Our long-term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on validating our approach for monitoring the development of lung nodules detected in successive chest low dose computed tomography (LDCT) scans of a patient. Our methodology for monitoring the detected lung nodules includes 3-D LDCT data registration, which is non-rigid and involves two steps: (i) global target-to-prototype alignment of one scan to another using the learned prior appearance model followed by (ii) local alignment in order to correct for intricate relative deformations. This approach has been validated on elastic lung phantoms constructed using state-of-the-art microfluidics technology. The elastic lung phantoms are fabricated from a flexible transparent polymer, i.e., polydimethylsiloxane (PDMS). These Phantoms mimic the contractions and expansions of the lung and nodules seen during normal breathing. Experiments confirm the high accuracy of the proposed approach for measuring the growth rate of the detected lung nodules.