ISIT-GEN: An in silico imaging trial to assess the inter-scanner generalizability of CTLESS for myocardial perfusion SPECT on defect-detection task
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915207679311872 |
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| author | Yu, Zitong Choi, Nu Ri Yang, Zezhang Obuchowski, Nancy A. Siegel, Barry A. Jha, Abhinav K. |
| author_facet | Yu, Zitong Choi, Nu Ri Yang, Zezhang Obuchowski, Nancy A. Siegel, Barry A. Jha, Abhinav K. |
| contents | A recently proposed scatter-window and deep learning-based attenuation compensation (AC) method for myocardial perfusion imaging (MPI) by single-photon emission computed tomography (SPECT), namely CTLESS, demonstrated promising performance on the clinical task of myocardial perfusion defect detection with retrospective data acquired on SPECT scanners from a single vendor. For clinical translation of CTLESS, it is important to assess the generalizability of CTLESS across different SPECT scanners. For this purpose, we conducted a virtual imaging trial, titled in silico imaging trial to assess generalizability (ISIT-GEN). ISIT-GEN assessed the generalizability of CTLESS on the cardiac perfusion defect detection task across SPECT scanners from three different vendors. The performance of CTLESS was compared with a standard-of-care CT-based AC (CTAC) method and a no-attenuation compensation (NAC) method using an anthropomorphic model observer. We observed that CTLESS had receiver operating characteristic (ROC) curves and area under the ROC curves similar to those of CTAC. Further, CTLESS was observed to significantly outperform the NAC method across three scanners. These results are suggestive of the inter-scanner generalizability of CTLESS and motivate further clinical evaluations. The study also highlights the value of using in silico imaging trials to assess the generalizability of deep learning-based AC methods feasibly and rigorously. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16706 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ISIT-GEN: An in silico imaging trial to assess the inter-scanner generalizability of CTLESS for myocardial perfusion SPECT on defect-detection task Yu, Zitong Choi, Nu Ri Yang, Zezhang Obuchowski, Nancy A. Siegel, Barry A. Jha, Abhinav K. Medical Physics A recently proposed scatter-window and deep learning-based attenuation compensation (AC) method for myocardial perfusion imaging (MPI) by single-photon emission computed tomography (SPECT), namely CTLESS, demonstrated promising performance on the clinical task of myocardial perfusion defect detection with retrospective data acquired on SPECT scanners from a single vendor. For clinical translation of CTLESS, it is important to assess the generalizability of CTLESS across different SPECT scanners. For this purpose, we conducted a virtual imaging trial, titled in silico imaging trial to assess generalizability (ISIT-GEN). ISIT-GEN assessed the generalizability of CTLESS on the cardiac perfusion defect detection task across SPECT scanners from three different vendors. The performance of CTLESS was compared with a standard-of-care CT-based AC (CTAC) method and a no-attenuation compensation (NAC) method using an anthropomorphic model observer. We observed that CTLESS had receiver operating characteristic (ROC) curves and area under the ROC curves similar to those of CTAC. Further, CTLESS was observed to significantly outperform the NAC method across three scanners. These results are suggestive of the inter-scanner generalizability of CTLESS and motivate further clinical evaluations. The study also highlights the value of using in silico imaging trials to assess the generalizability of deep learning-based AC methods feasibly and rigorously. |
| title | ISIT-GEN: An in silico imaging trial to assess the inter-scanner generalizability of CTLESS for myocardial perfusion SPECT on defect-detection task |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2503.16706 |