Advances in quantitative MRI increasingly rely on methods that can overcome motion, extract multiple tissue parameters efficiently, and provide clinically meaningful biomarkers. Across the brain and liver, motion compensation, multi-parametric mapping, and susceptibility-based tissue characterization remain key challenges. In this work, we present three complementary developments that address these challenges: deep-learning–based motion correction for mGRE imaging, free-breathing whole-liver quantitative mapping using implicit neural representations, and susceptibility-based fibrosis assessment validated against histology. We first propose a temporal fusion network (tUnet) for motion correction of multi-echo gradient-echo (mGRE) images and further refine it using autofocus-derived, subject-specific motion trajectories to create a fine-tuned version (tftUnet). Evaluated in the context of quantitative susceptibility mapping (QSM) for Parkinson’s disease (PD), the method significantly improves image quality. In simulated brain data (n = 15), tUnet boosted SSIM, PSNR, and RMSE (all p < 0.05). In nine PD patients with real motion artifacts, radiologist scoring increased from 1.11 ± 0.33 (uncorrected) to 2.56 ± 1.01 (tUnet) and 3.33 ± 1.22 (tftUnet), demonstrating substantial suppression of motion artifacts in clinical QSM. Motivated by the broader need for motion-robust quantitative imaging, we also developed a free-breathing 3D whole-liver multi-parametric mapping technique capable of estimating water T1, water T2, fat fraction (FF), and R2*. Using a multi-echo 3D stack-of-spirals acquisition with inversion-recovery and T2-preparation, we designed an implicit neural representation–based fingerprinting method (FINR) that jointly reconstructs static images, motion fields, water–fat separation, and parametric maps. In ten healthy subjects, FINR produced sharp, motion-free images with minimal bias compared with conventional breath-held imaging, with training requiring ~3 hours per subject and inference under 1 minute. Finally, to enable non-invasive assessment of chronic liver disease, we investigated whether mGRE-derived susceptibility sources can quantify fibrosis in ex-vivo liver explants. After fat–water separation, mGRE data were processed to obtain R2QSM and R2’QSM (R2’ = R2 – R2), isolating diamagnetic (negative) susceptibility related to fibrosis. In 20 fixed liver sections with histology, negative susceptibility differentiated mild (F0–F1) from moderate–advanced fibrosis (F2–F3; p = 0.0025), distinguished F2–F3 from cirrhosis (F4; p = 0.021), and separated early (F0–F2) from advanced disease (F3–F4) with 90% sensitivity, 90% specificity, and an AUC of 0.88. It outperformed other MRI parameters and showed positive correlations with fibrosis stage (r = 0.60 for R2*QSM; r = 0.58 for R2’QSM).