Giavanna Jadick, Maya Ventura, Patrick J La Rivière
{"title":"光子计数探测器在MV-kV双能计算机断层成像中的应用。","authors":"Giavanna Jadick, Maya Ventura, Patrick J La Rivière","doi":"10.1117/1.JMI.11.S1.S12811","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>High soft-tissue contrast imaging is essential for effective radiotherapy treatment. This could potentially be realized using both megavoltage and kilovoltage x-ray sources available on some therapy treatment systems to perform \"MV-kV\" dual-energy (DE) computed tomography (CT). However, noisy megavoltage images obtained with existing energy-integrating detectors (EIDs) are a limiting factor for clinical translation. We explore the potential for non-spectral photon-counting detectors (PCDs) to improve MV-kV image quality simply by equally weighting all MV photons rather than up-weighting the less informative, lower contrast high-energy photons as in an EID.</p><p><strong>Approach: </strong>Three computational methods were applied to compare non-spectral PCDs with EIDs in MV-kV DE imaging. A single-line integral estimation theory approach was used to calculate the basis material signal-to-noise ratio (SNR) of tissue (1 to 50 cm) and bone (0.1 to 10 cm). CT images of a tissue cylinder with seven bone inserts (densities 1.0 to <math><mrow><mn>2.2</mn> <mtext> </mtext> <mi>g</mi> <mo>/</mo> <msup><mrow><mi>cm</mi></mrow> <mrow><mn>3</mn></mrow> </msup> </mrow> </math> ) were simulated to assess material decomposition accuracy. Multiple noisy simulations of an anthropomorphic phantom were performed to generate pixel-by-pixel noise profiles.</p><p><strong>Results: </strong>PCDs yielded a 15% to 45% improvement in single-line integral SNR for both materials. In CT simulations, similar material decomposition accuracy was achieved, with both EIDs and PCDs slightly underestimating bone density. However, PCDs yield a higher contrast-to-noise ratio and more uniform noise texture than EIDs in virtual monoenergetic images.</p><p><strong>Conclusions: </strong>We demonstrate the potential for improved MV-kV DE CT imaging using non-spectral PCDs and quantify the degree of improvement in a range of object compositions. This work motivates the experimental assessment of PCDs for megavoltage imaging and the potential clinical translation of PCDs to radiotherapy imaging.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"11 Suppl 1","pages":"S12811"},"PeriodicalIF":1.9000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11670364/pdf/","citationCount":"0","resultStr":"{\"title\":\"Utility of photon-counting detectors for MV-kV dual-energy computed tomography imaging.\",\"authors\":\"Giavanna Jadick, Maya Ventura, Patrick J La Rivière\",\"doi\":\"10.1117/1.JMI.11.S1.S12811\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>High soft-tissue contrast imaging is essential for effective radiotherapy treatment. This could potentially be realized using both megavoltage and kilovoltage x-ray sources available on some therapy treatment systems to perform \\\"MV-kV\\\" dual-energy (DE) computed tomography (CT). However, noisy megavoltage images obtained with existing energy-integrating detectors (EIDs) are a limiting factor for clinical translation. We explore the potential for non-spectral photon-counting detectors (PCDs) to improve MV-kV image quality simply by equally weighting all MV photons rather than up-weighting the less informative, lower contrast high-energy photons as in an EID.</p><p><strong>Approach: </strong>Three computational methods were applied to compare non-spectral PCDs with EIDs in MV-kV DE imaging. A single-line integral estimation theory approach was used to calculate the basis material signal-to-noise ratio (SNR) of tissue (1 to 50 cm) and bone (0.1 to 10 cm). CT images of a tissue cylinder with seven bone inserts (densities 1.0 to <math><mrow><mn>2.2</mn> <mtext> </mtext> <mi>g</mi> <mo>/</mo> <msup><mrow><mi>cm</mi></mrow> <mrow><mn>3</mn></mrow> </msup> </mrow> </math> ) were simulated to assess material decomposition accuracy. Multiple noisy simulations of an anthropomorphic phantom were performed to generate pixel-by-pixel noise profiles.</p><p><strong>Results: </strong>PCDs yielded a 15% to 45% improvement in single-line integral SNR for both materials. In CT simulations, similar material decomposition accuracy was achieved, with both EIDs and PCDs slightly underestimating bone density. However, PCDs yield a higher contrast-to-noise ratio and more uniform noise texture than EIDs in virtual monoenergetic images.</p><p><strong>Conclusions: </strong>We demonstrate the potential for improved MV-kV DE CT imaging using non-spectral PCDs and quantify the degree of improvement in a range of object compositions. 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引用次数: 0
摘要
目的:软组织高对比度成像对有效的放射治疗至关重要。这可以通过使用某些治疗系统上可用的兆电压和千伏x射线源来执行“MV-kV”双能(DE)计算机断层扫描(CT)来实现。然而,现有能量积分检测器(eid)获得的噪声巨电压图像是临床翻译的限制因素。我们探索了非光谱光子计数探测器(PCDs)的潜力,通过对所有MV光子进行均等加权来改善MV- kv图像质量,而不是像在EID中那样对信息较少、对比度较低的高能光子进行加权。方法:采用三种计算方法对MV-kV DE成像中的非光谱PCDs与EIDs进行比较。采用单线积分估计理论计算组织(1 ~ 50 cm)和骨骼(0.1 ~ 10 cm)的基材信噪比(SNR)。模拟含有7个骨插入物(密度1.0 - 2.2 g / cm3)的组织圆柱体的CT图像,以评估材料分解的准确性。对拟人化幻影进行多重噪声模拟,生成逐像素噪声剖面。结果:两种材料的PCDs单线积分信噪比提高了15%至45%。在CT模拟中,实现了相似的材料分解精度,EIDs和PCDs都略微低估了骨密度。然而,在虚拟单能图像中,PCDs比eid产生更高的噪比和更均匀的噪声纹理。结论:我们展示了使用非光谱PCDs改善MV-kV DE CT成像的潜力,并量化了一系列物体成分的改善程度。这项工作激发了对PCDs进行巨压成像的实验评估,以及将PCDs转化为放射治疗成像的潜在临床应用。
Utility of photon-counting detectors for MV-kV dual-energy computed tomography imaging.
Purpose: High soft-tissue contrast imaging is essential for effective radiotherapy treatment. This could potentially be realized using both megavoltage and kilovoltage x-ray sources available on some therapy treatment systems to perform "MV-kV" dual-energy (DE) computed tomography (CT). However, noisy megavoltage images obtained with existing energy-integrating detectors (EIDs) are a limiting factor for clinical translation. We explore the potential for non-spectral photon-counting detectors (PCDs) to improve MV-kV image quality simply by equally weighting all MV photons rather than up-weighting the less informative, lower contrast high-energy photons as in an EID.
Approach: Three computational methods were applied to compare non-spectral PCDs with EIDs in MV-kV DE imaging. A single-line integral estimation theory approach was used to calculate the basis material signal-to-noise ratio (SNR) of tissue (1 to 50 cm) and bone (0.1 to 10 cm). CT images of a tissue cylinder with seven bone inserts (densities 1.0 to ) were simulated to assess material decomposition accuracy. Multiple noisy simulations of an anthropomorphic phantom were performed to generate pixel-by-pixel noise profiles.
Results: PCDs yielded a 15% to 45% improvement in single-line integral SNR for both materials. In CT simulations, similar material decomposition accuracy was achieved, with both EIDs and PCDs slightly underestimating bone density. However, PCDs yield a higher contrast-to-noise ratio and more uniform noise texture than EIDs in virtual monoenergetic images.
Conclusions: We demonstrate the potential for improved MV-kV DE CT imaging using non-spectral PCDs and quantify the degree of improvement in a range of object compositions. This work motivates the experimental assessment of PCDs for megavoltage imaging and the potential clinical translation of PCDs to radiotherapy imaging.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.