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The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery.

ABSTRACT: In 2012, Rivaie et al. introduced RMIL conjugate gradient (CG) method which is globally convergent under the exact line search. Later, Dai (2016) pointed out abnormality in the convergence result and thus, imposed certain restricted RMIL CG parameter as a remedy. In this paper, we suggest an efficient RMIL spectral CG method. The remarkable feature of this method is that, the convergence result is free from additional condition usually imposed on RMIL. Subsequently, the search direction is sufficiently descent independent of any line search technique. Thus, numerical experiments on some set of benchmark problems indicate that the method is promising and efficient. Furthermore, the efficiency of the proposed method is demonstrated on applications arising from arm robotic model and image restoration problems.

SUBMITTER: Salihu N

PROVIDER: S-EPMC10019646 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery.

Salihu Nasiru N Kumam Poom P Awwal Aliyu Muhammed AM Sulaiman Ibrahim Mohammed IM Seangwattana Thidaporn T

PloS one 20230316 3

In 2012, Rivaie et al. introduced RMIL conjugate gradient (CG) method which is globally convergent under the exact line search. Later, Dai (2016) pointed out abnormality in the convergence result and thus, imposed certain restricted RMIL CG parameter as a remedy. In this paper, we suggest an efficient RMIL spectral CG method. The remarkable feature of this method is that, the convergence result is free from additional condition usually imposed on RMIL. Subsequently, the search direction is suffi ...[more]

PMID: 36928212

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Project description:ObjectivesOur Enhanced Recovery After Thoracic Surgery protocol was implemented on February 1, 2018, and firmly established 7 months later. We instituted protocol modifications on January 1, 2020, aiming to further reduce postoperative opioid consumption. We sought to evaluate the influence of such efforts on clinical outcomes and the use of both schedule II and schedule IV opioids following robotic thoracoscopic procedures.MethodsA retrospective study of patients undergoing elective robotic procedures between September 1, 2018, and December 31, 2020, was conducted. Essential components of pain management in the original protocol included nonopioid analgesics, intercostal nerve blocks with long-acting liposomal bupivacaine diluted with normal saline, and opioids (ie, scheduled tramadol administration and as-needed schedule II narcotics). Protocol optimization included replacing saline diluent with 0.25% bupivacaine and switching tramadol to as needed, keeping other aspects unchanged. Demographic characteristics, type of robotic procedures, postoperative outcomes, and in-hospital and postdischarge opioids prescribed (ie, milligrams of morphine equivalent [MME]) were extracted from electronic medical records.ResultsThree hundred twenty-four patients met the inclusion criteria (159 in the original and 183 in the optimized protocol). There was no difference in postoperative outcomes or acute postoperative pain; there was a significant reduction of in-hospital and postdischarge opioid requirements in the optimized cohort. For anatomic resections: mean, 60.0 MME (range, 0-60.0 MME) versus mean, 105.0 MME (range, 60.0-150.0 MME), and other procedures: mean, 0 MME (range, 0-60 MME) versus mean, 140.0 (range, 60.0-150.0 MME) (P < .00001) with median schedule II opioids prescribed = 0.ConclusionsSmall modifications to our protocol for pain management strategies are safe and associated with significant decrease of opioid requirements, particularly schedule II narcotics, during the postoperative period without influencing acute pain levels.

Project description:Despite significant dosimetric gains, clinical implementation of the 4π non-coplanar radiotherapy on the widely available C-arm gantry system is hindered by limited clearance, and the need to perform complex coordinated gantry and couch motion. A robotic radiotherapy platform would be conducive to such treatment but a new conflict between field size and MLC modulation resolution needs to be managed for versatile applications. This study investigates the dosimetry and delivery efficiency of purposefully creating many isocenters to achieve simultaneously high MLC modulation resolution and large tumor coverage. An integrated optimization framework was proposed for simultaneous beam orientation optimization (BOO), isocenter selection, and fluence map optimization (FMO). The framework includes a least-square dose fidelity objective, a total variation term for regularizing the fluence smoothness, and a group sparsity term for beam selection. A minimal number of isocenters were identified for efficient target coverage. Colliding beams excluded, high-resolution small-field 4π intensity-modulated radiotherapy (IMRT) treatment plans with 50 cm source-to-isocenter distance (SID-50) on 10 Head and Neck (H&N) cancer patients were compared with low-resolution large-field plans with 100 cm SID (SID-100). With the same or better target coverage, the average reduction of [Dmean, Dmax] of 20-beam SID-50 plans from 20-beam SID-100 plans were [2.09 Gy, 1.19 Gy] for organs at risk (OARs) overall, [3.05 Gy, 0.04 Gy] for parotid gland, [3.62 Gy, 5.19 Gy] for larynx, and [3.27 Gy, 1.10 Gy] for mandible. R50 and integral dose were reduced by 5.3% and 9.6%, respectively. Wilcoxon signed-rank test showed significant difference (p < 0.05) in planning target volume (PTV) homogeneity, PTV Dmax, R50, Integral dose, and OAR Dmean and Dmax. The estimated delivery time of 20-beam [SID-50, SID-100] plans were [19, 18] min and [14, 9] min, assuming 5 fractions and 30 fractions, respectively. With clinically acceptable delivery efficiency, many-isocenter optimization is dosimetrically desirable for treating large targets with high modulation resolution on the robotic platform.

Dataset Information

The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery.

Publications

The global convergence of spectral RMIL conjugate gradient method for unconstrained optimization with applications to robotic model and image recovery.

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