Scientist Challenges CERN to Test Tech He Claims Saves Billions & Lives

DALLAS, TX – March 20, 2026 – An Italian-American scientist is publicly challenging the titans of high-energy physics and the medical imaging industry to a definitive test of a technology he claims has been systematically ignored for over 30 years. Dario Crosetto, through his nonprofit Crosetto Foundation, asserts that his 3D-Flow architecture could save CERN over $12 billion in research waste and revolutionize early cancer detection, saving millions of lives. The foundation is now demanding a public ‘Known Dataset Test’ to prove these claims once and for all.

The challenge is set to confront the scientific establishment at the upcoming Total Body PET (TBPET) Conference in Valencia, Spain, this May, where Crosetto has submitted an abstract detailing his work. At its core is a fundamental dispute over how to find a needle in a haystack of data—whether in the subatomic debris of particle collisions or the faint metabolic signals of nascent tumors.

A Breakthrough Validated, Then Vanished

The story of the 3D-Flow architecture begins not with this recent challenge, but in 1993 at Fermilab, one of America’s premier particle physics laboratories. There, in a full-day scientific review, Crosetto’s invention was officially recognized as a breakthrough for identifying specific signals in ultra-high-speed data streams. This validation was so significant that in 1994, the U.S. government granted him a Green Card for ‘Exceptional Ability’ in just 24 hours, an acknowledgment of expertise bringing tangible benefits to the nation.

Following this, the U.S. Department of Energy awarded Crosetto a $1 million grant in 1995 to complete a feasibility study. By 1999, the study was successfully concluded, with simulations and a complete design for a custom microchip (ASIC) that could implement the 3D-Flow system. According to the Crosetto Foundation, the design was ready to be fabricated. However, the crucial Non-Recurring Engineering (NRE) funding to manufacture the chip never materialized. For three decades, the technology, despite its initial validation and proven feasibility, remained largely on the shelf, a source of immense frustration for its inventor.

The $12 Billion Question for CERN

At the heart of Crosetto’s challenge to CERN is a claim of staggering inefficiency. The Large Hadron Collider (LHC) smashes protons together at a rate of 40 million times per second, generating petabytes of data. To manage this deluge, a sophisticated ‘Level-1 Trigger’ system must make a split-second decision on which data to keep and which to discard. Crosetto argues that the current systems, based on Field-Programmable Gate Arrays (FPGAs), are a critical bottleneck.

He claims these systems perform fewer than 100 programmable operations on each dataset before making a decision, leading to what he calls ‘discovery by luck.’ In this paradigm, rare and potentially groundbreaking ‘Good Events’ are lost in the electronic ‘dead time’ because the trigger isn’t powerful enough to analyze the data stream thoroughly. This, he states, costs taxpayers roughly $4 million daily just to operate the upgraded High-Luminosity LHC.

In stark contrast, Crosetto’s 3D-Flow architecture is designed to perform thousands of operations on the same dataset with zero dead time. He proposes a simple, seconds-long laboratory experiment: the ‘Known Dataset Test.’ The test would involve inserting 1,000 manually identified ‘Good Events’ into a 2-terabyte dataset and running it through two systems. One would be a 6-kilowatt system based on 3D-Flow, executing over 2,800 operations per dataset. The other would be the 650-kilowatt CERN-CMS trigger system, which he claims performs fewer than 100 operations. The foundation asserts this test would definitively prove that current architectures are mathematically incapable of finding hidden events, and that adopting 3D-Flow could prevent over $12 billion in projected waste over the next decade.

A New Paradigm for Cancer Detection?

The implications of the 3D-Flow technology extend far beyond particle physics. Crosetto has applied the same principles to medical imaging, designing a system called the 3D-Complete Body Screening (3D-CBS). The goal is to detect cancer at its earliest possible stage—when it is a cluster of fewer than 100 cells—by more effectively filtering faint tumor-marker signals from background radiation in a Positron Emission Tomography (PET) scan.

The foundation presents a dramatic cost-benefit analysis. The 3D-CBS device is projected to cost $3.5 million, with a per-test cost of around $200. This is compared to state-of-the-art systems like the EXPLORER total-body PET scanner, a remarkable machine that costs upwards of $22 million, with individual scans costing between $4,500 and $9,000. Crosetto argues that while the EXPLORER represents an improvement, its underlying electronics still suffer from the same fundamental limitations as other existing systems.

He points to a stark statistic from his research: an analysis of 100,000 patients over two decades showed that traditional PET screening protocols failed to improve two-year survival rates. His proposed solution is not just a better machine, but a more effective and affordable screening paradigm that could make early, life-saving detection accessible to the broader population. In a personal test of the technology, Crosetto underwent an EXPLORER scan for a confirmed skin cancer but claims the hospital was unable to provide the essential metabolic activity data needed for early management, highlighting what he sees as the device's practical limitations despite its high cost.

A Wall of Institutional Silence

For over thirty years, Crosetto has submitted proposals, distributed his research, and met with industry leaders, including Siemens, General Electric (GE), and United Imaging Healthcare (UIH), the co-developer of the EXPLORER. He alleges a pattern of institutional silence, followed by the quiet adoption of his core ideas. He claims that after extensive technical meetings, these companies later announced efficiency improvements in their PET scanners attributed to new electronics capable of executing complex algorithms—a key feature of his 3D-Flow architecture.

This long struggle has culminated in a public campaign. The Crosetto Foundation is urging citizens in the U.S. and Europe to contact their elected representatives and demand a transparent, public, and comparative scientific review of these technologies. The core of the message is that public administrators have an obligation to ensure accountability for taxpayer funds, especially when a verifiable, cost-effective alternative with life-saving potential is on the table.

The upcoming TBPET conference in Valencia may become an unexpected forum for this debate. The scientific community will be faced with a direct challenge, not just to its technology, but to its principles of openness and verification. Whether Crosetto's ‘Known Dataset Test’ will be accepted or dismissed will be a test in itself—a test of whether science serves truth or institutional inertia.