{"title":"Quantifying photon counting detector (PCD) performance using PCD-CT images.","authors":"Linying Zhan, Guang-Hong Chen, Ke Li","doi":"10.1002/mp.17701","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Photon counting detector CTs (PCD-CTs) have recently been introduced to clinical imaging. This development creates a new need for end-users to quantify and monitor the physical performance of PCDs. Traditionally, the characterization of PCD performance relied on detector counts, which are typically accessible to the manufacturer but are not usually available to clinical end-users.</p><p><strong>Purpose: </strong>The goal of this work was to develop a new method for quantifying PCD performance using reconstructed PCD-CT images, without requiring access to the PCD counts.</p><p><strong>Methods: </strong>The proposed method is based on a set of closed-form relationships that connect PCD-CT image noise, the PCD deadtime ( <math><semantics><mi>τ</mi> <annotation>$\\tau$</annotation></semantics> </math> ), and the zero-frequency detective quantum efficiency ( <math> <semantics><msub><mi>DQE</mi> <mn>0</mn></msub> <annotation>${\\rm DQE}_0$</annotation></semantics> </math> ) of PCDs. At a low tube current (mA) level, the mean output counts of the PCD were estimated by fitting the measured PCD-CT noise power spectrum (NPS) to a parametric model. <math> <semantics><msub><mi>DQE</mi> <mn>0</mn></msub> <annotation>${\\rm DQE}_0$</annotation></semantics> </math> was then calculated by normalizing the estimated mean detector counts to the expected input x-ray photon number. To estimate <math><semantics><mi>τ</mi> <annotation>$\\tau$</annotation></semantics> </math> , the image variance of PCD-CT was measured at different mA levels. A novel quantitative relationship between PCD-CT image variance, <math><semantics><mi>τ</mi> <annotation>$\\tau$</annotation></semantics> </math> , and mA was employed to estimate <math><semantics><mi>τ</mi> <annotation>$\\tau$</annotation></semantics> </math> through parametric fitting. The method was validated using both simulated and experimental PCD-CT data, covering a range of <math><semantics><mi>τ</mi> <annotation>$\\tau$</annotation></semantics> </math> , <math> <semantics><msub><mi>DQE</mi> <mn>0</mn></msub> <annotation>${\\rm DQE}_0$</annotation></semantics> </math> , and system geometries.</p><p><strong>Results: </strong>For the simulated curved-detector PCD-CT, the estimation errors for <math> <semantics><msub><mi>DQE</mi> <mn>0</mn></msub> <annotation>${\\rm DQE}_0$</annotation></semantics> </math> and deadtime were -3.7% and 0.5%, respectively. For the simulated collinear-detector PCD-CT, the estimation errors for <math> <semantics><msub><mi>DQE</mi> <mn>0</mn></msub> <annotation>${\\rm DQE}_0$</annotation></semantics> </math> and deadtime were -3.3% and -1.0%, respectively. For the experimental collinear-detector PCD-CT, the estimation errors for <math> <semantics><msub><mi>DQE</mi> <mn>0</mn></msub> <annotation>${\\rm DQE}_0$</annotation></semantics> </math> and deadtime were -2.6% and 1.6%, respectively.</p><p><strong>Conclusions: </strong>By analyzing the variance and NPS of PCD-CT images, <math> <semantics><msub><mi>DQE</mi> <mn>0</mn></msub> <annotation>${\\rm DQE}_0$</annotation></semantics> </math> and deadtime of scanner's PCD can be accurately estimated, without access to raw detector counts or projection data.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/mp.17701","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Photon counting detector CTs (PCD-CTs) have recently been introduced to clinical imaging. This development creates a new need for end-users to quantify and monitor the physical performance of PCDs. Traditionally, the characterization of PCD performance relied on detector counts, which are typically accessible to the manufacturer but are not usually available to clinical end-users.
Purpose: The goal of this work was to develop a new method for quantifying PCD performance using reconstructed PCD-CT images, without requiring access to the PCD counts.
Methods: The proposed method is based on a set of closed-form relationships that connect PCD-CT image noise, the PCD deadtime ( ), and the zero-frequency detective quantum efficiency ( ) of PCDs. At a low tube current (mA) level, the mean output counts of the PCD were estimated by fitting the measured PCD-CT noise power spectrum (NPS) to a parametric model. was then calculated by normalizing the estimated mean detector counts to the expected input x-ray photon number. To estimate , the image variance of PCD-CT was measured at different mA levels. A novel quantitative relationship between PCD-CT image variance, , and mA was employed to estimate through parametric fitting. The method was validated using both simulated and experimental PCD-CT data, covering a range of , , and system geometries.
Results: For the simulated curved-detector PCD-CT, the estimation errors for and deadtime were -3.7% and 0.5%, respectively. For the simulated collinear-detector PCD-CT, the estimation errors for and deadtime were -3.3% and -1.0%, respectively. For the experimental collinear-detector PCD-CT, the estimation errors for and deadtime were -2.6% and 1.6%, respectively.
Conclusions: By analyzing the variance and NPS of PCD-CT images, and deadtime of scanner's PCD can be accurately estimated, without access to raw detector counts or projection data.