{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:02:03Z","timestamp":1789700523792,"version":"4.0.1"},"reference-count":46,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Conventional VMAT optimization largely treats delivery time and deliverability as emergent properties of simplified, control\u2010point\u2010centric models that often ignore finite acceleration and other dynamic limits. As modern linacs continue to increase maximum axis speeds and dose rates, there is a growing need for a planning paradigm that makes the plan quality\u2010time trade\u2010off explicit and steerable, especially when shorter treatments can provide clinical benefits beyond throughput.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This work introduces Dynamic Modulated Arc Therapy (DMAT), a time\u2010aware, modulation\u2010steered framework that jointly optimizes dosimetric quality, delivery time, and modulation complexity. Here, time\u2010aware denotes that delivery time is computed within the optimizer from an explicit model of machine dynamics, including finite acceleration and axis synchronization, rather than inferred after optimization; modulation\u2010steered denotes that leaf\u2010travel allowance, control\u2010point density, total monitor units (MUs), and aperture complexity are governed together by a single user\u2010selected modulation level, making the amount and distribution of modulation an explicit input to the optimizer. Clinical goals enter the optimizer as metric\u2010based cost functions defined by each criterion's metric, goal value, acceptable variation, and priority, with supporting objectives generated automatically.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>DMAT couples (1) direct machine emulation that accounts for axis synchronization and dynamic limits, (2) dynamic modulation control, and (3) clinical metrics as optimization cost functions. A user\u2010selected modulation level (\u22123 to +3) governs leaf\u2010travel allowance, total MU behavior, aperture\u2010complexity penalties, and control\u2010point (CP) density. Plans are created by defining a treatment trajectory, initializing CP geometry, leaf positions, and MU, then iteratively improving the solution with a progressive\u2010resolution loop alternating dosimetric updates with sequencing\/deliverability updates, followed by post\u2010processing to reduce complexity with minimal dose change. CP density is adapted nonuniformly: a preliminary uniform\u2010CP optimization is followed by redistribution according to combined delivery\u2010complexity and geometric information. DMAT was evaluated using a hypothetical accelerated C\u2010arm delivery system (max gantry speed 2.5 RPM, max MLC speed 6.25\u00a0cm\/s, max dose rate 3000 MU\/min) on representative head\u2010and\u2010neck, lung SBRT, and prostate SBRT cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Across all cases, increasing modulation level produced plans with increased modulation surrogates (e.g., MU\/Gy and aperture complexity) and progressively longer delivery time, while adaptive non\u2010uniform CP allocation concentrated additional CPs in arc sectors where higher angular resolution was most beneficial. The resulting quality\u2010time trade\u2010off space was disease site dependent: the head\u2010and\u2010neck case showed substantial plan\u2010quality gains with increased modulation and CP density, whereas prostate SBRT and lung SBRT exhibited smaller incremental quality improvements beyond baseline despite longer delivery times. When efficiency was prioritized (negative modulation levels), DMAT predictably reduced modulation and maintained a constant CP budget while shortening delivery time, producing corresponding and quantifiable reductions in plan quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>DMAT establishes a time\u2010aware, modulation\u2010steered planning and delivery methodology in which plan quality and modulation complexity are co\u2010optimized under explicit clinical and user control using machine\u2010aware timing. Accurate delivery time is exposed to users during planning through advanced machine emulation. By doing so, DMAT makes quality\u2010time trade\u2010offs transparent, predictable, and navigable, providing a practical foundation for leveraging next\u2010generation delivery systems\u2019 capabilities and supporting time\u2010constrained workflows such as motion\u2010sensitive treatments and adaptive radiotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70764","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T06:59:06Z","timestamp":1787641146000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic modulated arc therapy (DMAT): A time\u2010aware, modulation\u2010steered optimization framework for next\u2010generation radiotherapy delivery"],"prefix":"10.1002","volume":"27","author":[{"given":"Taoran","family":"Li","sequence":"first","affiliation":[{"name":"Varian Medical Systems Inc., a Siemens Healthineers company  Palo Alto California 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