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Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences. [ABSTRACT FROM AUTHOR]

Background: Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)‐guided radiation treatment precision. Data‐driven sparse MRI reconstruction, however, requires large‐scale training datasets for prior learning, which is time‐consuming and challenging to acquire in clinical settings. Purpose: To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR‐guided radiotherapy. Methods: A multi‐layer perceptron network was trained to parameterize the NeRP model of a patient‐specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior‐augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5‐min volumetric MRI time series with 340 ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden‐angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30 s of the time series for prior embedding and augmentation. The remaining 4.5‐min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model‐reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Results: Across the seven patients evaluated, the peak signal‐to‐noise‐ratio between model‐reconstructed and ground truth MR images was 38.02 ± 2.60 dB and the structure similarity index measure was 0.98 ± 0.01. Throughout the 4.5‐min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model‐reconstructed and ground truth MRI showed good consistency. The 95‐percentile Hausdorff distance between GTV contours was 2.41 ± 0.77 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all three orthogonal directions. Conclusion: A prior‐augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR‐guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large‐scale training datasets. [ABSTRACT FROM AUTHOR]

A wide range of mono- and disubstituted 2,3-dihydroquinazolin-4(1 H)-ones were synthesized via a one-pot three components condensation of isatoic anhydride, aldehydes, and ammonium salts or primary amines in the presence of aluminum methanesulfonate in EtOH/HO solution. The catalyst is reusable and could be recycled for several runs without any distinct decrease in its efficiency. A plausible mechanism for this one-pot three components reaction was proposed. [ABSTRACT FROM AUTHOR]

Separating bone from air in MR data is one of the major challenges in using MR images to derive synthetic CT. The problem is further complicated when the anatomic regions filled with air are altered across scans due to air mobility, for instance, in pelvic regions, thereby the air regions estimated using an ultrashort echo time (UTE) sequence are invalid in other image series acquired for multispectral classification. This study aims to develop and investigate a female pelvic bone shape model to identify low intensity regions in MRI where air is unlikely to be present in support of synthetic CT generation without UTE imaging. CT scans of 30 patients were collected for the study, 17 of them also have corresponding MR scans. The shape model was built from the CT dataset, where the reference image was aligned to each of the training images using B-spline deformable registration. Principal component analysis was performed on B-spline coefficients for a compact model where shape variance was described by linear combination of principal modes. The model was applied to identify pelvic bone in MR images by deforming the corresponding MR data of the reference image to target MR images, where the search space of the deformation process was constrained within the subspace spanned by principal modes. The local minima in the search space were removed effectively by the shape model, thus supporting an efficient binary search for the optimal solution. We evaluated the model by its efficacy in identifying bone voxels and excluding air regions. The model was tested across the 17 patients that have corresponding MR scans using a leave-one-out cross validation. A simple model using the first leading principal mode only was found to achieve reasonable accuracy, where an averaged 87% of bone voxels were correctly identified. Finally dilation of the optimally fit bone mask by 5 mm was found to cover 96% of bone voxels while minimally impacting the overlap with air (below 0.4%).