Izotropic’s AI Breakthrough Positions IzoView to Redefine Breast CT Imaging Standards
Izotropic Corporation (OTCQB: IZOZF) has announced a significant AI breakthrough in breast CT imaging with its proprietary machine-learning reconstruction algorithm integration into the IzoView Breast CT Imaging System. The algorithm, developed in collaboration with Johns Hopkins University School of Medicine, enhances image quality while maintaining low radiation doses.
The company's innovative AI solution addresses key limitations of existing CT denoising methods like Model-Based Iterative Reconstruction (MBIR) and Deep Machine-Learning Reconstruction (DMLR). The algorithm works on raw X-ray data before reconstruction, enabling faster processing and consistent image optimization at low radiation doses.
Trained on 15 years of specialized breast CT data and protected as a trade secret, the technology provides Izotropic with a significant competitive advantage in the breast CT imaging market.
Izotropic Corporation (OTCQB: IZOZF) ha annunciato un importante progresso basato sull'IA per la tomografia mammaria (breast CT), integrando il suo algoritmo proprietario di ricostruzione basata sul machine learning nel IzoView Breast CT Imaging System. Sviluppato in collaborazione con la Johns Hopkins University School of Medicine, l'algoritmo migliora la qualità delle immagini mantenendo dosi di radiazione contenute.
La soluzione affronta i limiti delle tecniche di denoising CT esistenti, come il Model-Based Iterative Reconstruction (MBIR) e il Deep Machine-Learning Reconstruction (DMLR). L'algoritmo opera sui dati X-ray grezzi prima della ricostruzione, permettendo processi più rapidi e un'ottimizzazione costante delle immagini a basse dosi.
Addestrata su 15 anni di dati specializzati di breast CT e tutelata come segreto commerciale, la tecnologia conferisce a Izotropic un vantaggio competitivo nel mercato della tomografia mammaria.
Izotropic Corporation (OTCQB: IZOZF) ha anunciado un importante avance de IA en imágenes de tomografía mamaria, integrando su algoritmo propietario de reconstrucción por machine learning en el IzoView Breast CT Imaging System. Desarrollado junto a la Johns Hopkins University School of Medicine, el algoritmo mejora la calidad de imagen manteniendo dosis de radiación bajas.
La solución innovadora supera limitaciones de métodos actuales de supresión de ruido en TC, como Model-Based Iterative Reconstruction (MBIR) y Deep Machine-Learning Reconstruction (DMLR). El algoritmo actúa sobre los datos de rayos X en bruto antes de la reconstrucción, lo que permite un procesamiento más rápido y una optimización consistente de imágenes con bajas dosis.
Entrenada con 15 años de datos especializados de breast CT y protegida como secreto comercial, la tecnología otorga a Izotropic una ventaja competitiva en el mercado de la tomografía mamaria.
Izotropic Corporation (OTCQB: IZOZF)는 자체 머신러닝 재구성 알고리즘을 IzoView Breast CT Imaging System에 통합해 유방 CT 영상 분야에서 중요한 AI 성과를 발표했습니다. Johns Hopkins University School of Medicine과 공동 개발한 이 알고리즘은 방사선량을 낮게 유지하면서 영상 품질을 향상시킵니다.
이 혁신적 AI 솔루션은 Model-Based Iterative Reconstruction(MBIR) 및 Deep Machine-Learning Reconstruction(DMLR) 같은 기존 CT 노이즈 제거 기법의 한계를 해결합니다. 알고리즘은 재구성 이전의 원시 X선 데이터에서 동작해 처리 속도를 높이고 저선량에서 일관된 영상 최적화를 가능하게 합니다.
15년간의 전문 유방 CT 데이터로 학습되고 영업 비밀로 보호되는 이 기술은 유방 CT 시장에서 Izotropic에 상당한 경쟁 우위를 제공합니다.
Izotropic Corporation (OTCQB: IZOZF) a annoncé une avancée majeure en IA pour l'imagerie CT mammaire, en intégrant son algorithme propriétaire de reconstruction par apprentissage automatique dans le IzoView Breast CT Imaging System. Développé en collaboration avec la Johns Hopkins University School of Medicine, l'algorithme améliore la qualité des images tout en maintenant des doses de radiation faibles.
La solution innovante corrige les limites des méthodes actuelles de débruitage CT, telles que le Model-Based Iterative Reconstruction (MBIR) et le Deep Machine-Learning Reconstruction (DMLR). L'algorithme agit sur les données de rayons X brutes avant la reconstruction, permettant un traitement plus rapide et une optimisation constante des images à faibles doses.
Entraînée sur 15 ans de données spécialisées de breast CT et protégée comme secret commercial, la technologie confère à Izotropic un avantage concurrentiel significatif sur le marché de l'imagerie mammaire.
Izotropic Corporation (OTCQB: IZOZF) hat einen bedeutenden KI-Fortschritt bei der Brust-CT-Bildgebung angekündigt: die Integration seines proprietären Machine-Learning-Rekonstruktionsalgorithmus in das IzoView Breast CT Imaging System. Der gemeinsam mit der Johns Hopkins University School of Medicine entwickelte Algorithmus verbessert die Bildqualität bei gleichzeitiger Beibehaltung niedriger Strahlendosen.
Die innovative KI-Lösung behebt zentrale Schwächen bestehender CT-Entstörverfahren wie Model-Based Iterative Reconstruction (MBIR) und Deep Machine-Learning Reconstruction (DMLR). Der Algorithmus arbeitet auf den rohen Röntgendaten vor der Rekonstruktion, was schnellere Verarbeitung und eine gleichmäßige Bildoptimierung bei niedrigen Dosen ermöglicht.
Trainiert mit 15 Jahren spezialisierter Brust-CT-Daten und als Geschäftsgeheimnis geschützt, verschafft die Technologie Izotropic einen deutlichen Wettbewerbsvorteil im Markt für Brust-CT.
- Proprietary AI algorithm improves image quality while maintaining low radiation doses
- Algorithm is trained on 15 years of specialized breast CT data, creating strong competitive barriers
- Technology is protected as a trade secret, providing durable market advantage
- Solution overcomes limitations of existing denoising methods in clinical workflows
- Positions company for future AI diagnostic applications integration
- No immediate revenue impact or commercialization timeline mentioned
- Regulatory approval status and timeline not specified
- Proprietary algorithm positions IzoView to redefine global standards and expectations for image quality and safety in breast CT -
- Trained on 15 years of specialized breast CT data and protected as a trade secret, giving Izotropic a durable competitive edge -
- AI enhances IzoView’s image quality by reducing CT image noise without increasing radiation dose -
VANCOUVER, British Columbia, and SACRAMENTO, Calif., Aug. 28, 2025 (GLOBE NEWSWIRE) -- via IBN – Izotropic Corporation (CSE: IZO) (OTCQB: IZOZF) (FSE: 1R3) (“Izotropic”, or the “Company”), a medical device company commercializing innovative, emerging technologies and imaging-based products for the more accurate screening, diagnoses, and treatment of breast cancers, is pleased to announce the integration of its proprietary AI-based machine-learning reconstruction algorithm into its flagship device, the IzoView Breast CT Imaging System (“IzoView”).
The Algorithm
Artificial intelligence is reshaping medical imaging, and Izotropic is leading that shift with breast CT. In collaboration with The Johns Hopkins University School of Medicine, the Company has developed a proprietary machine-learning reconstruction algorithm designed to further improve IzoView’s image quality while maintaining low radiation doses, without the constraints that limit other AI methods.
Noise in CT Imaging
All CT imaging, including breast CT, naturally exhibits image noise, seen as a grainy texture in the reconstructed image. The less radiation used, the more visible image noise can become. Since CT systems are designed to minimize dose for patient safety, there is a trade-off between image quality and radiation exposure. Image noise is recognized as an inherent characteristic of all CT imaging and a key area of opportunity for technical innovation.
Existing AI Denoising Methods
Most traditional post-image-processing denoising techniques offer limited noise suppression and have led to the development of two modern denoising methods: Model-Based Iterative Reconstruction (“MBIR”) and Deep Machine-Learning Reconstruction (“DMLR”), both of which typically work to clean up image noise after the image has been reconstructed. While both have advanced the field, each comes with limitations that render them impractical for routine clinical workflows and applications where time and throughput are critical, as is the case in breast cancer screening.
MBIR improves image quality by simulating how X-rays travel through the body and refines the image through repeated calculations. While it can reduce noise, the method is extremely slow, often taking minutes to reconstruct a single image. It requires significant computing power, putting a drain on hospital and clinic IT infrastructures, and can produce images that look overly smooth, hiding subtle features that radiologists rely on for diagnosis. With breast CT producing up to 500 images in a single scan, MBIR is impractical for real-world use.
DMLR uses AI to remove noise, but most approaches require pairs of high and low radiation dose scans to train the algorithm, which is unrealistic and increases radiation exposure. Other variations have been developed that learn from single low-dose scans, but they tend to perform poorly in breast CT, where image noise tends to be correlated in ways that confuse algorithms and make it difficult to preserve fine anatomical details. As a result, current DMLR methods are not well-suited to breast CT.
IzoView’s AI Solution
Izotropic’s breast CT image reconstruction algorithm is a novel self-supervised deep learning method that produces superior denoising while preserving the natural texture of breast CT images. Unlike other methods that attempt to clean image noise after reconstruction, IzoView’s algorithm works earlier on the raw X-ray data captured before reconstruction. It mitigates long computer processing times, does not require paired image datasets for training, enables consistent image optimization at low radiation doses, and is made for real-world clinical workflows in breast cancer screening environments. This innovation reinforces the case for low-dose protocols in regulatory submissions and could enable IzoView to set the global standard for image quality and radiation dose expectations in breast CT imaging.
Protected as a Trade Secret
As general-purpose AI models become increasingly commoditized, the development of tailored, modality-specific algorithms becomes a major industry differentiator. IzoView’s algorithm is trained specifically on 15 years of breast CT data, a technically complex and specialized imaging domain, and is protected as a trade secret. As generic AI reconstruction and existing denoising methods underperform in breast CT, Izotropic’s algorithm represents a strong competitive edge over legacy breast CT devices currently on the market and would be challenging and costly for others to attempt to replicate.
The Future of IzoView & AI
The integration of Izotropic’s proprietary AI reconstruction algorithm positions IzoView at the forefront of imaging innovation and establishes a strong foundation for the future of intelligent imaging. With computer-aided diagnostics positioned as the radiologist support tool of the future, systems that generate clean, high-quality imaging data will be essential to unlocking their full potential. IzoView’s high-resolution, denoised images, generated in a way that prioritizes patient safety, offer an ideal dataset for training and deploying advanced AI diagnostic applications. As imaging continues to converge with AI and precision care, Izotropic is positioned to drive the next generation of imaging devices and define new standards in breast CT imaging.
About Izotropic:
More information about Izotropic Corporation can be found on its website at izocorp.com and by reviewing its profile on SEDAR at sedarplus.ca.
Forward-Looking Statements:
This document may contain statements that are "Forward-Looking Statements," which are based upon the current estimates, assumptions, projections, and expectations of the Company's management, business, and its knowledge of the relevant market and economic environment in which it operates. The Company has tried, where possible, to identify such information and statements by using words such as "anticipate," "believe," "envision," "estimate," "expect," "intend," "may," "plan," "predict," "project," "target," "potential," "will," "would," "could," "should," "continue," "contemplate" and other similar expressions and derivations thereof in connection with any discussion of future events, trends or prospects or future operating or financial performance, although not all forward-looking statements contain these identifying words.
These statements are not guarantees of performance and involve risks, including those related to capital requirements and uncertainties that are difficult to control or predict, and as such, they may cause future results of the Company's activity to differ significantly from the content and implications of such statements. Forward-Looking Statements are pertinent only as of the date on which they are made, and the Company undertakes no obligation to update or revise any Forward-Looking Statements to reflect new information or the occurrence of future events or circumstances unless otherwise required to do so by law. Neither the Company nor its shareholders, officers, and consultants shall be liable for any action and the results of any action taken by any person based on the information contained herein, including, without limitation, the purchase or sale of Company securities. Nothing in this document should be deemed to be medical or other advice of any kind. All images are for illustrative purposes only. IzoView has not yet been approved or cleared for sale.
Contacts:
Robert Thast, Interim Chief Executive Officer
Telephone: 1-604-220-5031 or 1-833-IZOCORP ext. 1
Email: bthast@izocorp.com
James Gagnon, International Communications
Telephone: 1-604-780-7576 or 1-833-IZOCORP ext. 2
Email: jgagnon@izocorp.com
General and Corporate Inquiries
Telephone: 1-604-825-4778 or 1-833-IZOCORP ext. 3
Email: info@izocorp.com
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FAQ
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