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The existing regularized optimization\u2010based time\u2010delay estimation (TDE) techniques suffer from at least one of the following drawbacks: (1) The regularizer is not aligned with the tissue deformation physics due to taking only the first\u2010order displacement derivative into account; (2) The \u2010norm of the displacement derivatives, which oversmooths the estimated time\u2010delay, is utilized as the regularizer; (3) The modulus function defined mathematically should be approximated by a smooth function to facilitate the optimization of \u2010norm.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Our purpose is to develop a novel TDE technique that resolves the aforementioned shortcomings of the existing algorithms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Herein, we propose employing the alternating direction method of multipliers (ADMM) for optimizing a novel cost function consisting of \u2010norm data fidelity term and \u2010norm first\u2010 and second\u2010order spatial continuity terms. ADMM empowers the proposed algorithm to use different techniques for optimizing different parts of the cost function and obtain high\u2010contrast strain images with smooth backgrounds and sharp boundaries. We name our technique <jats:bold>A<\/jats:bold>DMM for tota<jats:bold>L<\/jats:bold> varia<jats:bold>T<\/jats:bold>ion <jats:bold>R<\/jats:bold>eg<jats:bold>U<\/jats:bold>lar<jats:bold>I<\/jats:bold>zation in ultrasound <jats:bold>ST<\/jats:bold>rain imaging (ALTRUIST). ALTRUIST's efficacy is quantified using absolute error (AE), Structural SIMilarity (SSIM), signal\u2010to\u2010noise ratio (SNR), contrast\u2010to\u2010noise ratio (CNR), and strain ratio (SR) with respect to GLUE, OVERWIND, and \u2010SOUL, three recently published energy\u2010based techniques, and UMEN\u2010Net, a state\u2010of\u2010the\u2010art deep learning\u2010based algorithm. Analysis of variance (ANOVA)\u2010led multiple comparison tests and paired \u2010tests at  overall significance level were conducted to assess the statistical significance of our findings. The Bonferroni correction was taken into account in all statistical tests. Two simulated layer phantoms, three simulated resolution phantoms, one hard\u2010inclusion simulated phantom, one multi\u2010inclusion simulated phantom, one experimental breast phantom, and three in vivo liver cancer datasets have been used for validation experiments. We have published the ALTRUIST code at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"http:\/\/code.sonography.ai\">http:\/\/code.sonography.ai<\/jats:ext-link>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>ALTRUIST substantially outperforms the four state\u2010of\u2010the\u2010art benchmarks in all validation experiments, both qualitatively and quantitatively. ALTRUIST yields up to , , and  SNR improvements and , , and  CNR improvements over \u2010SOUL, its closest competitor, for simulated, phantom, and in vivo liver cancer datasets, respectively, where the asterisk (*) indicates statistical significance. In addition, ANOVA\u2010led multiple comparison tests and paired \u2010tests indicate that ALTRUIST generally achieves statistically significant improvements over GLUE, UMEN\u2010Net, OVERWIND, and \u2010SOUL in terms of AE, SSIM map, SNR, and\u00a0CNR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A novel ultrasonic displacement tracking algorithm named ALTRUIST has been developed. The principal novelty of ALTRUIST is incorporating ADMM for optimizing an \u2010norm regularization\u2010based cost function. 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