Dosimetric comparison between laterality-specific and general knowledge-based planning models for nonsmall cell lung cancer.

To investigate the dosimetric impact of laterality-specific RapidPlan models for nonsmall cell lung cancer. Three RapidPlan models were developed and validated for Right, Left, and General conventional lung radiotherapy. Each model was trained using 50 plans.

Cross-shaped windows transformer with self-supervised pretraining for clinically significant prostate cancer detection in bi-parametric MRI.

Background: Bi-parametric magnetic resonance imaging (bpMRI) has demonstrated promising results in prostate cancer (PCa) detection. Vision transformers have achieved competitive performance compared to convolutional neural network (CNN) in deep learning, but they need abundant annotated data for training. Self-supervised learning can effectively leverage unlabeled data to extract useful semantic representations without annotation and its associated costs.

Photon-Counting CT in Cancer Radiotherapy: Technological Advances and Clinical Benefits.

Photon-counting computed tomography (PCCT) marks a significant advancement over conventional energy-integrating detector (EID) CT systems. This review highlights PCCT's superior spatial and contrast resolution, reduced radiation dose, and multi-energy imaging capabilities, which address key challenges in radiotherapy, such as accurate tumor delineation, precise dose calculation, and treatment response monitoring. PCCT's improved anatomical clarity enhances tumor targeting while minimizing damage to surrounding healthy tissues.

Ensuring High Quality Treatment Plans with a Plan Quality Review Checklist.

Treatment plan quality is a crucial component for a successful outcome of radiation therapy treatments. As the complexity of radiation therapy planning and delivery techniques increases, the role of the medical physicist in assessing treatment plan quality becomes more critical. Integrating plan quality review throughout the treatment planning process allows improvements without delaying treatment or rushing to produce changes at the last minute.

Lung SBRT: Dose gradient optimization based on target size.

This study investigated optimization settings that steepen the dose gradient as a function of target size for lung stereotactic body radiation therapy (SBRT). Sixty-eight lung SBRT patients with planning target volumes (PTVs) ranging from 2-203 cc were categorized into small (<20 cc), medium (20-50 cc), and large (>50 cc) groups. VMAT plans were generated using the normal tissue objective (NTO) to penalize the dose gradient at progressively steeper NTO fall-off values (0.

A lung SBRT treatment planning technique to focus high dose on gross disease.

This study investigated a straightforward treatment planning technique for definitive stereotactic body radiation therapy (SBRT) for patients with early-stage lung cancer aimed at increasing dose to gross disease by strategically penalizing the normal tissue objective (NTO) in the Eclipse treatment planning system. Twenty-five SBRT cases were replanned to 50 Gy in 5 fractions using static and dynamic NTO methods (50 plans total). The NTO had a start dose of 100% at the target border, end dose of 20%, fall-off rate of 0.

CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model.

The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients.A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans.

Image-domain material decomposition for dual-energy CT using unsupervised learning with data-fidelity loss.

Background: Dual-energy computed tomography (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold.

Diffeomorphic transformer-based abdomen MRI-CT deformable image registration.

Background: Stereotactic body radiotherapy (SBRT) is a well-established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs-at-risk (OAR) toxicity from radiation. MRI-CT deformable image registration (DIR) is required to propagate the contours defined on high-contrast MRI to CT images.

Diffeomorphic Transformer-based Abdomen MRI-CT Deformable Image Registration.

Background: Stereotactic body radiotherapy (SBRT) is a well-established treatment modality for liver metastases in patients unsuitable for surgery. Both CT and MRI are useful during treatment planning for accurate target delineation and to reduce potential organs-at-risk (OAR) toxicity from radiation. MRI-CT deformable image registration (DIR) is required to propagate the contours defined on high-contrast MRI to CT images.

Cardiac MRI segmentation using shifted-window multilayer perceptron mixer networks.

. In this work, we proposed a deep-learning segmentation algorithm for cardiac magnetic resonance imaging to aid in contouring of the left ventricle, right ventricle, and Myocardium (Myo).We proposed a shifted window multilayer perceptron (Swin-MLP) mixer network which is built upon a 3D U-shaped symmetric encoder-decoder structure.

MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.

Purpose: Glioblastoma (GBM) is aggressive and malignant. The methylation status of the -methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests.

MRI-based prostate cancer classification using 3D efficient capsule network.

Background: Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information.

Total scalp irradiation: A study comparing multiple types of bolus and VMAT optimization techniques.

Purpose: To investigate bolus design and VMAT optimization settings for total scalp irradiation.

Methods: Three silicone bolus designs (flat, hat, and custom) from .decimal were evaluated for adherence to five anthropomorphic head phantoms.

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling.

. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution.

Lesion segmentation on F-fluciclovine PET/CT images using deep learning.

Background And Purpose: A novel radiotracer, F-fluciclovine (anti-3-F-FACBC), has been demonstrated to be associated with significantly improved survival when it is used in PET/CT imaging to guide postprostatectomy salvage radiotherapy for prostate cancer. We aimed to investigate the feasibility of using a deep learning method to automatically detect and segment lesions on F-fluciclovine PET/CT images.

Materials And Methods: We retrospectively identified 84 patients who are enrolled in Arm B of the Emory Molecular Prostate Imaging for Radiotherapy Enhancement (EMPIRE-1) trial.

Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy.

The advantage of proton therapy as compared to photon therapy stems from the Bragg peak effect, which allows protons to deposit most of their energy directly at the tumor while sparing healthy tissue. However, even with such benefits, proton therapy does present certain challenges. The biological effectiveness differences between protons and photons are not fully incorporated into clinical treatment planning processes.

Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method.

Background: The number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients.

Image-Domain Material Decomposition for Dual-energy CT using Unsupervised Learning with Data-fidelity Loss.

Background: Dual-energy CT (DECT) and material decomposition play vital roles in quantitative medical imaging. However, the decomposition process may suffer from significant noise amplification, leading to severely degraded image signal-to-noise ratios (SNRs). While existing iterative algorithms perform noise suppression using different image priors, these heuristic image priors cannot accurately represent the features of the target image manifold.