{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:47:02Z","timestamp":1760060822567,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["75N91021C00036","U01CA211205","R01CA190299"],"award-info":[{"award-number":["75N91021C00036","U01CA211205","R01CA190299"]}]},{"name":"University of Michigan Basic Radiologic Sciences","award":["75N91021C00036","U01CA211205","R01CA190299"],"award-info":[{"award-number":["75N91021C00036","U01CA211205","R01CA190299"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Clinical application of AI\/DL-aided acquisitions for quantitative bi-parametric (q-bp)MRI requires validation and harmonization across vendor platforms. An AI\/DL-accelerated q-bpMRI, including 5-echo T2 and 4-b-value apparent diffusion coefficient (ADC) mapping, was implemented on two 3T clinical scanners by two vendors alongside the qualitative standard-of-care (SOC) MRI protocols for six patients with biopsy-confirmed prostate cancer (PCa). AI\/DL versus SOC bpMRI image quality was compared for MR-visible PCa lesions on a 4-point Likert-like scale. Quantitative validation and protocol bias assessment were performed using a multiparametric phantom with reference T2 and diffusion kurtosis values mimicking prostate tissue ranges. Six-minute q-bpMRI achieved acceptable diagnostic quality comparable to the SOC. Better SNR was observed for DL\/AI versus SOC ADC with method-dependent distortion susceptibility and resolution enhancement. The measured biases were unaffected by AI\/DL reconstruction and related to acquisition protocol parameters: constant for spin-echo T2 (\u22127 ms to +5 ms) and ADC (4b-fit: \u22120.37 \u00b5m2\/ms and 2b-fit: \u22120.19 \u00b5m2\/ms), while nonlinear for echo-planar T2 (\u221237 ms to +14 ms). Measured phantom ADC bias dependence on b-value range was consistent with that observed for PCa lesions. Bias correction harmonized lesion T2 and ADC values across different AI\/DL-aided q-bpMRI acquisitions. The developed workflow enables harmonization of AI\/DL-accelerated quantitative T2 and ADC mapping in multi-vendor clinical settings.<\/jats:p>","DOI":"10.3390\/s25185858","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T12:33:32Z","timestamp":1758285212000},"page":"5858","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Cross-Scanner Harmonization of AI\/DL Accelerated Quantitative Bi-Parametric Prostate MRI"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0403-1501","authenticated-orcid":false,"given":"Dariya","family":"Malyarenko","sequence":"first","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4714-4031","authenticated-orcid":false,"given":"Scott D.","family":"Swanson","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob","family":"Richardson","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suzan","family":"Lowe","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"O\u2019Connor","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0713-9641","authenticated-orcid":false,"given":"Yun","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reve","family":"Chahine","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shane A.","family":"Wells","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"},{"name":"Department of Urology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas L.","family":"Chenevert","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ferro, M., Crocetto, F., Bruzzese, D., Imbriaco, M., Fusco, F., Longo, N., Napolitano, L., La Civita, E., Cennamo, M., and Liotti, A. (2021). Prostate Health Index and Multiparametric MRI: Partners in Crime Fighting Overdiagnosis and Overtreatment in Prostate Cancer. Cancers, 13.","DOI":"10.3390\/cancers13184723"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2214\/AJR.20.24268","article-title":"PI-RADS Committee Position on MRI Without Contrast Medium in Biopsy-Naive Men With Suspected Prostate Cancer: Narrative Review","volume":"216","author":"Schoots","year":"2021","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1016\/j.jacr.2018.11.031","article-title":"Temporary Health Impact of Prostate MRI and Transrectal Prostate Biopsy in Active Surveillance Prostate Cancer Patients","volume":"16","author":"Shankar","year":"2019","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1148\/rg.2016150234","article-title":"PI-RADS Version 2: A Pictorial Update","volume":"36","author":"Purysko","year":"2016","journal-title":"Radiographics"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"847","DOI":"10.2214\/AJR.18.20571","article-title":"A Systematic Review of the Existing Prostate Imaging Reporting and Data System Version 2 (PI-RADSv2) Literature and Subset Meta-Analysis of PI-RADSv2 Categories Stratified by Gleason Scores","volume":"212","author":"Barkovich","year":"2019","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1148\/radiol.212692","article-title":"Contribution of Dynamic Contrast-enhanced and Diffusion MRI to PI-RADS for Detecting Clinically Significant Prostate Cancer","volume":"306","author":"Tavakoli","year":"2023","journal-title":"Radiology"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"W439","DOI":"10.2214\/AJR.14.13373","article-title":"Dynamic contrast-enhanced MRI for the detection of prostate cancer: Meta-analysis","volume":"204","author":"Tan","year":"2015","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Meyer, H.J., Wienke, A., and Surov, A. (2020). Discrimination between clinical significant and insignificant prostate cancer with apparent diffusion coefficient\u2014A systematic review and meta analysis. BMC Cancer, 20.","DOI":"10.1186\/s12885-020-06942-x"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s00345-019-02827-2","article-title":"Effects of the addition of quantitative apparent diffusion coefficient data on the diagnostic performance of the PI-RADS v2 scoring system to detect clinically significant prostate cancer","volume":"38","author":"Moraes","year":"2020","journal-title":"World J. Urol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20180091","DOI":"10.1259\/bjr.20180091","article-title":"Utility of quantitative apparent diffusion coefficient measurements and normalized apparent diffusion coefficient ratios in the diagnosis of clinically significant peripheral zone prostate cancer","volume":"91","author":"Nguyen","year":"2018","journal-title":"Br. J. Radiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/j.diii.2014.11.016","article-title":"Characterization of prostate cancer using T2 mapping at 3T: A multi-scanner study","volume":"96","author":"Souchon","year":"2015","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"910","DOI":"10.1002\/jmri.26608","article-title":"Simplified Luminal Water Imaging for the Detection of Prostate Cancer From Multiecho T(2) MR Images","volume":"50","author":"Devine","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1097\/RLI.0000000000000569","article-title":"Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping","volume":"54","author":"Panda","year":"2019","journal-title":"Investig. Radiol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e2431715","DOI":"10.2214\/AJR.24.31715","article-title":"Quantitative Prostate MRI, From the AJR Special Series on Quantitative Imaging","volume":"225","author":"Margolis","year":"2024","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1148\/radiol.2021204459","article-title":"Validation of Prostate Tissue Composition by Using Hybrid Multidimensional MRI: Correlation with Histologic Findings","volume":"302","author":"Chatterjee","year":"2022","journal-title":"Radiology"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1148\/radiol.2017170904","article-title":"Advanced Diffusion-weighted Imaging Modeling for Prostate Cancer Characterization: Correlation with Quantitative Histopathologic Tumor Tissue Composition-A Hypothesis-generating Study","volume":"286","author":"Hectors","year":"2018","journal-title":"Radiology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1097\/RLI.0000000000000115","article-title":"Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging","volume":"50","author":"Panagiotaki","year":"2015","journal-title":"Investig. Radiol."},{"key":"ref_18","first-page":"011004","article-title":"Optimized b-value selection for the discrimination of prostate cancer grades, including the cribriform pattern, using diffusion weighted imaging","volume":"5","author":"Hurrell","year":"2018","journal-title":"J. Med. Imaging"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"20120469","DOI":"10.1259\/bjr.20120469","article-title":"Relationship between T2 relaxation and apparent diffusion coefficient in malignant and non-malignant prostate regions and the effect of peripheral zone fractional volume","volume":"86","author":"Simpkin","year":"2013","journal-title":"Br. J. Radiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109600","DOI":"10.1016\/j.ejrad.2021.109600","article-title":"Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality","volume":"137","author":"Gassenmaier","year":"2021","journal-title":"Eur. J. Radiol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1148\/radiol.204097","article-title":"Deep Learning Reconstruction of Diffusion-weighted MRI Improves Image Quality for Prostatic Imaging","volume":"303","author":"Ueda","year":"2022","journal-title":"Radiology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7364","DOI":"10.1007\/s00330-024-10771-y","article-title":"Assessing deep learning reconstruction for faster prostate MRI: Visual vs. diagnostic performance metrics","volume":"34","author":"Roest","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1002\/jmri.27078","article-title":"Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians","volume":"53","author":"Lin","year":"2021","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.jacr.2023.10.021","article-title":"Best Practices for Artificial Intelligence and Machine Learning for Computer-Aided Diagnosis in Medical Imaging","volume":"21","author":"Vergara","year":"2024","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.acra.2022.08.031","article-title":"A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs)","volume":"30","author":"Obuchowski","year":"2023","journal-title":"Acad. Radiol."},{"key":"ref_26","unstructured":"US FDA Center for Devices and Radiological Health (2025, August 03). Technical Performance Assessment of Quantitative Imaging in Radiological Device Premarket Submissions, Available online: https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/technical-performance-assessment-quantitative-imaging-radiological-device-premarket-submissions."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e233055","DOI":"10.1148\/radiol.233055","article-title":"The QIBA Profile for Diffusion-Weighted MRI: Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker","volume":"313","author":"Boss","year":"2024","journal-title":"Radiology"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3590","DOI":"10.1002\/mp.16908","article-title":"Technical note: Hydrogel-based mimics of prostate cancer with matched relaxation, diffusion and kurtosis for validating multi-parametric MRI","volume":"51","author":"Malyarenko","year":"2024","journal-title":"Med. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1002\/mrm.26550","article-title":"ADC Quantification of the Vertebral Bone Marrow Water Component: Removing the Confounding Effect of Residual Fat","volume":"78","author":"Dieckmeyer","year":"2017","journal-title":"Magn. Reson. Med."},{"key":"ref_30","unstructured":"Malyarenko, D., Swanson, S.D., Richardson, J., Lowe, S., O\u2019Connor, J., Fajardo, J.E., Jiang, Y., Wells, S., and Chenevert, T.L. (2025, January 10\u201315). Harmonization of AI\/DL accelerated quantitative bi-parametric prostate MRI: Demonstration in multi-parametric phantom and patients. Proceedings of the 2025 ISMRM, Honolulu, HI, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.mri.2022.08.008","article-title":"Reduced field-of-view and multi-shot DWI acquisition techniques: Prospective evaluation of image quality and distortion reduction in prostate cancer imaging","volume":"93","author":"Lawrence","year":"2022","journal-title":"Magn. Reson. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e230427","DOI":"10.1148\/radiol.230427","article-title":"Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI","volume":"308","author":"Bischoff","year":"2023","journal-title":"Radiology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3534","DOI":"10.1002\/mp.12311","article-title":"Validation of phantom-based harmonization for patient harmonization","volume":"44","author":"Panetta","year":"2017","journal-title":"Med. Phys."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.acra.2020.02.027","article-title":"Portable Perfusion Phantom Offers Quantitative Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Accurate Prostate Cancer Grade Stratification: A Pilot Study","volume":"28","author":"Kim","year":"2021","journal-title":"Acad. Radiol."},{"key":"ref_35","first-page":"2770","article-title":"Traveling Subject-Informed Harmonization Increases Reliability of Brain Diffusion Tensor and Neurite Mapping","volume":"15","author":"Saito","year":"2023","journal-title":"Aging. Dis."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1002\/jmri.28024","article-title":"Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate","volume":"56","author":"Johnson","year":"2022","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/S1470-2045(24)00220-1","article-title":"PI-CAI consortium. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): An international, paired, non-inferiority, confirmatory study","volume":"25","author":"Saha","year":"2024","journal-title":"Lancet Oncol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4915","DOI":"10.1007\/s00330-024-11323-0","article-title":"AI-powered prostate cancer detection: A multi-centre, multi-scanner validation study","volume":"35","author":"Giganti","year":"2025","journal-title":"Eur. Radiol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s41747-022-00288-8","article-title":"Artificial intelligence for prostate MRI: Open datasets, available applications, and grand challenges","volume":"6","author":"Sunoqrot","year":"2022","journal-title":"Eur. Radiol. Exp."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Telecan, T., Chiorean, A., Sipos-Lascu, R., Caraiani, C., Boca, B., Hendea, R.M., Buliga, T., Andras, I., Crisan, N., and Lupsor-Platon, M. (2025). ISUP Grade Prediction of Prostate Nodules on T2WI Acquisitions Using Clinical Features, Textural Parameters and Machine Learning-Based Algorithms. Cancers, 17.","DOI":"10.3390\/cancers17122035"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Castaldo, R., Brancato, V., Cavaliere, C., Trama, F., Illiano, E., Costantini, E., Ragozzino, A., Salvatore, M., Nicolai, E., and Franzese, M. (2022). A Framework of Analysis to Facilitate the Harmonization of Multicenter Radiomic Features in Prostate Cancer. J. Clin. Med., 12.","DOI":"10.3390\/jcm12010140"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"120125","DOI":"10.1016\/j.neuroimage.2023.120125","article-title":"Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization","volume":"274","author":"Hu","year":"2023","journal-title":"Neuroimage"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"668","DOI":"10.1002\/jmri.28093","article-title":"Repeatability and Reproducibility Assessment of the Apparent Diffusion Coefficient in the Prostate: A Trial of the ECOG-ACRIN Research Group (ACRIN 6701)","volume":"56","author":"Boss","year":"2022","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.mri.2024.04.018","article-title":"Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T","volume":"110","author":"Lemainque","year":"2024","journal-title":"Magn. Reson. Imaging"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1002\/jmri.25237","article-title":"Variability and bias assessment in breast ADC measurement across multiple systems","volume":"44","author":"Keenan","year":"2016","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3325","DOI":"10.1002\/mp.15556","article-title":"Technical note: Temperature and concentration dependence of water diffusion in polyvinylpyrrolidone solutions","volume":"49","author":"Amouzandeh","year":"2022","journal-title":"Med. Phys."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4740","DOI":"10.1039\/b005319h","article-title":"Temperature-dependent self-diffusion coefficients of water and six selected molecular 16 liquids for calibration in accurate 1H NMR PFG measurements","volume":"2","author":"Holz","year":"2000","journal-title":"Phys. Chem. Chem. Phys."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5858\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:49:01Z","timestamp":1760035741000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/18\/5858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"references-count":47,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["s25185858"],"URL":"https:\/\/doi.org\/10.3390\/s25185858","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,9,19]]}}}