Myocardial perfusion imaging with conventional gamma camera: to what level do data-driven, video and belt respiratory motion curves correlate?

Objectives We previously developed an LED video device and a data-driven (nuclear medicine images) method to generate respiratory-motion (RM) curves. These 2 systems are used to deal with RM in nuclear medicine using our processing software (REGAT). Here, we evaluate how RM curves generated by 3 methods compare between them: video, data-driven and respiratory belt monitor.

Methods Were included 5 patients having stress (3.7 MBq/Kg 99mTc-Tetrofosmin) then rest supine SPECT MPI 3-hours later (11 MBq/Kg). After rest MPI acquisition, video and belt devices were installed. The video device consisted of a red light LED mounted on a small black plate and clipped to an ECG patch in the epigastric area and a consumer device video webcam camera focusing on the LED (WebCam Pro Philips SPC 1330 NC PC Camera). The Respiratory Monitor Belt consisted of a commercially available device (Vernier Software & Technology). Then, 3 successive dynamic planar acquisitions in the RAO view (3 phases) were acquired on a dual-head Symbia-T2 SPECT/CT. Concomitant to each acquired phase, a video and a belt-RM curve were acquired. Video was processed to generate a RM curve. Dynamic acquisitions were processed with REGAT to generate data-driven RM curves.

Results Mean±SD of linear correlations between data-driven RM curves vs belt-RM curves and video-RM curves were 0.87±0.04, 0.89±0.04 for phase 1, 0.86±0.07 and 0.85±0.07 for phase 2, and 0.86±0.07 and 0.86±0.07 for phase 3, respectively. Mean±SD of linear correlations between belt-RM curves vs video-RM curves were 0.96±0.02 for phase 1, 0.95±0.03 for phase 2, and 0.94±0.05 for phase 3.

Conclusions In MPI studies using 99mTc-Tetrofosmin, data-driven RM curves generated with REGAT, video-RM curves and belt-RM curves are very highly correlated. Each of these 3 methods may be used alternatively to deal with RM.