What’s New in Photon Counting CT?

July 16, 2026 · 8 articles · 9 research themes · covering July 09, 2026 – July 16, 2026

Overview

Across this week’s set of studies, a clear theme is the push toward more quantitative, material-aware imaging—especially enabled by spectral photon-counting CT. Work on scanner-specific spectral modeling produced voxelwise effective atomic number and electron density maps, while physics research established performance bounds for estimating proton stopping power ratio and range from photon-counting CT. Together, these efforts strengthen the case for spectral CT not just as a detector of anatomy, but as a tool for physics- and treatment-relevant tissue characterization.

Cardiac imaging is another dominant thread, spanning both clinical positioning and technical optimization. A narrative review reinforces cardiac CT angiography’s role as a first-line strategy for ruling out coronary artery disease in appropriate patients and highlights expanded value for atherosclerosis characterization. Complementing this, photon-counting CT studies show that late iodine enhancement myocardial scar detectability can be improved by tuning spectral analysis and reconstruction parameters, and that deep-learning denoising can enhance ultrahigh-resolution coronary CT angiography for non-calcified plaque assessment.

Finally, radiation-free imaging acceleration and AI reconstruction are gaining momentum in MRI. A feasibility study demonstrated faster, higher-quality 3D bone MRI using AI-driven reconstruction for IR-UTE sequences, and a prospective lung study showed that an AI-powered compressed-sensing MRI protocol can achieve nodule detection performance that meaningfully benchmarks against photon-counting CT. In parallel with spectral CT advances, these MRI results point toward practical, faster workflows for screening and musculoskeletal assessment without ionizing radiation.

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Spectral/Photon-Counting CT Quantification (Zeff, electron density, material properties)

Power law spectral photon-counting CT for quantitative effective atomic number and electron density imaging.

This study developed and evaluated a scanner-specific power-law spectral photon-counting CT method to generate voxelwise effective atomic number (Zeff) and electron density (ρe) maps on a commercial spectral photon-counting CT system (MARS Microlab 5×120 SPCCT), using NIST cross sections to fit model coefficients over 7–115 keV for elements Z=6–21. The key finding was that the power-law model enabled quantitative Zeff and ρe imaging and demonstrated applicability for tissue characterization in heterogeneous samples scanned with a QRM spectral-CT phantom containing soft-tissue-equivalent, hydroxyapatite, and iodine inserts. This supports more accurate, material-relevant quantitative imaging from spectral photon-counting CT for improved tissue characterization.

Alzaabi M, Behouch A, Tariq B et al. · Physics in medicine and biology · (2026) · View on PubMed ↗ · Free PDF ↗

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Photon-Counting CT for Proton Therapy Planning (stopping power/range estimation)

Optimal dimensionality and fundamental limits of proton stopping power estimation with photon-counting CT material decomposition.

This physics study analyzed the fundamental limits of estimating proton stopping power ratio (SPR) and proton range from photon-counting CT (PCCT) data using material decomposition (MD), focusing on how dimensionality of MD affects achievable accuracy. The key finding was that increasing the MD dimensionality with PCCT energy measurements can improve SPR/range estimation but is constrained by fundamental estimation limits. Scientifically, the results define performance bounds that can guide PCCT-based treatment planning strategies to reduce range uncertainty in proton therapy.

Larsson K, Näsmark T, Andersson J et al. · Physics in medicine and biology · (2026) · View on PubMed ↗ · Free PDF ↗

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Photon-Counting CT in Oncology (whole-body or skeletal disease characterization)

Ultra-High-Resolution Dual-Source Photon-Counting CT for Expanded Characterization of Pathophysiological Imaging Patterns in Multiple Myeloma.

This retrospective study evaluated whole-body dual-source photon-counting CT (DS-PCCT) in 84 patients with multiple myeloma (53 therapy-naïve and 31 with precursor conditions) at initial diagnosis, using low-keV virtual monoenergetic images (VMI) to characterize axial and appendicular skeletal manifestations based on soft-tissue and fat attenuation. The study found that DS-PCCT enabled expanded characterization of multiple myeloma pathophysiological imaging patterns by leveraging spectral attenuation differences in low-keV VMI. These findings support DS-PCCT as a high-resolution, spectral whole-body imaging approach that may improve assessment of skeletal involvement in multiple myeloma at diagnosis.

Heidemeier A, Huflage H, Rasche L et al. · Investigative radiology · (2026) · View on PubMed ↗

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Photon-Counting CT for Cardiac Imaging (myocardial scar, late iodine enhancement)

Image-quality optimization for late iodine enhancement with photon-counting CT: impact of spectral analysis and reconstruction parameters.

This single-center retrospective study assessed 74 patients undergoing photon-counting detector CT (PCD-CT) for myocardial scar evaluation with late iodine enhancement (LIE), testing how spectral analysis and reconstruction parameters affected LIE image quality. The key finding was that specific combinations of spectral analysis settings and reconstruction kernel/slice parameters (e.g., Qr40 vs Qr36 kernels and different slice thicknesses) significantly influenced contrast-to-noise ratio and thus LIE detectability. Clinically, optimizing these PCD-CT parameters could improve myocardial scar identification by increasing LIE CNR in routine practice.

Bruno E, Palmisano A, Pisu F et al. · La Radiologia medica · (2026) · View on PubMed ↗

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Cardiac CT in Clinical Practice (reviews/guidelines and diagnostic role)

[Computed tomography in cardiology: its necessity and developments].

This narrative review summarized the necessity and recent developments of computed tomography in cardiology, focusing on cardiac CT angiography (CCTA) and its role in guideline-based non-invasive coronary artery disease evaluation. The key finding was that CCTA has become a central first-line tool for ruling out coronary artery disease in low-to-intermediate pretest probability patients and can also support selected acute/emergency use cases, while enabling broader coronary atherosclerosis characterization beyond luminal stenosis. Scientifically and clinically, the review highlights how advances in CCTA expand diagnostic capability for plaque assessment and patient management.

Breitbart P, Giokoglu E, Yikit E et al. · Innere Medizin (Heidelberg, Germany) · (2026) · View on PubMed ↗ · Free PDF ↗

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AI Reconstruction & Acceleration for MRI (bone, lung, compressed sensing, reconstruction networks)

Fast MRI of bones in the knee - An AI-driven reconstruction approach for adiabatic inversion recovery prepared ultra-short echo time sequences.

This feasibility study investigated an AI-driven reconstruction method for accelerating 3D radial inversion recovery ultra-short echo time (IR-UTE) MRI of knee bone in eight healthy subjects, using a denoising convolutional neural network (DnCNN) trained on undersampled data and reconstructed with conjugate gradient sensitivity encoding (CG-SENSE) within the open-source Pulseq framework. The key finding was that the proposed data-driven reconstruction improved signal-to-noise and enabled faster acquisition/3D bone imaging compared with conventional requirements for IR-UTE. Clinically, this could reduce scan times for radiation-free bone MRI, improving practicality for musculoskeletal imaging.

Nunn PH, Huflage H, Grunz JP et al. · Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · (2026) · View on PubMed ↗

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Deep Learning Denoising for CT (photon-counting CT image quality enhancement)

Deep-learning denoising for ultrahigh-resolution photon-counting detector CT: phantom and in vivo evaluation of non-calcified coronary plaques.

This study evaluated convolutional neural network (CNN) denoising for ultrahigh-resolution photon-counting detector CT (PCD) coronary CT angiography (CCTA) of non-calcified coronary plaques (NCPs) using both a dynamic phantom and in vivo patient data. The key finding was that CNN-based denoising improved image quality for detecting and characterizing non-calcified plaques under varying reconstruction conditions (e.g., sharp kernel Bv64, 0.2/0.4 mm slice thickness, quantum iterative reconstruction levels 3/4) compared with reconstructions without denoising. This suggests that deep-learning denoising can enhance the diagnostic performance of ultrahigh-resolution PCD-CT CCTA for clinically relevant plaque assessment.

Hyska S, Hagar MT, Osoria-Velasquez J et al. · The international journal of cardiovascular imaging · (2026) · View on PubMed ↗ · Free PDF ↗

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AI/CS Accelerated MRI for Lung Screening (nodule detection benchmarking)

Diagnostic performance of a single breath-hold lung MRI scan with AI-powered compressed sensing for nodule detection in comparison to photon counting detector-CT.

This single-center prospective study compared a single breath-hold, AI-powered compressed sensing accelerated 3-T lung MRI protocol (3D T1w-FFE, acceleration factor 9) for lung nodule detection against photon-counting detector CT (PCD-CT) as the reference standard in 148 healthy adults. The key finding was that the AI-CS MRI approach achieved diagnostic performance for nodule detection that could be meaningfully benchmarked against PCD-CT using Lung-RADS scoring and nodule size assessment. Clinically, this supports the potential of faster, radiation-free lung MRI screening strategies as an alternative or complement to photon-counting CT.

Palmisano A, Piccinni G, Serra D et al. · European radiology · (2026) · View on PubMed ↗ · Free PDF ↗

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Generated automatically on July 16, 2026. Covers PubMed articles published July 09, 2026 – July 16, 2026. Summaries are AI-generated; always consult the original publication for clinical or research decisions.