T1w MRI Research Articles

NIMG-79. A RADIOMIC-RISK SCORE FOR SURVIVAL PREDICTION IN GLIOMA: A LARGE MULTI-INSTITUTIONAL RETROSPECTIVE VALIDATION STUDY

Abstract Gliomas have dismal survival. Recently, radiomics has demonstrated success in developing non-invasive image-based biomarkers for survival prediction in gliomas. However, clinical applicability of radiomics-based biomarkers will ultimately require rigorous validation on large, multi-institutional data. This work attempts to evaluate a radiomic-based survival risk-assessment prognostic score on a multi-institutional cohort of Glioma patients. Our rationale is that optimizing and validating radiomic features that capture intensity-based textural variations and morphology-based changes (e.g., local curvature and global contour) on large multi-institutional cohorts can allow for robust risk assessment in Glioma. n=668 pre-operative T1w MRI scans for Glioma patients from 4 cohorts: University of California San Francisco “UCSF” (n=495), LUMIERE (n=86)), xCures (n=13), and Cleveland Clinic (CCF) (n=74) were analyzed. Subjects from UCSF, CCF, and xCures were used for training, whereas the LUMIERE cohort was used for hold-out testing. Following pre-processing, expert-vetted tumor segmentation into Edema (ED) and Enhancing tumor (ET) regions was performed. A total of n=1558 radiomic features were extracted from the tumor regions (779 per region), namely, 31 global contour, 20 curvature-based, 624 gradient co-occurrence statistics, and 104 Collage features. Cox regression model was employed to create a radiomic risk score (RRS) for every subject, which included radiomics features as well as on clinical and demographic variables (IDH, MGMT, sex, age), for survival analysis. RRS incorporated a total of 43 radiomic features, and the associated clinical and demographic variables (CDS), yielding significant differences between high and low risk groups in the training (RRS: p=0.043, HR = 2, CSD: p=0.0031, HR = 2.8) and testing sets (RRS: p=0.0096, HR = 1.8, CSD: p=0.0071, HR = 1.8). Combining RRS and CDS improved the OS prediction at group level (p=3.6e-6, 0.0011, HR=5.4, 2, for training and testing, respectively). Radiomics-based risk-stratification may be promising for reliable risk-stratification in glioma.

Dynamic glucose-enhanced MRI of gliomas: A preliminary clinical application.

The study aimed to investigate the feasibility of dynamic glucose-enhanced (DGE) MRI technology in the clinical application of glioma. Twenty patients with glioma were examined using a preoperative DGE-MRI protocol before clinical intervention. A brief hyperglycemic state was achieved by injecting 50 mL of 50% w/w D-glucose intravenously during the DGE imaging. The total acquisition time for the DGE was 15 min. Area-under-the-curve (AUC) images were calculated using the DGE images. AUC2-7min values of the glioma core, margin area, edema area, and contralateral brain parenchyma were compared using Mann-Whitney U tests. Overall, gray and white matter areas in the AUC images showed relatively low DGE signal change and bilateral symmetry. However, the tumor cores displayed a significant hyperintensity. A high DGE signal change was also seen in the necrotic, cystic, and cerebrospinal areas. These results show that DGE MRI is a feasible technique for the study of brain tumors as part of a clinical exam. Importantly, DGE MRI showed enhancement in areas confirmed histopathologically as tumors, whereas Gd T1w MRI did not show any enhancement in this area. Since the D-glucose molecule is smaller than Gd-based contrast agents, DGE MRI may be more sensitive to subtle blood-brain barrier disruptions, thus potentially providing early information about possible malignancy. These findings provide a new perspective for the further exploration and analysis of D-glucose uptake in brain tumors.

Morphological similarity and white matter structural mapping of new daily persistent headache: a structural connectivity and tract-specific study

BackgroundNew daily persistent headache (NDPH) is a rare primary headache disorder characterized by daily and persistent sudden onset headaches. Specific abnormalities in gray matter and white matter structure are associated with pain, but have not been well studied in NDPH. The objective of this work is to explore the fiber tracts and structural connectivity, which can help reveal unique gray and white matter structural abnormalities in NDPH.MethodsThe regional radiomics similarity networks were calculated from T1 weighted (T1w) MRI to depict the gray matter structure. The fiber connectivity matrices weighted by diffusion metrics like fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD) were built, meanwhile the fiber tracts were segmented by anatomically-guided superficial fiber segmentation (Anat-SFSeg) method to explore the white matter structure from diffusion MRI. The considerable different neuroimaging features between NDPH and healthy controls (HC) were extracted from the connectivity and tract-based analyses. Finally, decision tree regression was used to predict the clinical scores (i.e. pain intensity) from the above neuroimaging features.ResultsT1w and diffusion MRI data were available in 51 participants after quality control: 22 patients with NDPH and 29 HCs. Significantly decreased morphological similarity was found between the right superior frontal gyrus and right hippocampus. The superficial white matter (SWM) showed significantly decreased FA in fiber tracts including the right superficial-frontal, left superficial-occipital, bilateral superficial-occipital-temporal (Sup-OT) and right superficial-temporal, meanwhile significant increased RD was found in the left Sup-OT. For the fiber connectivity, NDPH showed significantly decreased FA in the bilateral basal ganglion and temporal lobe, increased MD in the right frontal lobe, and increased RD in the right frontal lobe and left temporal-occipital lobe. Clinical scores could be predicted dominantly by the above significantly different neuroimaging features through decision tree regression.ConclusionsOur research indicates the structural abnormalities of SWM and the neural pathways projected between regions like right hippocampus and left caudate nucleus, along with morphological similarity changes between the right superior frontal gyrus and right hippocampus, constitute the pathological features of NDPH. The decision tree regression demonstrates correlations between these structural changes and clinical scores.

Super-resolution reconstruction of time-resolved four-dimensional computed tomography (TR-4DCT) with multiple breathing cycles based on TR-4DMRI.

Respiratory motion irregularities in lung cancer patients are common and can be severe during multi-fractional (∼20 mins/fraction) radiotherapy. However, the current clinical standard of motion management is to use a single-breath respiratory-correlated four-dimension computed tomography (RC-4DCT or 4DCT) to estimate tumor motion to delineate the internal tumor volume (ITV), covering the trajectory of tumor motion, as a treatment target. To develop a novel multi-breath time-resolved (TR) 4DCT using the super-resolution reconstruction framework with TR 4D magnetic resonance imaging (TR-4DMRI) as guidance for patient-specific breathing irregularity assessment, overcoming the shortcomings of RC-4DCT, including binning artifacts and single-breath limitations. Six lung cancer patients participated in the IRB-approved protocol study to receive multiple T1w MRI scans, besides an RC-4DCT scan on the simulation day, including 80 low-resolution (lowR: 5×5×5mm3) free-breathing (FB) 3D cine MRFB images in 40s (2Hz) and a high-resolution (highR: 2×2×2mm3) 3D breath-hold (BH) MRBH image for each patient. A CT (1×1×3mm3) image was selected from 10-bin RC-4DCT with minimal binning artifacts and a close diaphragm match (<1cm) to the MRBH image. A mutual-information-based Freeform deformable image registration (DIR) was used to register the CT and MRBH via the opposite directions (namely F1: and F2: ) to establish CT-MR voxel correspondences. An intensity-based enhanced Demons DIR was then applied for , in which the original MRBH was used in D1: , while the deformed MRBH was used in D2: . The deformation vector fields (DVFs) obtained from each DIR were composed to apply to the deformed CT (D1) and original CT (D2) to reconstruct TR-4DCT images. A digital 4D-XCAT phantom at the end of inhalation (EOI) and end of exhalation (EOE) with 2.5cm diaphragmatic motion and three spherical targets (ϕ=2, 3, 4cm) were first tested to reconstruct TR-4DCT. For each of the six patients, TR-4DCT images at the EOI, middle (MID), and EOE were reconstructed with both D1 and D2 approaches. TR-4DCT image quality was evaluated with mean distance-to-agreement (MDA) at the diaphragm compared with MRFB, tumor volume ratio (TVR) referenced to MRBH, and tumor shape difference (DICE index) compared with the selected input CT. Additionally, differences in the tumor center of mass (|∆COMD1-D2|), together with TVR and DICE comparison, was assessed in the D1 and D2 reconstructed TR-4DCT images. In the phantom, TR-4DCT quality is assessed by MDA=2.0±0.8mm at the diaphragm, TVR=0.8±0.0 for all tumors, and DICE=0.83±0.01, 0.85±0.02, 0.88±0.01 for ϕ=2, 3, 4cm tumors, respectively. In six patients, the MDA in diaphragm match is -1.6±3.1mm (D1) and 1.0±3.9mm (D2) between the reconstructed TR-4DCT and lowR MRFB among 18 images (3 phases/patient). The tumor similarity is TVR=1.2±0.2 and DICE=0.70±0.07 for D1 and TVR=1.4±0.3 (D2) and DICE=0.73±0.07 for D2. The tumor position difference is |∆COMD1-D2|=1.2±0.8mm between D1 and D2 reconstructions. The feasibility of super-resolution reconstruction of multi-breathing-cycle TR-4DCT is demonstrated and image quality at the diaphragm and tumor is assessed in both the 4D-XCAT phantom and six lung cancer patients. The similarity of D1 and D2 reconstruction suggests consistent and reliable DIR results. Clinically, TR-4DCT has the potential for breathing irregularity assessment and dosimetry evaluation in radiotherapy.

Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information

The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy.&#xD;Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners.&#xD;Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion.&#xD;Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model.&#xD;Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images.

AI-ADC: Channel and Spatial Attention-Based Contrastive Learning to Generate ADC Maps from T2W MRI for Prostate Cancer Detection.

Apparent Diffusion Coefficient (ADC) maps in prostate MRI can reveal tumor characteristics, but their accuracy can be compromised by artifacts related with patient motion or rectal gas associated distortions. To address these challenges, we propose a novel approach that utilizes a Generative Adversarial Network to synthesize ADC maps from T2-weighted magnetic resonance images (T2W MRI). By leveraging contrastive learning, our model accurately maps axial T2W MRI to ADC maps within the cropped region of the prostate organ boundary, capturing subtle variations and intricate structural details by learning similar and dissimilar pairs from two imaging modalities. We trained our model on a comprehensive dataset of unpaired T2-weighted images and ADC maps from 506 patients. In evaluating our model, named AI-ADC, we compared it against three state-of-the-art methods: CycleGAN, CUT, and StyTr2. Our model demonstrated a higher mean Structural Similarity Index (SSIM) of 0.863 on a test dataset of 3240 2D MRI slices from 195 patients, compared to values of 0.855, 0.797, and 0.824 for CycleGAN, CUT, and StyTr2, respectively. Similarly, our model achieved a significantly lower Fréchet Inception Distance (FID) value of 31.992, compared to values of 43.458, 179.983, and 58.784 for the other three models, indicating its superior performance in generating ADC maps. Furthermore, we evaluated our model on 147 patients from the publicly available ProstateX dataset, where it demonstrated a higher SSIM of 0.647 and a lower FID of 113.876 compared to the other three models. These results highlight the efficacy of our proposed model in generating ADC maps from T2W MRI, showcasing its potential for enhancing clinical diagnostics and radiological workflows.

Clinical relevance of paramagnetic rim lesion heterogeneity in multiple sclerosis.

Previous studies reveal heterogeneity in terms of paramagnetic rim lesions (PRL) associated tissue damage. We investigated the physiopathology and clinical implications of this heterogeneity. In 103 MS patients (72 relapsing and 31 progressive), brain lesions were manually segmented on 3T 3D-FLAIR and rim visibility was assessed with a visual confidence level score (VCLS) on 3D-EPI phase. Using T1 relaxation time maps, lesions were categorized in long-T1 and short-T1. Lesion age was calculated from time of first gadolinium enhancement (N = 84 lesions). Results on clinical scores were validated in an extended cohort of 167 patients using normalized T1w-MPRAGE lesion values. Rim visibility (VCLS analysis) was associated with increasing lesional T1 (P/PFDR < 0.001). Of 1680 analyzed lesions, 427 were categorized as PRL. Long-T1 PRL were older than short-T1 PRL (average 0.8 vs. 2.0 years, P/PFDR = 0.005/0.008), and featured larger lesional volume (P/PFDR < 0.0001) and multi-shell diffusion-measured axonal damage (P/PFDR < 0.0001). The total volume of long-T1-PRL versus PRL showed 2× predictive power for both higher MS disability (EDSS; P/PFDR = 0.003/0.005 vs. P/PFDR = 0.042/0.057) and severity (MSSS; P/PFDR = 0.0006/0.001 vs. P/PFDR = 0.004/0.007). In random forest, having ≥1 long-T1-PRL versus ≥4 PRL showed 2-4× higher performance to predict a higher EDSS and MSSS. In the validation cohort, long-T1 PRL outperformed (~2×) PRL in predicting both EDSS and MSSS. PRL show substantial heterogeneity in terms of intralesional tissue damage. More destructive, likely older, long-T1 PRL improve the association with MS clinical scales. This PRL heterogeneity characterization was replicated using standard T1w MRI, highlighting its potential for clinical translation.

BrainQCNet: A Deep Learning attention-based model for the automated detection of artifacts in brain structural MRI scans

Abstract Analyses of structural MRI (sMRI) data depend on robust upstream data quality control (QC). It is also crucial that researchers seek to retain maximal amounts of data to ensure reproducible, generalizable models and to avoid wasted effort, including that of participants. The time-consuming and difficult task of manual QC evaluation has prompted the development of tools for the automatic assessment of brain sMRI scans. Existing tools have proved particularly valuable in this age of Big Data; as datasets continue to grow, reducing execution time for QC evaluation will be of considerable benefit. The development of Deep Learning (DL) models for artifact detection in structural MRI scans offers a promising avenue toward fast, accurate QC evaluation. In this study, we trained an interpretable Deep Learning model, ProtoPNet, to classify minimally preprocessed 2D slices of scans that had been manually annotated with a refined quality assessment (ABIDE 1; n = 980 scans). To evaluate the best model, we applied it to 2141 ABCD T1-weighted MRI scans for which gold-standard manual QC annotations were available. We obtained excellent accuracy: 82.4% for good quality scans (Pass), 91.4% for medium to low quality scans (Fail). Further validation using 799 T1w MRI scans from ABIDE 2 and 750 T1w MRI scans from ADHD-200 confirmed the reliability of our model. Accuracy was comparable to or exceeded that of existing ML models, with fast processing and prediction time (1 minute per scan, GPU machine, CUDA-compatible). Our attention model also performs better than traditional DL (i.e., convolutional neural network models) in detecting poor quality scans. To facilitate faster and more accurate QC prediction for the neuroimaging community, we have shared the model that returned the most reliable global quality scores as a BIDS-app (https://github.com/garciaml/BrainQCNet).

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Radiogenomics based survival prediction of small-sample glioblastoma patients by multi-task DFFSP model.

In this paper, the multi-task dense-feature-fusion survival prediction (DFFSP) model is proposed to predict the three-year survival for glioblastoma (GBM) patients based on radiogenomics data. The contrast-enhanced T1-weighted (T1w) image, T2-weighted (T2w) image and copy number variation (CNV) is used as the input of the three branches of the DFFSP model. This model uses two image extraction modules consisting of residual blocks and one dense feature fusion module to make multi-scale fusion of T1w and T2w image features as backbone. Also, a gene feature extraction module is used to adaptively weight CNV fragments. Besides, a transfer learning module is introduced to solve the small sample problem and an image reconstruction module is adopted to make the model anatomy-aware under a multi-task framework. 256 sample pairs (T1w and corresponding T2w MRI slices) and 187 CNVs of 74 patients were used. The experimental results show that the proposed model can predict the three-year survival of GBM patients with the accuracy of 89.1 %, which is improved by 3.2 and 4.7 % compared with the model without genes and the model using last fusion strategy, respectively. This model could also classify the patients into high-risk and low-risk groups, which will effectively assist doctors in diagnosing GBM patients.

Predicting liver ablation volumes with real-time MRI thermometry

Background & AimsMRI guidance offers better lesion targeting for microwave ablation of liver lesions with higher soft-tissue contrast, as well as the possibility of real-time thermometry. This study aims to evaluate the correlation of real-time MR thermometry-predicted lesion volume with the ablation zone in postprocedural first-day images. Materials and MethodsThis single center retrospective analysis evaluated prospectively collected patients who underwent MRI-guided microwave ablation with real-time thermometry between December 2020 and July 2023. All procedures were performed in general anesthesia on a 1.5T MRI scanner. Real-time thermometry data were acquired using multi-slice gradient-echo EPI sequences, and thermal dose (CEM43 of 240 minutes as a threshold) maps were created. The volume of tissue exposed to lethal thermal dose in MR thermometry (thermal dose) was compared with the ablation zone volume in portal phase T1w MRI on postprocedural first-day using Pearson correlation test, and visual quantitative assessment by radiologists was performed to evaluate the similarity of shapes and volumes. ResultsOut of 30 patients with 33 lesions with thermometry images, six (18.1%) lesions were excluded due to artifacts limiting interpretation of thermal dose volume. Twenty-four patients with 27 lesions (20 male, age 63.1±9.1) were evaluated for the volume correlation. The volume of thermal dose-predicted lesions and the postprocedural first-day ablation zones showed a strong correlation (R=0.89, p<0.001). Similarly, visual similarity of MR thermometry-predicted shape and the ablation zone shape was graded as perfect in 23 (85.1%) lesions. ConclusionsReal-time thermal dose-predicted volumes show very good correlation with the ablation zone volumes in images obtained one day after the procedure, which could reduce the local recurrence rates with the possibility of reablating lesions within the same procedure.

Systemic inflammatory markers and volume of enhancing tissue on post-contrast T1w MRI images in differentiating true tumor progression from pseudoprogression in high-grade glioma

BackgroundHigh-grade glioma (HGG) patients post-radiotherapy often face challenges distinguishing true tumor progression (TTP) from pseudoprogression (PsP). This study evaluates the effectiveness of systemic inflammatory markers and volume of enhancing tissue on post-contrast T1 weighted (T1WCE) MRI images for this differentiation within the first six months after treatment. Material and MethodsWe conducted a retrospective analysis on a cohort of HGG patients from 2015 to 2021, categorized per WHO 2016 and 2021 criteria. We analyzed treatment responses using modified RANO criteria and conducted volumetry on T1WCE and T2W/FLAIR images.Blood parameters assessed included neutrophil/lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI). We employed Chi-square, Fisher’s exact test, and Mann-Whitney U test for statistical analyses, using log-transformed predictors due to multicollinearity. A Cox regression analysis assessed the impact of PsP- and TTP-related factors on overall survival (OS). ResultsThe cohort consisted of 39 patients, where 16 exhibited PsP and 23 showed TTP. Univariate analysis revealed significantly higher NLR and SII in the TTP group [NLR: 4.1 vs 7.3, p = 0.002; SII 546.5 vs 890.5p = 0.009]. T1WCE volume distinctly differentiated PsP from TTP [2.2 vs 11.7, p < 0.001]. In multivariate regression, significant predictors included NLR and T1WCE volume in the “NLR Model,” and T1WCE volume and SII in the “SII Model.” The study also found a significantly lower OS rate in TTP patients compared to those with PsP [HR 3.97, CI 1.59 to 9.93, p = 0.003]. ConclusionElevated both, SII and NLR, and increased T1WCE volume were effective in differentiating TTP from PsP in HGG patients post-radiotherapy. These results suggest the potential utility of incorporating these markers into clinical practice, though further research is necessary to confirm these findings in larger patient cohorts.

MRI T2w Radiomics-Based Machine Learning Models in Imaging Simulated Biopsy Add Diagnostic Value to PI-RADS in Predicting Prostate Cancer: A Retrospective Diagnostic Study.

Currently, prostate cancer (PCa) prebiopsy medical image diagnosis mainly relies on mpMRI and PI-RADS scores. However, PI-RADS has its limitations, such as inter- and intra-radiologist variability and the potential for imperceptible features. The primary objective of this study is to evaluate the effectiveness of a machine learning model based on radiomics analysis of MRI T2-weighted (T2w) images for predicting PCa in prebiopsy cases. A retrospective analysis was conducted using 820 lesions (363 cases, 457 controls) from The Cancer Imaging Archive (TCIA) Database for model development and validation. An additional 83 lesions (30 cases, 53 controls) from Hong Kong Queen Mary Hospital were used for independent external validation. The MRI T2w images were preprocessed, and radiomic features were extracted. Feature selection was performed using Cross Validation Least Angle Regression (CV-LARS). Using three different machine learning algorithms, a total of 18 prediction models and 3 shape control models were developed. The performance of the models, including the area under the curve (AUC) and diagnostic values such as sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were compared to the PI-RADS scoring system for both internal and external validation. All the models showed significant differences compared to the shape control model (all p < 0.001, except SVM model PI-RADS+2 Features p = 0.004, SVM model PI-RADS+3 Features p = 0.002). In internal validation, the best model, based on the LR algorithm, incorporated 3 radiomic features (AUC = 0.838, sensitivity = 76.85%, specificity = 77.36%). In external validation, the LR (3 features) model outperformed PI-RADS in predictive value with AUC 0.870 vs. 0.658, sensitivity 56.67% vs. 46.67%, specificity 92.45% vs. 84.91%, PPV 80.95% vs. 63.64%, and NPV 79.03% vs. 73.77%. The machine learning model based on radiomics analysis of MRI T2w images, along with simulated biopsy, provides additional diagnostic value to the PI-RADS scoring system in predicting PCa.

Tractography from T1-weighted MRI: empirically exploring the clinical viability of streamline propagation without diffusion MRI

Abstract Over the last few decades, diffusion MRI (dMRI) streamline tractography has emerged as the dominant method for in vivo estimation of white matter (WM) pathways in the brain. One key limitation to this technique is that modern tractography implementations require high angular resolution diffusion imaging (HARDI). However, HARDI can be difficult to collect clinically, limiting the reach of tractography analyses to research cohorts and thus limiting many WM investigations to certain populations and pathologies. As such, a clinically viable tractography solution applicable to wider patient populations scanned as a part of routine care would be of key significance in broadening WM analyses to underfunded or rarer diseases and to the clinical setting. Such a solution would require the ability to perform arbitrary tractography analyses, use only clinical imaging for input, and be open source and widely accessible and implementable. Thus, here we evaluate our recently developed, containerized and open-source, T1-weighted (T1w) MRI-based deep learning model for streamline propagation. We empirically assess its performance against traditional dMRI-based and established atlas-based approaches in a healthy young population, an aging one, and in those with epilepsy, depression, and brain cancer. In the healthy young population, we find slightly increased error compared to traditional tractography with the deep learning model that falls within the bounds attributable to dMRI variability and is considerably less than the atlas-based approach. Further, seeking to replicate previously published dMRI tractography effects in the remaining cohorts as an initial assessment of clinical viability, we find this model successfully does so in some key cases—particularly in applications that rely on long-range streamlines including those not captured by the atlas-based approach—but importantly not all. These results suggest a deep learning-based approach to tractography with T1w MRI demonstrates promise within the limitations of our definition of clinical viability and especially over atlas-based approaches but requires refinement and more robust consideration of out-of-distribution effects prior to widespread clinical use. We also find these results raise additional questions regarding the differences in image content between dMRI and T1w MRI and their relationship to tractography. Further investigation of these questions will improve the field’s understanding of which features of the brain influence measured tractography effects.

Evaluation of Thyroid Nodule by US Including Elastography versus MRI T2W and Diffusion Sequences

Abstract Background The thyroid gland frequently develops thyroid nodules. Effective treatment of malignant thyroid nodules depends on an early and accurate diagnosis. Benign and malignant thyroid nodules are typically distinguished using conventional thyroid ultrasonography (US). In order to explore changes in tissue stiffness, strain elastography (SE) uses an external force and a deformation response detector. A promising method for distinguishing between benign and malignant thyroid nodules is ultrasound elastography. Malignant versus benign thyroid nodules might be distinguished using MRI apparent diffusion coefficient (ADC) values. This study compares MRI T2W and diffusion sequences with ultrasound, which includes elastography, to assess thyroid nodules. Methods This cross-sectional research was carried out at Ain-Shams University's radiodiagnosis department. It included 23 female or male patients with suspicious thyroid nodule by clinical examination, or ultrasonography including some cases elastography if present for further work up with MR diffusion subjected to DWI and ADC value and results were compared with histopathology if present or follow up of the patients. The duration of the study was 24 months. Results Diffusion had an accuracy of 91.3%, while the ADC value and elastography had a greater diagnostic accuracy with an area under the curve of 95.7% in discriminating between benign and malignant cases. Conclusions When describing thyroid nodules, ADC value provided better diagnostic performance than US TIRADS rating. Over and above ACR-TIRADS classification, useful information is added by ultrasound elastography and strain ratio analysis of thyroid nodules. It is advised as an auxiliary tool. However, the distinction of cancerous and benign thyroid nodules might potentially be done with greater precision using diffusion weighted MRI.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Evaluating Virtual Contrast-Enhanced Magnetic Resonance Imaging in Nasopharyngeal Carcinoma Radiation Therapy: A Retrospective Analysis for Primary Gross Tumor Delineation

PurposeTo investigate the potential of virtual contrast-enhanced MRI (VCE-MRI) for gross-tumor-volume (GTV) delineation of nasopharyngeal carcinoma (NPC) using multi-institutional data. Methods and MaterialsThis study retrospectively retrieved T1-weighted (T1w), T2-weighted (T2w) MRI, gadolinium-based contrast-enhanced MRI (CE-MRI) and planning CT of 348 biopsy-proven NPC patients from three oncology centers. A multimodality-guided synergistic neural network (MMgSN-Net) was trained using 288 patients to leverage complementary features in T1w and T2w MRI for VCE-MRI synthesis, which was independently evaluated using 60 patients. Three board-certified radiation oncologists and two medical physicists participated in clinical evaluations in three aspects: image quality assessment of the synthetic VCE-MRI, VCE-MRI in assisting target volume delineation, and effectiveness of VCE-MRI-based contours in treatment planning. The image quality assessment includes distinguishability between VCE-MRI and CE-MRI, clarity of tumor-to-normal tissue interface and veracity of contrast enhancement in tumor invasion risk areas. Primary tumor delineation and treatment planning were manually performed by radiation oncologists and medical physicists, respectively. ResultsThe mean accuracy to distinguish VCE-MRI from CE-MRI was 31.67%; no significant difference was observed in the clarity of tumor-to-normal tissue interface between VCE-MRI and CE-MRI; for the veracity of contrast enhancement in tumor invasion risk areas, an accuracy of 85.8% was obtained. The image quality assessment results suggest that the image quality of VCE-MRI is highly similar to real CE-MRI. The mean dosimetric difference of planning target volumes were less than 1Gy. ConclusionsThe VCE-MRI is highly promising to replace the use of gadolinium-based CE-MRI in tumor delineation of NPC patients.

Insights into traditional Large Deformation Diffeomorphic Metric Mapping and unsupervised deep-learning for diffeomorphic registration and their evaluation

This paper explores the connections between traditional Large Deformation Diffeomorphic Metric Mapping methods and unsupervised deep-learning approaches for non-rigid registration, particularly emphasizing diffeomorphic registration. The study provides useful insights and establishes connections between the methods, thereby facilitating a profound understanding of the methodological landscape. The methods considered in our study are extensively evaluated in T1w MRI images using traditional NIREP and Learn2Reg OASIS evaluation protocols with a focus on fairness, to establish equitable benchmarks and facilitate informed comparisons. Through a comprehensive analysis of the results, we address key questions, including the intricate relationship between accuracy and transformation quality in performance, the disentanglement of the influence of registration ingredients on performance, and the determination of benchmark methods and baselines. We offer valuable insights into the strengths and limitations of both traditional and deep-learning methods, shedding light on their comparative performance and guiding future advancements in the field.

IMG-08. SYNTHESIZING MISSING MRI SEQUENCES IN PEDIATRIC BRAIN TUMORS USING GENERATIVE ADVERSARIAL NETWORKS; TOWARDS IMPROVED VOLUMETRIC TUMOR ASSESSMENT

Abstract BACKGROUND Standard MRI sequences, such as pre- and post-contrast T1w, T2w, and FLAIR images, are essential for optimizing segmentation of tumor subregions and evaluating treatment responses in pediatric brain tumors (PBTs). However, MRI sets are often incomplete due to imaging artifacts or inconsistent acquisition protocols across various centers. Generative Adversarial Networks (GANs) have been effectively utilized to generate missing MRI sequences for adult brain tumors. This study applies image-to-image translation models using GANs to synthesize missing FLAIR images from T2w images in PBTs. METHODS This retrospective study developed two GAN models, pix2pix in both 2D and 3D, trained and validated on T2w and FLAIR image pairs from 79 and 19 patients, respectively, with pediatric-type diffuse high-grade gliomas (diffuse midline glioma (DMG) including diffuse intrinsic pontine glioma (DIPG)), collected from the Children’s Brain Tumor Network (CBTN). The 2D pix2pix model processes each T2w MRI volume slice-by-slice, generating corresponding FLAIR slices to reconstruct the complete image volume. The 3D pix2pix model allows translation of 3D FLAIR volumes directly from T2w volumes without the need for individual slice processing and reconstruction. The quality of the generated FLAIR volumes was evaluated using Structural Similarity Index (SSI; best closer to 1) and Mean Squared Error (MSE; best near 0) on the validation set. RESULTS The 2D and 3D models achieved robust performance with median SSI, MSE of 0.88, 0.004, and 0.92, 0.002, respectively. CONCLUSIONS The GAN models developed in this study effectively generate missing FLAIR MRI volumes from corresponding T2w images in PBTs. The 3D model, outperforming 2D, speeds the overall processing pipeline by eliminating volume slicing and reconstruction. Future work includes assessing the impact of these synthesized images on the accuracy of our pretrained autosegmentation models in differentiating tumor subregions and measuring tumor volumes in longitudinal MRIs.

Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson’s disease

BackgroundThe efficacy of levodopa, the most crucial metric for Parkinson’s disease diagnosis and treatment, is traditionally gauged through the levodopa challenge test, which lacks a predictive model. This study aims to probe the predictive power of T1-weighted MRI, the most accessible modality for levodopa response.MethodsThis retrospective study used two datasets: from the Parkinson’s Progression Markers Initiative (219 records) and the external clinical dataset from Ruijin Hospital (217 records). A novel feature extraction method using MedicalNet, a pre-trained deep learning network, along with three previous approaches was applied. Three machine learning models were trained and tested on the PPMI dataset and included clinical features, imaging features, and their union set, using the area under the curve (AUC) as the metric. The most significant brain regions were visualized. The external clinical dataset was further evaluated using trained models. A paired one-tailed t-test was performed between the two sets; statistical significance was set at p < 0.001.ResultsFor 46 test set records (mean age, 62 ± 9 years, 28 men), MedicalNet-extracted features demonstrated a consistent improvement in all three machine learning models (SVM 0.83 ± 0.01 versus 0.73 ± 0.01, XgBoost 0.80 ± 0.04 versus 0.74 ± 0.02, MLP 0.80 ± 0.03 versus 0.70 ± 0.07, p < 0.001). Both feature sets were validated on the clinical dataset using SVM, where MedicalNet features alone achieved an AUC of 0.64 ± 0.03. Key responsible brain regions were visualized.ConclusionThe T1-weighed MRI features were more robust and generalizable than the clinical features in prediction; their combination provided the best results. T1-weighed MRI provided insights on specific regions responsible for levodopa response prediction.Critical relevance statementThis study demonstrated that T1w MRI features extracted by a deep learning model have the potential to predict the levodopa response of PD patients and are more robust than widely used clinical information, which might help in determining treatment strategy.Key PointsThis study investigated the predictive value of T1w features for levodopa response.MedicalNet extractor outperformed all other previously published methods with key region visualization.T1w features are more effective than clinical information in levodopa response prediction.Graphical