{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T09:10:50Z","timestamp":1789031450708,"version":"build-2803163510"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol Exp"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To evaluate the image quality of a novel deep-learning iterative reconstruction (DLIR) algorithm combined with high-frequency kernels compared with iterative reconstruction (IR) algorithms through the assessment of the detectability of small high-contrast lesions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Three image-quality phantoms were scanned with doses from 0.8 to 15 mGy. Images were reconstructed using ASIR-V0, ASIR-V80, and DLIR (Low, Medium, High), with an edge-enhancing kernel for a chest protocol and a sharp kernel for a spine protocol. A dedicated cubic phantom was used to assess axial, longitudinal resolution, and noise power spectra. Contrast of calcium-based lesions of 3 and 5\u2009mm in diameter was assessed at three concentrations (200, 400, and 800\u2009mg\/cc) using two anthropomorphic chest and abdominal phantoms. Detectability was evaluated using a non-prewhitening with eye filter model observer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In-plane spatial resolution was stable across DLIR strength levels and slightly better than ASIR-V0, while longitudinal resolution did not depend on algorithms. DLIR markedly reduced image noise, below ASIR-V80 for DLIR-High with both kernels. While IR preserved the contrast of lesions, it decreased with DLIR strength for the small lesion 200\u2009mg\/cc-3mm. The highest detectability was achieved with DLIR-High, except for the HA200-3mm below 3 mGy for the chest protocol and 7 mGy for the spine protocol.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>DLIR combined with high-frequency kernels reduced image noise while preserving spatial resolution and FBP-like noise texture, outperforming IR in detectability for most lesion sizes, concentrations, and dose levels. DLIR reduced contrast for small and low-concentration lesions, especially at low doses, which partially counterbalanced its detectability advantage over IR.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>\n                            <jats:bold>\n                              <jats:italic>Question<\/jats:italic>\n                            <\/jats:bold>\n                            This study evaluates whether deep-learning reconstruction with high-frequency kernels improves the detectability of small high-contrast lesions compared to iterative reconstruction.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>\n                            <jats:bold>\n                              <jats:italic>Findings<\/jats:italic>\n                            <\/jats:bold>\n                            The new deep-learning reconstruction reduces image noise while preserving fine anatomical detail and improving in-plane spatial resolution.\n                          <\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:italic>Relevance statement<\/jats:italic>\n                    <\/jats:title>\n                    <jats:p>The new deep-learning reconstruction combined with high-frequency kernels improves detection of small lesions in comparison with iterative reconstruction at standard doses, but very small or low-density lesions may appear less visible at lower dose levels.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-026-00801-3","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T08:45:46Z","timestamp":1789029946000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of deep-learning iterative reconstruction combined with high-frequency kernels in CT: a task-based image quality study"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0039-4244","authenticated-orcid":false,"given":"Ana\u00efs","family":"Viry","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"Monnin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chiara","family":"Pozzessere","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damien","family":"Racine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"801_CR1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-36045-4","volume":"8","author":"DC Rotzinger","year":"2018","unstructured":"Rotzinger DC, Racine D, Beigelman-Aubry C et al (2018) Task-based model observer assessment of a partial model-based iterative reconstruction algorithm in thoracic oncologic multidetector CT. 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