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Spatial variations of the diffusion gradients make diffusion weightings and directions non\u2010uniform across the field of view, and deform perfect shells in the \u2010space designed for isotropic directional coverage. Such imperfections hinder parameter estimation: Anisotropic shells hamper the deconvolution of the fiber orientation distribution function (fODF), while brute\u2010force retraining of a nonlinear regressor for each unique set of directions and diffusion weightings is computationally inefficient.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Here, we propose a protocol\u2010independent parameter estimation (PIPE) method that enables fast parameter estimation for the most general case where each voxel is measured with a different protocol in \u2010space. PIPE applies to any spherical convolution\u2010based dMRI model, irrespective of its complexity, which makes it suitable both for white and gray matter in the brain or spinal cord, and for other tissues where fiber bundles have the same properties  (fiber response)  within a voxel, but are distributed with an arbitrary fODF. We also derive a parsimonious representation that isolates isotropic and anisotropic effects of gradient nonlinearities on multidimensional diffusion encodings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Applied to in vivo human MRI with linear tensor encoding on a high\u2010performance gradient system, PIPE evaluates fiber response and fODF parameters for the whole brain in the presence of significant gradient nonlinearities in under 3\u2009min.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>PIPE enables fast parameter estimation in the presence of arbitrary gradient nonlinearities, eliminating the need to arrange dMRI in shells or to retrain the estimator for different protocols in each voxel. PIPE applies to any model based on a convolution of a voxel\u2010wise fiber response and fODF, and data from varying \u2010values, diffusion\/echo times, and other scan parameters.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mrm.70144","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T04:37:54Z","timestamp":1765600674000},"page":"2277-2290","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["What If Each Voxel Were Measured With a Different Diffusion Protocol?"],"prefix":"10.1002","volume":"95","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1039-4803","authenticated-orcid":false,"given":"Santiago","family":"Coelho","sequence":"first","affiliation":[{"name":"Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI<sup>2<\/sup>R), Department of Radiology New York University School of Medicine  New York NY USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory","family":"Lemberskiy","sequence":"additional","affiliation":[{"name":"Center for Biomedical Imaging and Center for Advanced Imaging Innovation and Research (CAI<sup>2<\/sup>R), Department of Radiology New York University School of Medicine  New York NY USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6251-2341","authenticated-orcid":false,"given":"Ante","family":"Zhu","sequence":"additional","affiliation":[{"name":"GE HealthCare Technology and Innovation Center, Niskayuna  New York NY USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3663-6559","authenticated-orcid":false,"given":"Hong\u2010Hsi","family":"Lee","sequence":"additional","affiliation":[{"name":"Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital, Harvard Medical School  Charlestown South Carolina USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7020-0492","authenticated-orcid":false,"given":"Nastaren","family":"Abad","sequence":"additional","affiliation":[{"name":"GE HealthCare Technology and Innovation Center, Niskayuna  New York NY USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas K. 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