The central premise of the field has not changed: imaging the dead can answer questions that matter enormously for the living. When a fetus dies in utero, when a newborn succumbs shortly after birth, or when a child dies unexpectedly, families and clinicians want answers. Conventional autopsy has long been the gold standard, but consent rates for full autopsy have fallen across much of the world, driven by religious and cultural concerns, emotional distress and simple unfamiliarity with the procedure. Less-invasive alternatives that use imaging — chiefly postmortem computed tomography and postmortem magnetic resonance imaging — promise a way to recover diagnostic information while respecting families’ wishes. That promise, the commentary argues, has come substantially closer to reality over the past ten years.

The turning point came in organizational form. In 2016, the European Society of Paediatric Radiology convened a dedicated postmortem imaging task force, building on an earlier questionnaire-based survey that had revealed how inconsistent practice was across European centres. That survey, published in 2014, documented wide variation in whether imaging was offered at all, which modalities were used, and how results were reported. The task force gave the field a focal point: a standing group of experts who could translate scattered research findings into practical, consensus-based guidance. The commentary’s authors, who have led much of this effort, frame the past decade as the story of what happened once that structure existed.

The most tangible products of that structure are the protocols. In 2019, a joint working group of the European Society of Paediatric Radiology and the International Society for Forensic Radiology and Imaging published the first standardized protocol for paediatric postmortem computed tomography, specifying how scans should be acquired in children of different sizes and ages. More recently, the task force has turned to magnetic resonance imaging, publishing a referral template for fetal and neonatal postmortem imaging in 2024, a full clinical protocol for fetal and neonatal postmortem MRI in 2025, and, in 2026, recommendations for standardized structured reporting of those examinations. Reporting guidance for perinatal and paediatric postmortem CT followed a parallel track, published in Insights into Imaging in 2024.

Standardized reporting deserves particular emphasis because it addresses one of the field’s most stubborn problems. An imaging study is only as useful as the report that follows it, and early adopters of postmortem MRI found that radiologists described findings in wildly different ways, making it difficult to compare results across centres or to accumulate the evidence base needed to convince coroners, pathologists and funders. The 2026 structured reporting recommendations aim to fix this by defining what must be documented, in what order and with what terminology, so that a postmortem MRI report from Amsterdam can be read and trusted in London, Toronto or Melbourne. It is the kind of unglamorous infrastructure work that rarely makes headlines but determines whether a technique becomes routine or remains a curiosity.

The evidence base itself has grown in parallel. One of the decade’s most influential studies, the DRIFT trial published in The Lancet Child & Adolescent Health in 2018, compared chest radiographs with computed tomography for detecting rib fractures in children and provided diagnostic accuracy data that directly inform how skeletal injury should be assessed after death. Other work has pushed into more sophisticated territory: diffusion-weighted postmortem MRI of the fetal brain has been used to help time perinatal deaths, a question with real forensic and medico-legal significance, while feasibility studies have explored multiparametric mapping and spectroscopy approaches that quantify the biochemical changes tissues undergo after death. These quantitative techniques remain largely research tools, but they hint at a future in which postmortem imaging does more than mirror autopsy — it measures things autopsy cannot.

Geography, however, remains a limiting factor. A 2023 survey of North American practice found that paediatric postmortem imaging is used far less consistently there than the European literature might suggest, and a companion European survey published the same year examined the fragile funding streams that support the work. Postmortem imaging often falls between budgetary stools: it is not routine diagnostic imaging with a living patient, nor is it always funded as part of forensic or medico-legal investigation. The commentary is candid that reimbursement and sustainable financing are now among the biggest obstacles to wider adoption, perhaps bigger than any technical question about image quality or diagnostic accuracy.

The institutional response has been to widen the coalition. In 2026, a multi-society statement on implementing paediatric and fetal postmortem imaging into clinical practice was issued jointly by the European Society of Paediatric Radiology, the Society for Pediatric Radiology, the Latin American Society of Pediatric Radiology, the Asian and Oceanic Society for Pediatric Radiology, the World Federation of Pediatric Imaging and the International Association of Forensic Radiographers. That breadth matters. It signals that the field is no longer a European project but a global one, and it gives radiologists, pathologists, coroners and health administrators on multiple continents a common reference point when they argue for local services.

Technology continues to move as well. Among the developments highlighted in the commentary’s own journal is early experience with photon-counting detector CT in paediatric postmortem imaging, a detector technology that offers higher spatial resolution and improved tissue contrast at comparable or lower radiation exposure than conventional CT. For a field whose credibility depends on detecting small, subtle findings — a hairline rib fracture, a tiny intracranial haemorrhage, a subtle malformation — such hardware advances could meaningfully shift diagnostic performance. The commentary also notes the publication of a comprehensive textbook on postmortem imaging of the fetus and child in 2025, consolidating a decade of accumulated knowledge into a single reference for practitioners entering the field.

Where does the field go from here? The commentary’s implicit answer is that the scientific groundwork has largely been laid and the remaining battles are organizational: funding, service implementation, training and the slow work of convincing legal systems and bereaved families that imaging can stand alongside — or in some cases replace — conventional autopsy. For early pregnancy loss, less-invasive autopsy approaches have already been shown to offer families answers where a full autopsy would be declined. The decade since “mortui vivos docent” has not eliminated the need for pathologists, and few in the field claim it should; instead, imaging has become a complementary tool that can guide autopsy, replace it when families refuse it, and extract information from bodies that must otherwise go unexamined. The dead, it turns out, teach the living best when the living build the systems to listen.

Subject of Research: Paediatric and perinatal postmortem imaging

Article Title: 10 years of paediatric and perinatal postmortem imaging: where are we now?

Article References: Arthurs, O. J., & van Rijn, R. R. (2026). 10 years of paediatric and perinatal postmortem imaging: where are we now?. Pediatric Radiology. https://doi.org/10.1007/s00247-026-06795-9

Image Credits: AI Generated

DOI: 10.1007/s00247-026-06795-9

Keywords: postmortem imaging, paediatric radiology, perinatal autopsy, postmortem MRI, postmortem CT, ESPR task force, less-invasive autopsy, fetal imaging, forensic radiology, structured reporting, photon-counting CT, Pediatric Radiology

The key to the approach lies in a piece of physics that dates back to 1976, when Robert Alvarez and Albert Macovski showed that X-ray attenuation in the diagnostic energy range can be described as the sum of just two effects: the photoelectric effect and Compton scattering. The photoelectric contribution depends strongly on the atomic number of the material, while the Compton contribution depends mainly on electron density. If attenuation can be measured at several different X-ray energies, the two contributions can be mathematically separated, and from them the material’s mass density and its effective atomic number — a weighted average of the atomic numbers of the elements it contains — can be calculated. This formalism underlies virtually every spectral CT technique in use today, from virtual non-contrast imaging to material decomposition.

What has historically made such calculations difficult in the clinic is that the raw projection data needed for them are locked away inside proprietary scanner software. Manufacturers guard the sinograms, the scanner geometry, the beam-hardening corrections and the spectral detector response functions that a from-scratch reconstruction would require. The German team sidestepped this obstacle elegantly: instead of raw data, they used virtual monoenergetic images, or VMIs, which the scanner’s own software already produces. VMIs simulate what a CT image would look like if the X-ray beam consisted of photons at a single energy, and because the manufacturer has already corrected them for beam hardening, beam quality and detector efficiency, they arrive ready for quantitative analysis. No knowledge of the bow-tie filter or the tube spectrum is needed.

The scanner in question was the Naeotom Alpha from Siemens Healthineers, the first clinically approved photon-counting CT system, released in 2021. Unlike conventional detectors that merely count photons in aggregate, photon-counting detectors register each X-ray photon individually and sort them by energy, providing spectral data intrinsically with every scan and without any additional radiation dose to the patient. From a single acquisition, the researchers generated virtual monoenergetic reconstructions at six energies — 40, 50, 60, 80, 100 and 150 kiloelectronvolts — using the scanner’s Syngo.Via software, and then imported these image sets into their own Python-based analysis pipeline.

The mathematics at the heart of the method is disarmingly compact. Following the Alvarez-Macovski model, the linear attenuation coefficient at each energy is written as a photoelectric term, scaling with density, effective atomic number and an inverse power of the energy, plus a Compton term described by the Klein-Nishina function. Substituting two composite coefficients reduces the problem to a linear system: with attenuation measured at two or more energies, the coefficients can be solved by linear regression, and from them density and effective atomic number follow directly. Using six energies rather than the minimum of two gives the regression extra robustness against the noise that inevitably contaminates real CT data, producing more stable values in the final maps.

Before the algorithm could touch a scanner, its four free parameters had to be calibrated. The team fitted the model to reference attenuation data from the NIST XCOM database for sixteen elements spanning effective atomic numbers from 5, boron, to 20, calcium — precisely the range occupied by the elements that make up human tissue, and a range conveniently free of the abrupt K-edge absorption features that would complicate the fit. The calibration, performed over 40 to 200 keV, yielded parameters remarkably close to those reported by earlier groups, with a coefficient of determination of 0.9996. A verification step then confirmed that the calibrated model reproduces the literature values for density and atomic number with slopes of almost exactly one, small residual deviations being folded in as correction factors.

To test the method on real scanner output, the researchers prepared a series of alcohol-water mixtures — ethanol at 0, 25, 50, 75 and 95 percent by volume, and isopropanol at 0, 10, 35, 60 and 70 percent. These humble mixtures were chosen deliberately: their densities and effective atomic numbers fall squarely in the soft-tissue range of the human body, they are cheap, safe and easy to prepare, and their expected properties can be calculated from published reference data. The tubes were mounted in a custom epoxy-resin phantom nested inside a larger PMMA ring to mimic the scattering conditions of a human torso, and scanned five times at 140 kilovolts so that image noise could be reduced by averaging.

The results were strikingly accurate. Across both mixture series, the measured mass densities deviated from the expected values by an average of just 1.0 percent, while the effective atomic numbers deviated by 0.84 percent. Pure water yielded an effective atomic number of 7.49 against an expected 7.47, and even the most concentrated alcohol solutions tracked their reference values almost perfectly. The maps themselves visually distinguished every concentration in both series. The accuracy is comparable to that reported by earlier image-domain approaches, such as the work of Heismann and colleagues, which required far more complex preprocessing of raw data to achieve deviations of similar magnitude.

The method is not without limits. The Zeff maps proved noisier than the density maps, because Compton scattering depends only weakly on attenuation in this energy range, leaving the atomic-number estimate more vulnerable to image noise, and residual beam-hardening effects produced a gentle gradient across the phantom. The algorithm is also valid only for materials with effective atomic numbers between 5 and 20 and densities up to about 2.0 grams per cubic centimeter — a range that comfortably covers biological tissue, including cortical bone at roughly 1.8 to 2.0, but excludes denser materials such as metal implants. Strong artifacts that distort Hounsfield units, such as severe beam hardening, will propagate into erroneous map values, since the entire method rests on the fidelity of the underlying image data.

Even so, the study lands at a moment of rapid momentum in quantitative spectral imaging. Recent work by Zimmerman and Poludniowski and by Lustermans and colleagues has demonstrated photon-counting CT’s potential for radiotherapy-related material characterization, while other groups are pursuing physics-informed deep-learning approaches to the same problem. Against those data-hungry machine-learning methods, the new algorithm offers a complementary virtue: it is analytically derived, transparently calibrated against tabulated reference data, computationally light, and runs entirely on image data that any clinical workstation can export. The authors suggest it could support applications from radiation therapy planning, where density and atomic number inform dose calculations, to oncology, where tissue characterization may distinguish tumor from benign lesions, or the identification of uric acid stones. Future studies, they note, must validate the approach across different phantoms, protocols, dose levels and realistic tissue compositions — but the demonstration that hospital-ready images alone can yield quantitative chemical maps marks a meaningful step toward making spectral material analysis a routine part of clinical CT.

Subject of Research: Calculation of mass density and effective atomic number maps from virtual monoenergetic photon-counting CT images

Article Title: Computation of the effective atomic number and mass density from virtual monoenergetic photon-counting CT reconstructions

Article References: Horstmeier, S., Thomsen, F. S., & Borggrefe, J. (2026). Computation of the effective atomic number and mass density from virtual monoenergetic photon-counting CT reconstructions. Heliyon, 12(14), Article e45404. https://doi.org/10.1016/j.heliyon.2026.e45404

Image Credits: AI Generated

DOI: Not provided

Keywords: photon-counting CT, effective atomic number, mass density, virtual monoenergetic imaging, spectral CT, Alvarez-Macovski model, material decomposition, radiation therapy, tissue characterization, Heliyon, X-ray attenuation, quantitative imaging