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Its ability to deliver focused ultrasound waves to precise brain regions has led to over 50 clinical trials targeting conditions such as opioid addiction, Alzheimer's disease, dementia, epilepsy, and glioblastoma. However, skull heterogeneity complicates accurate focal spot prediction and energy delivery, requiring rapid yet precise phase aberration correction in clinical\u00a0workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To address the trade\u2010off between computational efficiency and accuracy in current focus prediction methods, we introduce TUSNet, a deep learning framework for rapid and accurate transcranial ultrasound pressure field and phase aberration correction\u00a0computation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>TUSNet, an end\u2010to\u2010end neural network, was trained to predict both 2D transcranial ultrasound pressure fields and phase corrections. TUSNet was trained on 180432 synthetic skull Computed Tomography (CT) segments, and tested on 1232 real skull CT segments. Its performance was benchmarked against k\u2010Wave, a MATLAB\u2010based acoustic simulation package, evaluating computation speed, focal spot accuracy, phase correction accuracy, and pressure magnitude\u00a0estimation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>TUSNet computed pressure fields and phase corrections in 21 ms, which is over 1200 faster than k\u2010Wave, while achieving 98.3% accuracy in peak pressure magnitude estimation and a mean focal positioning error of only 0.18 mm relative to k\u2010Wave ground truth. End\u2010to\u2010end training took approximately 8 h on 4x NVIDIA A100 80 GB\u00a0GPUs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>TUSNet demonstrates that deep learning can provide accurate and rapid estimates of phase aberrations and transcranial pressure fields, offering a promising direction for accelerating ultrasound treatment planning. While the present validation is based on simulated, noise\u2010free ultrasound fields, the results establish a foundation that future experimental studies can build on to assess performance under real\u2010world clinical\u00a0conditions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70259","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:58:36Z","timestamp":1767178716000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A deep\u2010learning model for one\u2010shot transcranial ultrasound simulation and phase aberration correction"],"prefix":"10.1002","volume":"53","author":[{"given":"Kasra","family":"Naftchi\u2010Ardebili","sequence":"first","affiliation":[{"name":"Department of Bioengineering Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karanpartap","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerald R.","family":"Popelka","sequence":"additional","affiliation":[{"name":"Departments of Otolaryngology and Radiology Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kim Butts","family":"Pauly","sequence":"additional","affiliation":[{"name":"Departments of Radiology, Bioengineering, and Electrical Engineering Stanford University Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-3112-9.50012-8"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.23981"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2021.749162"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/srep34026"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.14.13632"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.31728\/jnn.2019.00046"},{"key":"e_1_2_9_8_1","doi-asserted-by":"crossref","unstructured":"LeinengaG LangtonC NisbetR G\u00f6tzJ.Ultrasound treatment of neurological diseases\u2010current and emerging applications.2016;12:161\u2010174","DOI":"10.1038\/nrneurol.2016.13"},{"key":"e_1_2_9_9_1","doi-asserted-by":"crossref","unstructured":"PiperRJ HughesMA MoranCM KandasamyJ.Focused ultrasound as a non\u2010invasive intervention for neurological disease: a review.2016;30(3):286\u2010293","DOI":"10.3109\/02688697.2016.1173189"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1600159"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13311-017-0515-1"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-04529-6"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(13)70048-6"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0000000000001826"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3171\/2011.10.FOCUS11248"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2017.3098"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(17)30403-9"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brs.2014.08.008"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2011.371"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/nn.3620"},{"key":"e_1_2_9_21_1","unstructured":"Various.NIH TUS clinical trials.https:\/\/clinicaltrials.gov\/search?intr=transcranial+ultrasound&viewType=Table&aggFilters=status%3Arec+act%2CstudyType%3Aint"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/58.156174"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.3109\/02656736.2013.861519"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-43775-6"},{"key":"e_1_2_9_25_1","doi-asserted-by":"crossref","unstructured":"BlackmoreJ ShrivastavaS SalletJ ButlerCR ClevelandRO.Ultrasound neuromodulation: a review of results mechanisms and safety.2019;45:1509\u20101536","DOI":"10.1016\/j.ultrasmedbio.2018.12.015"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aa843e"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3668316"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.1121\/1.4712021"},{"key":"e_1_2_9_29_1","doi-asserted-by":"crossref","unstructured":"VyasU ChristensenD.Ultrasound beam propagation using the hybrid angular spectrum method. 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