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Can Quantum Iterative Reconstruction Reinvent Photon-Counting CT Technology?
The advanced reconstruction algorithm offers optimal noise reduction, artifact correction, detailed spectral data and streamlined radiation dosing across cardiac, pulmonary and oncologic imaging.
The advent of photon-counting detector (PCD) technology in computed tomography (CT) marks a transformative milestone in medical imaging as it enables the acquisition of extremely detailed spectral data and a significant reduction in electronic noise. This improvement enhances image quality, streamlines radiation dose management, and broadens diagnostic possibilities.
However, to fully exploit the potential of PCD-CT systems, an advanced reconstruction algorithm capable of handling the complexity and volume of multi-energy data is required given that conventional iterative reconstruction (IR) algorithms — developed for energy-integrating detector (EID) CT systems — are not optimized for photon-counting CT due to unique technical challenges, such as the high complexity of the data, the inclusion of spectral information, and specific noise models.1
To address these issues, the quantum iterative reconstruction (QIR) algorithm was specifically designed to maximize the capabilities of PCD-CT. Developed with four levels of intensity, QIR is optimized to meet the hardware and software needs of PCD-CT systems. Capable of being applied to all acquisition modes, including the ultra-high resolution (UHR) mode, QIR is particularly effective for lung imaging and enabling high-quality, ultra-high-resolution views at exceptionally low radiation doses.2
Here one can see improved lesion conspicuity and reduced image noise with increasing levels of quantum iterative reconstruction (QIR) in axial CT images showing the liver of a 46-year-old woman with epithelioid hemangioendothelioma. (Images courtesy of Radiology.)
Quantum iterative reconstruction represents a notable advancement in CT image reconstruction through its model-based approach, which relies on a comprehensive mathematical representation of the entire image acquisition and formation process. The algorithm takes into account every physical aspect of the CT system — from X-ray generation to detection —integrating the specific characteristics of photon-counting detectors, the scanner’s geometry, and various noise contributions (including quantum noise, spectral components, and statistical correlations).2 In addition, QIR considers patient-specific factors such as tissue attenuation and X-ray scatter. The reconstruction process begins with an image obtained via filtered back-projection (FBP) and, through successive iterations, QIR progressively reduces the discrepancies between the “theoretical” image predicted by the model and the actual acquired image until an optimal result is achieved.3
A fundamental characteristic of QIR is its ability to dynamically reduce noise by adapting filtering based on the local signal-to-noise ratio (SNR). More aggressive noise reduction is applied in homogeneous areas, where the risk of losing important information is minimal. However, the filtering is more conservative more for regions rich in details and subtle transitions, thereby preserving edges and fine structures.4
This local, voxel-by-voxel regularization achieves an optimal balance between image uniformity and the preservation of anatomical details. Moreover, QIR effectively corrects geometric distortions that are often caused by beam dispersion and divergence in cone-beam systems. This improves image sharpness and reduces artifacts, particularly in the presence of metallic devices such as stents and prostheses.3
Key Advantages with the Combination of QIR and Photon-Counting CT Technology
Another advantage of QIR is its ability to exploit the multi-energy data acquired by PCD systems, coherently aligning anatomical structures across different spectral channels to reduce contrast discrepancies and improve diagnostic accuracy.2 In this context, the noise power spectrum (NPS) plays a crucial role. Quantum iterative reconstruction significantly influences the spatial distribution of noise. Specifically, as QIR intensity increases (from QIR 1 to QIR 4), noise is shifted toward lower frequencies, producing more uniform images.3 Moreover, the algorithm avoids oversmoothing by maintaining a Gaussian noise distribution, which preserves the image’s natural texture and ensures visual coherence.3
Photon-counting detector technology, as implemented in cutting-edge systems like NAEOTOM Alpha (Siemens Healthineers), naturally complements QIR by significantly enhancing both image resolution and noise reduction performance. In terms of resolution, these systems can acquire extremely thin slices (down to 0.4 mm), allowing for the detailed visualization of microstructures, a key factor for precise tissue characterization and early diagnosis. In addition, their intrinsic multi-energy capability facilitates advanced spectral imaging, enabling better tissue characterization and effective artifact reduction through more accurate material decomposition.
When it comes to noise reduction, PCDs inherently eliminate the electronic noise typical of EID systems. This excellent low-dose performance not only improves overall image clarity but also increases the robustness of diagnostic information, making it particularly valuable in clinical contexts where minimizing radiation exposure is essential. Furthermore, the synergy between PCD technology and QIR enables full utilization of the spectral data to optimize the reconstruction process. This integrated approach improves the discrimination among different tissue types, further reduces artifacts, and allows for more accurate quantification of tissue properties, ensuring greater image stability even under extremely low-dose conditions and delivering high-quality, reliable diagnostic results.
A Closer Look at the Intensity Levels and Clinical Applications of QIR
Quantum iterative reconstruction is available in four intensity levels. The “QIR-off” mode corresponds to the standard FBP reconstruction, without iterative optimization, while QIR levels 1–4 offer progressively increasing noise reduction. The QIR 1 and QIR 2 levels provide conservative noise suppression, which is ideal for high-dose acquisitions, whereas QIR 3 and QIR 4 offer more aggressive reduction that is optimal for low-dose protocols or scenarios requiring high-detail resolution.5 Additionally, QIR can be applied to all PCD-CT acquisition modes, including UHR mode, thereby further improving its effectiveness in lung imaging.2
There are numerous clinical applications with QIR. In cardiac imaging, the algorithm enables high-resolution visualization of the coronary arteries while maintaining a low radiation doce, an especially beneficial feature for pediatric and high-risk patients. In oncology, QIR improves the characterization of low-contrast lesions and supports reliable multi-energy diagnostics that are essential for managing complex cases. Finally, in pulmonary imaging, QIR contributes to a more precise assessment of chronic lung diseases by reducing artifacts and providing clearer, more detailed images.5
Final Notes
In conclusion, QIR in combination with PCD technology establishes a new standard in CT imaging. The model-based approach overcomes the limitations of traditional reconstruction algorithms, offering optimal noise management, precise artifact correction, and superior preservation of anatomical details. These advances open new perspectives in diagnostic imaging, enabling a significant reduction in radiation dose without compromising image quality.
Mr. Scappatura is a radiology technician at the UOC of Radiology of the Grand Metropolitan Hospital in Reggio Calabria, Italy. He is a member of the multidisciplinary CAR-T therapy team and the multidisciplinary prostate cancer team.
References
1. Sartoretti T, Wildberger J, Flohr T, Alkadhi H. Photon-counting detector CT: early clinical experience review. Br J Radiol. 2023;96(1147):20220544. doi: 10.1259/bjr.20220544.
2. Flohr T, Schmidt B. Technical basics and clinical benefits of photon-counting CT. Invest Radiol. 2023;58(7):441-450.
3. Nehra A, Rajendran K, Baffour F, et al. Seeing more with less: clinical advantages of CT with photon-counting detector. Radiographics. 2023;43(5):e220158. doi: 10.1148/rg.220158.
4. Sartoretti T, Landsmann A, Nakhostin D, et al. Quantum iterative reconstruction for abdominal photon-counting CT improves image quality. Radiology. 2022;303(2):339-348.
5. Woeltjen M, Niehoff J, Michael A, et al. Low-dose high-resolution photon-counting CT of the lung: radiation dose and image quality in the clinical routine. Diagnostics (Basel). 2022;12(6):1441.
Additional References
6. Masturzo L, Barca P, De Masi L, et al. Voxelwise characterization of noise for a clinical photon-counting CT scanner with a model-based iterative reconstruction algorithm. Eur Radiol Exp. 2025;9(1):2. doi: 10.1186/s41747-024-00541-2.
7. Meloni A, Frijia F, Panetta D, et al. Photon-counting computed tomography (PCCT): technical background and cardio-vascular applications. Diagnostics (Basel). 2023;13(4):645.
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