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The objective of this chapter is to provide information about the advantages of Photon-Counting Detectors for Computed Tomography (PCCTs) technology over traditional Energy Integrated Detectors (EIDs). We will discuss the clinical benefits of photon counting detectors and their impact on pre-clinical and clinical practice. Given the unique capability of counting individual photons and discriminating its energy, photon counting detectors have unique benefits over conventional energy integrating detectors, such as reduced electronic noise and radiation dose, increased contrast and contrast-to-noise ratio, reduced beam hardening and metal artifacts, and simultaneous high-resolution and multi-energy imaging. These benefits have a broad impact on clinical CT exams in terms of image quality and radiation dose. Certain benefits, such as the high resolution, will benefit specific exams and clinical areas more than the others. In addition to the improvement to the current clinical practice, there are also substantial opportunities for PCCT to enable new clinical applications, such as nanoparticles and multi-contrast imaging, that we will discuss at the end of this chapter. We will conclude discussing advances in the field of artificial intelligence (AI) and its impact on PCCT technology and clinical usage. This AI section offers a broad overview of the current research landscape where artificial intelligence techniques intersect with photon-counting computed tomography (PCCT), highlighting how AI techniques are being harnessed to elevate the capabilities of PCCT. AI has great potential to improve PCCT image quality, contrast resolution, and diagnostic accuracy, yet it also faces unique challenges. Given the rapid pace of development and the breadth of ongoing research, this review recognizes its limitations in encapsulating the full spectrum of possibilities at the intersection of AI and PCCT. By providing a snapshot of current trends, challenges, and potential innovations, we aim to illuminate the path forward for researchers and clinicians in the PCCT field.

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Photon-Counting Computed Tomography: Clinical Applications, Detector Technology, and Artificial Intelligence

  • 章节

  • 发表日期：

    2025年

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  • Devon Richtsmeier,
  • Xinchen Deng,
  • Pierre-Antoine Rodesch,
  • Joanna Nguyen,
  • James Day,
  • Krzysztof Kris Iniewski,
  • William Siu,
  • Magdalena Bazalova-Carter

摘要

The objective of this chapter is to provide information about the advantages of Photon-Counting Detectors for Computed Tomography (PCCTs) technology over traditional Energy Integrated Detectors (EIDs). We will discuss the clinical benefits of photon counting detectors and their impact on pre-clinical and clinical practice. Given the unique capability of counting individual photons and discriminating its energy, photon counting detectors have unique benefits over conventional energy integrating detectors, such as reduced electronic noise and radiation dose, increased contrast and contrast-to-noise ratio, reduced beam hardening and metal artifacts, and simultaneous high-resolution and multi-energy imaging. These benefits have a broad impact on clinical CT exams in terms of image quality and radiation dose. Certain benefits, such as the high resolution, will benefit specific exams and clinical areas more than the others. In addition to the improvement to the current clinical practice, there are also substantial opportunities for PCCT to enable new clinical applications, such as nanoparticles and multi-contrast imaging, that we will discuss at the end of this chapter. We will conclude discussing advances in the field of artificial intelligence (AI) and its impact on PCCT technology and clinical usage. This AI section offers a broad overview of the current research landscape where artificial intelligence techniques intersect with photon-counting computed tomography (PCCT), highlighting how AI techniques are being harnessed to elevate the capabilities of PCCT. AI has great potential to improve PCCT image quality, contrast resolution, and diagnostic accuracy, yet it also faces unique challenges. Given the rapid pace of development and the breadth of ongoing research, this review recognizes its limitations in encapsulating the full spectrum of possibilities at the intersection of AI and PCCT. By providing a snapshot of current trends, challenges, and potential innovations, we aim to illuminate the path forward for researchers and clinicians in the PCCT field.