'Brain age' is a biological clock typically used to describe brain health with one number, but its relationship with established gradients of cortical organization remains unclear. We address this gap by leveraging a data-driven, region-specific brain age approach in 335 neurologically intact adults, using a convolutional neural network (volBrain) to estimate regional brain ages directly from structural MRI without a predefined set of morphometric properties. Six distinct gradients of brain aging are replicated in two independent cohorts. Spatial patterns of accelerated brain aging in older adults quantitatively align with the archetypal sensorimotor-to-association axis of cortical organization. Other brain aging gradients reflect neurobiological hierarchies such as gene expression and externopyramidization. Participant-level correspondences to brain age gradients are associated with cognitive and sensorimotor performance and explained behavioral variance more effectively than global brain age. These results suggest that regional brain age patterns reflect fundamental principles of cortical organization and behavior.

Magnetic resonance (MR) tagging is an imaging technique for noninvasively tracking tissue motion in vivo by creating a visible pattern of magnetization saturation (tags) that deforms with the tissue. Due to longitudinal relaxation and progression to steady-state, the tags and tissue brightnesses change over time, which makes tracking with optical flow methods error-prone. Although Fourier methods can alleviate these problems, they are also sensitive to brightness changes as well as spectral spreading due to motion. To address these problems, we introduce the brightness-invariant tracking estimation (BRITE) technique for tagged MRI. BRITE disentangles the anatomy from the tag pattern in the observed tagged image sequence and simultaneously estimates the Lagrangian motion. The inherent ill-posedness of this problem is addressed by leveraging the expressive power of denoising diffusion probabilistic models to represent the probabilistic distribution of the underlying anatomy and the flexibility of physics-informed neural networks to estimate biologically-plausible motion. A set of tagged MR images of a gel phantom was acquired with various tag periods and imaging flip angles to demonstrate the impact of brightness variations and to validate our method. The results show that BRITE achieves more accurate motion and strain estimates as compared to other state of the art methods, while also being resistant to tag fading.

This study is aimed to clarify the effectiveness of the image-rotation technique and zooming augmentation to improve the accuracy of the deep learning (DL)-based dose conversion from pencil beam (PB) to Monte Carlo (MC) in proton beam therapy (PBT). We adapted 85 patients with head and neck cancers. The patient dataset was randomly divided into 101 plans (334 beams) for training/validation and 11 plans (34 beams) for testing. Further, we trained a DL model that inputs a computed tomography (CT) image and the PB dose in a single-proton field and outputs the MC dose, applying the image-rotation technique and zooming augmentation. We evaluated the DL-based dose conversion accuracy in a single-proton field. The average γ-passing rates (a criterion of 3%/3 mm) were 80.6 ± 6.6% for the PB dose, 87.6 ± 6.0% for the baseline model, 92.1 ± 4.7% for the image-rotation model, and 93.0 ± 5.2% for the data-augmentation model, respectively. Moreover, the average range differences for R90 were - 1.5 ± 3.6% in the PB dose, 0.2 ± 2.3% in the baseline model, -0.5 ± 1.2% in the image-rotation model, and - 0.5 ± 1.1% in the data-augmentation model, respectively. The doses as well as ranges were improved by the image-rotation technique and zooming augmentation. The image-rotation technique and zooming augmentation greatly improved the DL-based dose conversion accuracy from the PB to the MC. These techniques can be powerful tools for improving the DL-based dose calculation accuracy in PBT.

This paper presents a novel method for monocular patient-to-image intraoperative registration, specifically designed to operate without any external hardware tracking equipment or fiducial point markers. Leveraging a synthetic microscopy surgical scene dataset with a wide range of transformations, our approach directly maps preoperative CT scans to 2D intraoperative surgical frames through a lightweight neural network for real-time cochlear implant surgery guidance via a zero-shot learning approach. Unlike traditional methods, our framework seamlessly integrates with monocular surgical microscopes, making it highly practical for clinical use without additional hardware dependencies and requirements. Our method estimates camera poses, which include a rotation matrix and a translation vector, by learning from the synthetic dataset, enabling accurate and efficient intraoperative registration. The proposed framework was evaluated on nine clinical cases using a patient-specific and cross-patient validation strategy. Our results suggest that our approach achieves clinically relevant accuracy in predicting 6D camera poses for registering 3D preoperative CT scans to 2D surgical scenes with an angular error within 10 degrees in most cases, while also addressing limitations of traditional methods, such as reliance on external tracking systems or fiducial markers.

<i>Objective.</i>This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components necessary for optimal performance.<i>Approach.</i>We systematically analyze the impact of input and dose grid resolution, input type, loss function, model architecture, and noise on model performance. Two datasets are used: a public dataset (OpenKBP) and an in-house clinical dataset. Model performance is primarily evaluated using two metrics: dose score and dose-volume histogram (DVH) score.<i>Main results.</i>High-resolution inputs improve prediction accuracy (dose score and DVH score) by 8.6%-13.5% compared to low resolution. Using a combination of CT, planning target volumes, and organs-at-risk as input significantly enhances accuracy, with improvements of 57.4%-86.8% over using CT alone. Integrating mean absolute error (MAE) loss with value-based and criteria-based DVH loss functions further boosts DVH score by 7.2%-7.5% compared to MAE loss alone. In the robustness analysis, most models show minimal degradation under Poisson noise (0-0.3 Gy) but are more susceptible to adversarial noise (0.2-7.8 Gy). Notably, certain models, such as SwinUNETR, demonstrate superior robustness against adversarial perturbations.<i>Significance.</i>These findings highlight the importance of optimizing deep learning models and provide valuable guidance for achieving more accurate and reliable radiotherapy dose prediction.

This review explores the use of brain age estimation from MRI scans as a biomarker of brain health. With disorders like Alzheimer's and Parkinson's increasing globally, there is an urgent need for early detection tools that can identify at-risk individuals before cognitive symptoms emerge. Brain age offers a noninvasive, quantitative measure of neurobiological ageing, with applications in early diagnosis, disease monitoring, and personalized medicine. Studies show that individuals with Alzheimer's, mild cognitive impairment (MCI), and Parkinson's have older brain ages than their chronological age. Longitudinal research indicates that brain-predicted age difference (brain-PAD) rises with disease progression and often precedes cognitive decline. Advances in deep learning and multimodal imaging have improved the accuracy and interpretability of brain age predictions. Moreover, socioeconomic disparities and environmental factors significantly affect brain aging, highlighting the need for inclusive models. Brain age estimation is a promising biomarker for identify future risk of neurodegenerative disease, monitoring progression, and helping prognosis. Challenges like implementation of standardization, demographic biases, and interpretability remain. Future research should integrate brain age with biomarkers and multimodal imaging to enhance early diagnosis and intervention strategies.

Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiation therapy (RT). However, for non-coplanar beams, potential collisions between the treatment couch and the on-board imaging system limit the range that the gantry can be rotated. Limited-angle measurements are often insufficient to generate high-quality volumetric images for image-domain registration, therefore limiting the use of CBCT for position verification. An alternative to image-domain registration is to use a few 2D projections acquired by the onboard kV imager to register with the 3D planning CT for patient position verification, which is referred to as 2D-3D registration. The 2D-3D registration involves converting the 3D volume into a set of digitally reconstructed radiographs (DRRs) expected to be comparable to the acquired 2D projections. The domain gap between the generated DRRs and the acquired projections can happen due to the inaccurate geometry modeling in DRR generation and artifacts in the actual acquisitions. We aim to improve the efficiency and accuracy of the challenging 2D-3D registration problem in non-coplanar RT with limited-angle CBCT scans. We designed an accelerated, dataset-free, and patient-specific 2D-3D registration framework based on an implicit neural representation (INR) network and a composite similarity measure. The INR network consists of a lightweight three-layer multilayer perception followed by average pooling to calculate rigid motion parameters, which are used to transform the original 3D volume to the moving position. The Radon transform and imaging specifications at the moving position are used to generate DRRs with higher accuracy. We designed a composite similarity measure consisting of pixel-wise intensity difference and gradient differences between the generated DRRs and acquired projections to further reduce the impact of their domain gap on registration accuracy. We evaluated the proposed method on both simulation data and real phantom data acquired from a Varian TrueBeam machine. Comparisons with a conventional non-deep-learning registration approach and ablation studies on the composite similarity measure were conducted to demonstrate the efficacy of the proposed method. In the simulation data experiments, two X-ray projections of a head-and-neck image with <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><msup><mn>45</mn> <mo>∘</mo></msup> <annotation>${45}^\circ$</annotation></semantics> </math> discrepancy were used for the registration. The accuracy of the registration results was evaluated on experiments set up at four different moving positions with ground-truth moving parameters. The proposed method achieved sub-millimeter accuracy in translations and sub-degree accuracy in rotations. In the phantom experiments, a head-and-neck phantom was scanned at three different positions involving couch translations and rotations. We achieved translation errors of <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mo><</mo> <mn>2</mn> <mspace></mspace> <mi>mm</mi></mrow> <annotation>$< 2\nobreakspace {\rm mm}$</annotation></semantics> </math> and subdegree accuracy for pitch and roll. Experiments on registration using different numbers of projections with varying angle discrepancies demonstrate the improved accuracy and robustness of the proposed method, compared to both the conventional registration approach and the proposed approach without certain components of the composite similarity measure. We proposed a dataset-free lightweight INR-based registration with a composite similarity measure for the challenging 2D-3D registration problem with limited-angle CBCT scans. Comprehensive evaluations of both simulation data and experimental phantom data demonstrated the efficiency, accuracy, and robustness of the proposed method.

Accurately registering breast MR images from different time points enables the alignment of anatomical structures and tracking of tumor progression, supporting more effective breast cancer detection, diagnosis, and treatment planning. However, the complexity of dense tissue and its highly non-rigid nature pose challenges for conventional registration methods, which primarily focus on aligning general structures while overlooking intricate internal details. To address this, we propose \textbf{GuidedMorph}, a novel two-stage registration framework designed to better align dense tissue. In addition to a single-scale network for global structure alignment, we introduce a framework that utilizes dense tissue information to track breast movement. The learned transformation fields are fused by introducing the Dual Spatial Transformer Network (DSTN), improving overall alignment accuracy. A novel warping method based on the Euclidean distance transform (EDT) is also proposed to accurately warp the registered dense tissue and breast masks, preserving fine structural details during deformation. The framework supports paradigms that require external segmentation models and with image data only. It also operates effectively with the VoxelMorph and TransMorph backbones, offering a versatile solution for breast registration. We validate our method on ISPY2 and internal dataset, demonstrating superior performance in dense tissue, overall breast alignment, and breast structural similarity index measure (SSIM), with notable improvements by over 13.01% in dense tissue Dice, 3.13% in breast Dice, and 1.21% in breast SSIM compared to the best learning-based baseline.

The study aimed to develop machine learning (ML) models for pretherapy prediction of absorbed doses (ADs) in kidneys and tumoral lesions for metastatic castration-resistant prostate cancer (mCRPC) patients undergoing [¹⁷⁷Lu]Lu-PSMA-617 (Lu-PSMA) radioligand therapy (RLT). By leveraging radiomic features (RFs) from [⁶⁸Ga]Ga-PSMA-11 (Ga-PSMA) PET/CT scans and clinical biomarkers (CBs), the approach has the potential to improve patient selection and tailor dosimetry-guided therapy. Twenty patients with mCRPC underwent Ga-PSMA PET/CT scans prior to the administration of an initial 6.8±0.4 GBq dose of the first Lu-PSMA RLT cycle. Post-therapy dosimetry involved sequential scintigraphy imaging at approximately 4, 48, and 72 h, along with a SPECT/CT image at around 48 h, to calculate time-integrated activity (TIA) coefficients. Monte Carlo (MC) simulations, leveraging the Geant4 application for tomographic emission (GATE) toolkit, were employed to derive ADs. The ML models were trained using pretherapy RFs from Ga-PSMA PET/CT and CBs as input, while the ADs in kidneys and lesions (n=130), determined using MC simulations from scintigraphy and SPECT imaging, served as the ground truth. Model performance was assessed through leave-one-out cross-validation (LOOCV), with evaluation metrics including R² and root mean squared error (RMSE). The mean delivered ADs were 0.88 ± 0.34 Gy/GBq for kidneys and 2.36 ± 2.10 Gy/GBq for lesions. Combining CBs with the best RFs produced optimal results: the extra trees regressor (ETR) was the best ML model for predicting kidney ADs, achieving an RMSE of 0.11 Gy/GBq and an R² of 0.87. For lesion ADs, the gradient boosting regressor (GBR) performed best, with an RMSE of 1.04 Gy/GBq and an R² of 0.77. Integrating pretherapy Ga-PSMA PET/CT RFs with CBs shows potential in predicting ADs in RLT. To personalize treatment planning and enhance patient stratification, it is crucial to validate these preliminary findings with a larger sample size and an independent cohort.

Percutaneous screw fixation technique in pelvic trauma surgery is an extremely challenging operation that typically requires a trial-and-error insertion process under the guidance of continuous intraoperative X-ray. This process can be simplified by utilizing surgical navigation systems. Understanding the complexity of the intraosseous pelvis corridor is essential for establishing the optimal screw corridor, which further facilitates preoperative planning and intraoperative application. Traditional screw corridor search algorithms necessitate traversing the entrance and exit areas of the screw and calculating the distance from the corridor axis to the bone surface to ascertain the location of the screw. This process is computationally complex, and manual measurement by the physician is time consuming, labor intensive, and empirically dependent. In this study, we propose an automated planning algorithm for pelvic screw corridors based on deep learning deformable registration technology, which can efficiently and accurately identify the optimal screw corridors. Compared to traditional methods, the innovations of this study include: (1) the introduction of corridor safety range constraints on screw positioning, which enhances search efficiency; (2) the application of deep learning deformable registration to facilitate the automatic annotation of the screw entrance and exit areas, as well as the safety range of the corridor; and (3) the development of a highly efficient algorithm for optimal corridor searching, quickly determining the corridor without traversing the entrance and exit areas and enhancing efficiency via a vector-based diameter calculation method. The whole framework of the algorithm consists of three key components: atlas generation module, deformable registration and optimal corridor searching strategy. In the experiments, we test the performance of the proposed algorithm on 198 intact pelvises for calculating the optimal corridor of anterior column corridor and S1 sacroiliac screws. The results show that the new algorithm can increase the corridor diameter by 2.1%-3.3% compared to manual measurements, while significantly reducing the average time from 1038s and 3398s to 18.9s and 26.7s on anterior column corridor and S1 sacroiliac corridor, respectively, compared to the traditional screw searching algorithm. This demonstrates the advantages of the algorithm in terms of efficiency and accuracy. However, the current method is validated only on intact pelvises; further research is required for pelvic fracture scenarios.