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Hybrid AI Approach Cuts Mammography Workload by 38%
A Dutch research team demonstrated that a 'hybrid' AI strategy can reduce radiologist workload in mammography screening by nearly 40% without affecting performance.
Habitat AI Model Improves Risk Stratification of Lung Nodules on LDCT
A 'habitat' AI model outperforms standard 2D approaches in stratifying lung adenocarcinoma risk in subsolid nodules on low-dose CT scans.
AI Model Improves Prediction of Knee Osteoarthritis Progression Using MRI and Biomarkers
A new AI-assisted model that combines MRI, biochemical, and clinical data improves predictions of worsening knee osteoarthritis.
From the Research Hub
Advancements in deep learning for image-guided tumor ablation therapies: a comprehensive review.
Image-guided tumor ablation (IGTA) has revolutionized modern oncological treatments by providing minimally invasive options that ensure precise tumor eradication with minimal patient discomfort. Traditional techniques such as ultrasound (US), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) have been instrumental in the planning, execution, and evaluation of ablation therapies. However, these methods often face limitations, including poor contrast, susceptibility to artifacts, and variability in operator expertise, which can undermine the accuracy of tumor targeting and therapeutic outcomes. Incorporating deep learning (DL) into IGTA represents a significant advancement that addresses these challenges. This review explores the role and potential of DL in different phases of tumor ablation therapy: preoperative, intraoperative, and postoperative. In the preoperative stage, DL excels in advanced image segmentation, enhancement, and synthesis, facilitating precise surgical planning and optimized treatment strategies. During the intraoperative phase, DL supports image registration and fusion, and real-time surgical planning, enhancing navigation accuracy and ensuring precise ablation while safeguarding surrounding healthy tissues. In the postoperative phase, DL is pivotal in automating the monitoring of treatment responses and in the early detection of recurrences through detailed analyses of follow-up imaging. This review highlights the essential role of deep learning in modernizing IGTA, showcasing its significant implications for procedural safety, efficacy, and patient outcomes in oncology. As deep learning technologies continue to evolve, they are poised to redefine the standards of care in tumor ablation therapies, making treatments more accurate, personalized, and patient-friendly.
Dedicated prostate DOI-TOF-PET based on the ProVision detection concept.
The ProVision scanner is a dedicated prostate PET system with limited angular coverage; it employs a new detector technology that provides high spatial resolution as well as information about depth-of-interaction (DOI) and time-of-flight (TOF). The goal of this work is to develop a flexible image reconstruction framework and study the image performance of the current ProVision scanners.
Approach: Experimental datasets, including point-like sources, an image quality phantom, and a pelvic phantom, were acquired using the ProVision scanner to investigate the impact of oblique lines of response introduced via a multi-offset scanning protocol. This approach aims to mitigate data truncation artifacts and further characterise the current imaging performance of the system. For image reconstruction, we applied the list-mode Maximum Likelihood Expectation Maximisation algorithm incorporating TOF information. The system matrix and sensitivity models account for both detector attenuation and position uncertainty.
Main Results: The scanner provides good spatial resolution on the coronal plane; however, elongations caused by the limited angular coverage distort the reconstructed images. The availability of TOF and DOI information, as well as the addition of a multi-offset scanning protocol, could not fully compensate for these distortions.
Significance: The ProVision scanner concept, with innovative detector technology, shows promising outcomes for fast and inexpensive PET without CT. Despite current limitations due to limited angular coverage, which leads to image distortions, ongoing advancements, such as improved timing resolution, regularisation techniques, and artificial intelligence, are expected to significantly reduce these artifacts and enhance image quality.
A Cardiac-specific CT Foundation Model for Heart Transplantation
Heart failure is a major cause of morbitidy and mortality, with the severest forms requiring heart transplantation. Heart size matching between the donor and recipient is a critical step in ensuring a successful transplantation. Currently, a set of equations based on population measures of height, weight, sex and age, viz. predicted heart mass (PHM), are used but can be improved upon by personalized information from recipient and donor chest CT images. Here, we developed GigaHeart, the first heart-specific foundation model pretrained on 180,897 chest CT volumes from 56,607 patients. The key idea of GigaHeart is to direct the foundation models attention towards the heart by contrasting the heart region and the entire chest, thereby encouraging the model to capture fine-grained cardiac features. GigaHeart achieves the best performance on 8 cardiac-specific classification tasks and further, exhibits superior performance on cross-modal tasks by jointly modeling CT images and reports. We similarly developed a thorax-specific foundation model and observed promising performance on 9 thorax-specific tasks, indicating the potential to extend GigaHeart to other organ-specific foundation models. More importantly, GigaHeart addresses the heart sizing problem. It avoids oversizing by correctly segmenting the sizes of hearts of donors and recipients. In regressions against actual heart masses, our AI-segmented total cardiac volumes (TCVs) has a 33.3% R2 improvement when compared to PHM. Meanwhile, GigaHeart also solves the undersizing problem by adding a regression layer to the model. Specifically, GigaHeart reduces the mean squared error by 57% against PHM. In total, we show that GigaHeart increases the acceptable range of donor heart sizes and matches more accurately than the widely used PHM equations. In all, GigaHeart is a state-of-the-art, cardiac-specific foundation model with the key innovation of directing the models attention to the heart. GigaHeart can be finetuned for accomplishing a number of tasks accurately, of which AI-assisted heart sizing is a novel example.
From the RadAI FDA Approvals Database
Brightonix Imaging
PHAROS
PHAROS is a computed emission tomography system used in radiology to help clinicians obtain detailed three-dimensional images of the body. This technology assists in the diagnosis and monitoring of various medical conditions by providing high-quality images that aid in accurate assessment and treatment planning.
EOS imaging
VEA Align; spineEOS
VEA Align; spineEOS by EOS imaging is an AI-based software designed to automatically process radiological images of the spine. It helps clinicians by aligning and analyzing spinal images, improving accuracy and efficiency in diagnosing and treatment planning for spinal conditions.
Dentsply Sirona Inc.
DS Core Diagnosis
DS Core Diagnosis by Dentsply Sirona Inc. is an AI-powered software designed to process radiological images automatically. It helps clinicians by analyzing medical images, enabling more efficient and accurate diagnosis in radiology.
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