRI techniques to diagnose prostate cancer.

Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation.

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. In this study, we introduce histogram-based polynomial synthesis (HIPS), a fast preprocessing transform step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation for intensity transformation. HIPS was incorporated into THalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, a method developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for the use of T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts (MPRAGE, SPGR, and MP2RAGE), scanner manufacturers (PHILIPS, GE, and Siemens), and field strengths (3T and 7T). HIPS-transformed images improved intra-thalamic contrast and thalamic boundaries, and HIPS-THOMAS yielded significantly higher mean Dice coefficients and reduced volume errors compared to both the CNN method and T1w-THOMAS. Finally, all three methods were compared using the frequently travelling human phantom MRI dataset for inter- and intra-scanner variability, with HIPS displaying the least inter-scanner variability and performing comparably with T1w-THOMAS for intra-scanner variability. In conclusion, our findings highlight the efficacy and robustness of HIPS in enhancing thalamic nuclei segmentation from standard T1w MRI.

Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement in MRI Imaging.

Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667 ± 0.006 for Li's model; 0.0761 ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging.

Radiomics for residual tumour detection and prognosis in newly diagnosed glioblastoma based on postoperative [11C] methionine PET and T1c-w MRI

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.