{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:34:00Z","timestamp":1762342440756,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:00:00Z","timestamp":1735862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Research Foundation","award":["DFG 467843609","FOR5628"],"award-info":[{"award-number":["DFG 467843609","FOR5628"]}]},{"name":"Siemens Healthcare (Erlangen, Bavara, Germany)","award":["DFG 467843609","FOR5628"],"award-info":[{"award-number":["DFG 467843609","FOR5628"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Current Oncology"],"abstract":"<jats:p>Purpose: Breath-hold T2-weighted half-Fourier acquisition single-shot turbo spin echo (HASTE) magnetic resonance imaging (MRI) of the upper abdomen with a slice thickness below 5 mm suffers from high image noise and blurring. The purpose of this prospective study was to improve image quality and accelerate imaging acquisition by using single-breath-hold T2-weighted HASTE with deep learning (DL) reconstruction (DL-HASTE) with a 3 mm slice thickness. Method: MRI of the upper abdomen with DL-HASTE was performed in 35 participants (5 healthy volunteers and 30 patients) at 3 Tesla. In a subgroup of five healthy participants, signal-to-noise ratio (SNR) analysis was used after DL reconstruction to identify the smallest possible layer thickness (1, 2, 3, 4, 5 mm). DL-HASTE was acquired with a 3 mm slice thickness (DL-HASTE-3 mm) in 30 patients and compared with 5 mm DL-HASTE (DL-HASTE-5 mm) and with standard HASTE (standard-HASTE-5 mm). Image quality and motion artifacts were assessed quantitatively using Laplacian variance and semi-quantitatively by two radiologists using five-point Likert scales. Results: In the five healthy participants, DL-HASTE-3 mm was identified as the optimal slice (SNR 23.227 \u00b1 3.901). Both DL-HASTE-3 mm and DL-HASTE-5 mm were assigned significantly higher overall image quality scores than standard-HASTE-5 mm (Laplacian variance, both p &lt; 0.001; Likert scale, p &lt; 0.001). Compared with DL-HASTE-5 mm (1.10 \u00d7 10\u22125 \u00b1 6.93 \u00d7 10\u22126), DL-HASTE-3 mm (1.56 \u00d7 10\u22125 \u00b1 8.69 \u00d7 10\u22126) provided a significantly higher SNR Laplacian variance (p &lt; 0.001) and sharpness sub-scores for the intestinal tract, adrenal glands, and small anatomic structures (bile ducts, pancreatic ducts, and vessels; p &lt; 0.05). Lesion detectability was rated excellent for both DL-HASTE-3 mm and DL-HASTE-5 mm (both: 5 [IQR4\u20135]) and was assigned higher scores than standard-HASTE-5 mm (4 [IQR4\u20135]; p &lt; 0.001). DL-HASTE reduced the acquisition time by 63\u201369% compared with standard-HASTE-5 mm (p &lt; 0.001). Conclusions: DL-HASTE is a robust abdominal MRI technique that improves image quality while at the same time reducing acquisition time compared with the routine clinical HASTE sequence. Using ultra-thin DL-HASTE-3 mm results in an even greater improvement with a similar SNR.<\/jats:p>","DOI":"10.3390\/curroncol32010030","type":"journal-article","created":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T07:10:17Z","timestamp":1735888217000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep-Learning-Based Reconstruction of Single-Breath-Hold 3 mm HASTE Improves Abdominal Image Quality and Reduces Acquisition Time: A Quantitative Analysis"],"prefix":"10.3390","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9724-2887","authenticated-orcid":false,"given":"Felix","family":"Kubicka","sequence":"first","affiliation":[{"name":"Department of Radiology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t zu Berlin and Berlin Institute of Health, Charit\u00e9platz 1, 10117 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinxuan","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Radiology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t zu Berlin and Berlin Institute of Health, Charit\u00e9platz 1, 10117 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2171-6791","authenticated-orcid":false,"given":"Tom","family":"Meyer","sequence":"additional","affiliation":[{"name":"Department of Radiology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t zu Berlin and Berlin Institute of Health, Charit\u00e9platz 1, 10117 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0360-7233","authenticated-orcid":false,"given":"Dominik","family":"Nickel","sequence":"additional","affiliation":[{"name":"MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7517-1291","authenticated-orcid":false,"given":"Elisabeth","family":"Weiland","sequence":"additional","affiliation":[{"name":"MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052 Erlangen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5078-0725","authenticated-orcid":false,"given":"Moritz","family":"Wagner","sequence":"additional","affiliation":[{"name":"Department of Radiology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t zu Berlin and Berlin Institute of Health, Charit\u00e9platz 1, 10117 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3782-3003","authenticated-orcid":false,"given":"Stephan Rodrigo","family":"Marticorena Garcia","sequence":"additional","affiliation":[{"name":"Department of Radiology, Charit\u00e9\u2014Universit\u00e4tsmedizin Berlin, Freie Universit\u00e4t Berlin, Humboldt-Universit\u00e4t zu Berlin and Berlin Institute of Health, Charit\u00e9platz 1, 10117 Berlin, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1002\/jmri.1880060420","article-title":"HASTE MR imaging: Description of technique and preliminary results in the abdomen","volume":"6","author":"Semelka","year":"1996","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1148\/radiology.160.3.3737921","article-title":"Comparison of respiratory triggering and gating techniques for the removal of respiratory artifacts in MR imaging","volume":"160","author":"Lewis","year":"1986","journal-title":"Radiology"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1002\/jmri.21855","article-title":"Usefulness of the application of the BLADE technique to reduce motion artifacts on navigation-triggered prospective acquisition correction (PACE) T2-weighted MRI (T2WI) of the liver","volume":"30","author":"Nanko","year":"2009","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1002\/mrm.10171","article-title":"Generalized autocalibrating partially parallel acquisitions (GRAPPA)","volume":"47","author":"Griswold","year":"2002","journal-title":"Magn. Reson. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s002619910019","article-title":"Fast T2-weighted liver MR imaging: Comparison among breath-hold turbo-spin-echo, HASTE, and inversion recovery (IR) HASTE sequences","volume":"25","author":"Lee","year":"2000","journal-title":"Abdom. Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1097\/00004728-200101000-00010","article-title":"Fast breath-hold T2-weighted MRI of the kidney by means of half-Fourier single-shot turbo spin echo: Comparison with high resolution turbo spin echo sequence","volume":"25","author":"Nakayama","year":"2001","journal-title":"J. Comput. Assist. Tomogr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.mric.2020.03.003","article-title":"Advanced MR Imaging of the Pancreas","volume":"28","author":"Hill","year":"2020","journal-title":"Magn. Reson. Imaging Clin. N. Am."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s13244-011-0129-9","article-title":"Magnetic resonance cholangiopancreatography: The ABC of MRCP","volume":"3","author":"Griffin","year":"2012","journal-title":"Insights Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1148\/radiol.2018180445","article-title":"Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks","volume":"289","author":"Chen","year":"2018","journal-title":"Radiology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3378","DOI":"10.1007\/s00261-021-02964-6","article-title":"Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging","volume":"46","author":"Wang","year":"2021","journal-title":"Abdom. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1148\/radiol.2020200723","article-title":"Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting","volume":"298","author":"Kim","year":"2021","journal-title":"Radiology"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lee, D.H., Park, J.E., Nam, Y.K., Lee, J., Kim, S., Kim, Y.H., and Kim, H.S. (2021). Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-00558-2"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.mri.2021.05.001","article-title":"Feasibility of high-resolution magnetic resonance imaging of the liver using deep learning reconstruction based on the deep learning denoising technique","volume":"80","author":"Tanabe","year":"2021","journal-title":"Magn. Reson. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1186\/s13244-021-01060-z","article-title":"Cystic pancreatic lesions: MR imaging findings and management","volume":"12","author":"Morana","year":"2021","journal-title":"Insights Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1097\/RLI.0000000000000743","article-title":"Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold","volume":"56","author":"Herrmann","year":"2021","journal-title":"Investig. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.acra.2022.03.018","article-title":"Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T","volume":"30","author":"Herrmann","year":"2023","journal-title":"Acad. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.diii.2022.05.001","article-title":"Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence","volume":"103","author":"Kharrat","year":"2022","journal-title":"Diagn. Interv. Imaging."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"8447","DOI":"10.1007\/s00330-021-08008-3","article-title":"Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: Qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence","volume":"31","author":"Shanbhogue","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_19","first-page":"100038","article-title":"Feasibility of deep learning-reconstructed thin-slice single-breath-hold HASTE for detecting pancreatic lesions: A comparison with two conventional T2-weighted imaging sequences","volume":"9","author":"Liu","year":"2024","journal-title":"Res. Diagn. Interv. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gassenmaier, S., Warm, V., Nickel, D., Weiland, E., Herrmann, J., Almansour, H., Wessling, D., and Afat, S. (2023). Thin-Slice Prostate MRI Enabled by Deep Learning Image Reconstruction. Cancers, 15.","DOI":"10.3390\/cancers15030578"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.mri.2022.04.005","article-title":"Clinical feasibility of an abdominal thin-slice breath-hold single-shot fast spin echo sequence processed using a deep learning-based noise-reduction approach","volume":"90","author":"Tajima","year":"2022","journal-title":"Magn. Reson. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1097\/RLI.0000000000000785","article-title":"Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles","volume":"56","author":"Herrmann","year":"2021","journal-title":"Investig. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","article-title":"elastix: A toolbox for intensity-based medical image registration","volume":"29","author":"Klein","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pech-Pacheco, J.L., Cristobal, G., Chamorro-Martinez, J., and Fernandez-Valdivia, J. (2000, January 3\u20137). Diatom autofocusing in brightfield microscopy: A comparative study. Proceedings of the 15th International Conference on Pattern Recognition. ICPR-2000, Barcelona, Spain.","DOI":"10.1109\/ICPR.2000.903548"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1002\/mrm.28558","article-title":"Reduction of breathing artifacts in multifrequency magnetic resonance elastography of the abdomen","volume":"85","author":"Shahryari","year":"2021","journal-title":"Magn. Reson. Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.mri.2021.06.014","article-title":"Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI","volume":"81","author":"Sheng","year":"2021","journal-title":"Magn. Reson. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1148\/radiol.2019182646","article-title":"Bosniak Classification of Cystic Renal Masses, Version 2019: An Update Proposal and Needs Assessment","volume":"292","author":"Silverman","year":"2019","journal-title":"Radiology."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1177\/0284185114529562","article-title":"Bosniak classification system: Inter-observer and intra-observer agreement among experienced uroradiologists","volume":"56","author":"Graumann","year":"2015","journal-title":"Acta Radiol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.acuro.2019.11.007","article-title":"Comparison of computed tomography and magnetic resonance imaging in the assessment of complex renal cysts by using the Bosniak classification","volume":"44","author":"Yenice","year":"2020","journal-title":"Actas Urol. Esp."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111737","DOI":"10.1016\/j.ejrad.2024.111737","article-title":"Clinical feasibility of deep learning-accelerated single-shot turbo spin echo sequence with enhanced denoising for pancreas MRI at 3 Tesla","volume":"181","author":"Kim","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e212922","DOI":"10.1148\/radiol.212922","article-title":"Deep learning reconstruction for accelerated spine MRI: Prospective analysis of interchangeability","volume":"306","author":"Almansour","year":"2023","journal-title":"Radiology"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"30088","DOI":"10.1073\/pnas.1907377117","article-title":"On instabilities of deep learning in image reconstruction and the potential costs of, A.I","volume":"117","author":"Antun","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1067\/j.cpradiol.2021.06.006","article-title":"Deep Learning Network for Segmentation of the Prostate Gland With Median Lobe Enlargement in T2-weighted MR Images: Comparison With Manual Segmentation Method","volume":"51","author":"Salvaggio","year":"2022","journal-title":"Curr. Probl. Diagn. Radiol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Mazzeo, P.L., Frontoni, E., Sclaroff, S., and Distante, C. (2022). Robustness of Radiomics Features to Varying Segmentation Algorithms in Magnetic Resonance Images. Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-13324-4"}],"container-title":["Current Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1718-7729\/32\/1\/30\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:22:30Z","timestamp":1759918950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1718-7729\/32\/1\/30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,3]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["curroncol32010030"],"URL":"https:\/\/doi.org\/10.3390\/curroncol32010030","relation":{},"ISSN":["1718-7729"],"issn-type":[{"type":"electronic","value":"1718-7729"}],"subject":[],"published":{"date-parts":[[2025,1,3]]}}}