Jizhe Wang

An interventricular sulcus guided cardiac motion estimation method

Accurate estimation of cardiac motion from clinical medical images especially motion components parallel to edges has been found to be difficult using the intensity based optical flow algorithm without any prior information. This research aims to develop a new feature guided cardiac motion estimation method for gated myocardial perfusion (MP) PET images whose quality has greatly improved in recent years. A unique anatomical feature of human heart-the interventricular sulcus, which contains important motion information, is becoming visible and can potentially be extracted from MP PET images to guide the motion estimation process. In this work the motion of the extracted anatomical feature was used to create an initial estimate of the cardiac motion vector field (MVF) to be combined with the optical flow motion estimation algorithm to improve the estimation accuracy. To evaluate the proposed cardiac motion method, we simulated cardiac gated MP PET images from the 4-D XCAT phantom. The feature-guided initial MVFs were used in combination with the optical flow algorithm and applied to the simulated gated MP PET images to obtain the estimated cardiac MVF. The results were evaluated quantitatively by comparison with the cardiac MVF of the XCAT phantom which was regarded as the ground truth, and with those from non-feature based motion estimation method. The comparison indicates that our feature-guided method can achieve more accurate estimation of cardiac motion than non-feature-based method.

Improved spatial and temporal resolution of gated myocardial perfusion PET using post reconstruction dual respiratory and cardiac motion compensation

The current work was based on a previously developed 4D image reconstruction method for myocardial perfusion (MP) PET where dual respiratory motion (RM) and cardiac motion (CM) compensation were performed post reconstruction. We implemented and extended the work on patient data from a commercial PET system and demonstrated further image quality improvement. We divided the PET events into 6 equal-count respiratory gates based on heart “center-of-mass” movement estimated from the list-mode PET data. For each respiratory gate, the list-mode data was sorted and rebinned into 8 and 16 cardiac gates in addition to the conventional 8 cardiac gates. The vendor provided list-mode based time-of-flight reconstruction was used in all image reconstruction with and without the TOF information to provide high quality 3D PET images before the RM&CM compensation. For each cardiac gate, the RM vector fields (RMVF) were derived from the respiratory gated reconstructed images, and were applied to the dual gated images to obtain RM free images. The CM vector fields (CMVF) were estimated from the cardiac gated RM free images; the CM compensated image at each cardiac gate was the sum of the original image and images transformed from the other cardiac gates based on the CMVF. Dual RM&CM compensation were shown to improve spatial resolution by reducing RM blur through RM compensation and suppressing image noise through CM compensation as compared to those without CM compensation. The 16-frame cardiac gated images provided much higher temporal resolution for revealing subtle regional motion than the 8-frame cardiac gated images and showed similar noise level per frame. In conclusion, dual RM&CM compensation improves spatial resolution and allows higher number of cardiac gated frames in 4D gated MP PET with improved temporal resolution without compromise in image noise.

A papillary muscle guided motion estimation method for gated cardiac imaging

This research aims to develop a new feature guided motion estimation method for the left ventricular wall in gated cardiac imaging. The guiding feature is the “footprint” of one of the papillary muscles, which is the attachment of the papillary muscle on the endocardium. Myocardial perfusion (MP) PET images simulated from the 4-D XCAT phantom, which features papillary muscles, realistic cardiac motion with known motion vector field (MVF), were employed in the study. The 4-D MVF of the heart model of the XCAT phantom was used as a reference. For each MP PET image, the 3- D “footprint” surface of one of the papillary muscles was extracted and its centroid was calculated. The motion of the centroid of the “footprint” throughout a cardiac cycle was tracked and analyzed in 4-D. This motion was extrapolated to throughout the entire heart to build a papillary muscle guided initial estimation of the 4-D cardiac MVF. A previous motion estimation algorithm was applied to the simulated gated myocardial PET images to estimate the MVF. Three different initial MVF estimates were used in the estimation, including zero initial (0-initial), the papillary muscle guided initial (P-initial), and the true MVF from phantom (T-initial). Qualitative and quantitative comparison between the estimated MVFs and the true MVF showed the P-initial provided more accurate motion estimation in longitudinal motion than the 0-initial with over 70% improvement and comparable accuracy with that of the T-initial. We concluded that when the footprint can be tracked accurately, this feature guided approach will significantly improve the accuracy and robustness of traditional optical flow based motion estimation method.

Advances in 4D Gated Cardiac PET Imaging for Image Quality Improvement and Cardiac Motion and Contractility Estimation

Quantitative four-dimensional (4D) image reconstruction methods with respiratory and cardiac motion compensation are an active area of research in ECT imaging, including SPECT and PET. They are the extensions of three-dimensional (3D) statistical image reconstruction methods with iterative algorithms that incorporate accurate models of the imaging process and provide significant improvement in the quality and quantitative accuracy of the reconstructed images as compared to that obtained from conventional analytical image reconstruction methods. The new 4D image reconstruction methods incorporate additional models of the respiratory and cardiac motion of the patient to reduce image blurring due to respiratory motion and image noise of the cardiac-gated frames of the 4D cardiac-gated images. We describe respiratory motion estimation and gating method based on patient PET list-mode data. The estimated respiratory motion is applied to the respiratory gated data to reduce respiratory motion blur. The gated cardiac images derived from the list-model data are used to estimate cardiac motion. They are then used in the cardiac-gated images summing the motion-transformed cardiac-gated images for significant reduction in the gated images noise. Dual respiratory and cardiac motion compensation is achieved by combining the respiratory and cardiac motion compensation steps. The results are further significant improvements of the 4D gated cardiac PET images. The much improved gated cardiac PET image quality increases the visibility of anatomical details of the heart, which can be explored to provide more accurate estimation of the cardiac motion vector field and cardiac contractility.

Scanning surface-enhanced Raman spectroscopy (SERS) of chemical agent simulants on templated Au-Ag nanowire substrates

We report the results of scanning micro-Raman spectroscopy obtained on Au-Ag nanowires for a variety of chemical warfare agent simulants. Rough silver segments embedded in gold nanowires showed enhancement of 105 - 107 and allowed unique identification of 3 of 4 chemical agent simulants tested. These results suggest a promising method for detection of compounds significant for security applications, leading to sensors that are compact and selective.

Development and evaluation of data-driven respiratory gating methods with simulated list-mode PET data

Data-driven respiratory gating techniques in cardiac PET extract respiratory motion signal from the PET data to guide respiratory gating and motion estimation. However, the influence of the quality of the list-mode data including the uptake ratio between the myocardium and background and the detected count level on its performance has never been studied thoroughly. In this project, list-mode PET data derived from realistic Monte Carlo simulation of different uptake ratios and count levels was employed to quantitatively evaluate the respiratory motion signal extraction and estimation techniques. Simulated projection data were generated from the 4D XCAT phantom with known respiratory motion. The list-mode data were rebinned into projection data at 200 msec intervals and the centers-of-mass of a region-of-interest over the heart were calculated. Contributions from the background were subtracted in the centroid calculation. The peak in the Fourier transform of the time sequence of the centroid locations, or the frequency spectrum, was identified as the respiratory motion signal and its SNR was measured. The high frequency components of the frequency spectrum were removed for noise smoothing before extracting the respiratory motion signal for use in respiratory gating estimation. The results show that with background correction, the amplitude of the estimated respiratory motion signal is increased and is closer to the truth. Both lower myocardium to background uptake ratio and lower count level reduce the SNR of the respiratory motion signal. When they become too low, the extraction of the respiratory motion signal becomes difficult or fails completely.

Nanowire-based surface-enhanced Raman spectroscopy (SERS) for chemical warfare simulants

Hand-held instruments capable of spectroscopic identification of chemical warfare agents (CWA) would find extensive use in the field. Because CWA can be toxic at very low concentrations compared to typical background levels of commonly-used compounds (flame retardants, pesticides) that are chemically similar, spectroscopic measurements have the potential to reduce false alarms by distinguishing between dangerous and benign compounds. Unfortunately, most true spectroscopic instruments (infrared spectrometers, mass spectrometers, and gas chromatograph-mass spectrometers) are bench-top instruments. Surface-acoustic wave (SAW) sensors are commercially available in hand-held form, but rely on a handful of functionalized surfaces to achieve specificity. Here, we consider the potential for a hand-held device based on surface enhanced Raman scattering (SERS) using templated nanowires as enhancing substrates. We examine the magnitude of enhancement generated by the nanowires and the specificity achieved in measurements of a range of CWA simulants. We predict the ultimate sensitivity of a device based on a nanowire-based SERS core to be 1-2 orders of magnitude greater than a comparable SAW system, with a detection limit of approximately 0.01 mg m-3.

Dual respiratory and cardiac motion estimation in PET imaging: Methods design and quantitative evaluation

PURPOSE The goal of this study was to develop and evaluate four post-reconstruction respiratory and cardiac (R&C) motion vector field (MVF) estimation methods for cardiac 4D PET data. METHOD In Method 1, the dual R&C motions were estimated directly from the dual R&C gated images. In Method 2, respiratory motion (RM) and cardiac motion (CM) were separately estimated from the respiratory gated only and cardiac gated only images. The effects of RM on CM estimation were modeled in Method 3 by applying an image-based RM correction on the cardiac gated images before CM estimation, the effects of CM on RM estimation were neglected. Method 4 iteratively models the mutual effects of RM and CM during dual R&C motion estimations. Realistic simulation data were generated for quantitative evaluation of four methods. Almost noise-free PET projection data were generated from the 4D XCAT phantom with realistic R&C MVF using Monte Carlo simulation. Poisson noise was added to the scaled projection data to generate additional datasets of two more different noise levels. All the projection data were reconstructed using a 4D image reconstruction method to obtain dual R&C gated images. The four dual R&C MVF estimation methods were applied to the dual R&C gated images and the accuracy of motion estimation was quantitatively evaluated using the root mean square error (RMSE) of the estimated MVFs. RESULTS Results show that among the four estimation methods, Methods 2 performed the worst for noise-free case while Method 1 performed the worst for noisy cases in terms of quantitative accuracy of the estimated MVF. Methods 4 and 3 showed comparable results and achieved RMSE lower by up to 35% than that in Method 1 for noisy cases. CONCLUSION In conclusion, we have developed and evaluated 4 different post-reconstruction R&C MVF estimation methods for use in 4D PET imaging. Comparison of the performance of four methods on simulated data indicates separate R&C estimation with modeling of RM before CM estimation (Method 3) to be the best option for accurate estimation of dual R&C motion in clinical situation.

Development and evaluation of two interventricular sulcus extraction methods for cardiac PET

The goal is to develop and evaluate two methods to extract interventricular sulcus (IS) from 4D cardiac gated (CG) myocardial perfusion (MP) PET images for use in quantitative cardiac motion estimation. In Method 1, the entire myocardium was first segmented from the 3D MP PET image of each CG frame by 3D region growing. The left ventricle (LV) was then extracted by 3D image erosion and dilation, and the right ventricle (RV) by subtracting the LV from the segmented myocardium. Finally, the IS was identified from the overlap of the extracted LV and RV. In Method 2, the inner boundary (InB) of the RV was first extracted by segmenting the blood pool (BP) within the RV using 3D region growing. The septal side of the BP boundary, or the LV outer boundary (OutB) within the RV, was separated out. We then identified segments of the LV OutB on the anterior (Ant) and posterior (Post) sides of the RV. The three extracted line segments were fitted with a B-spline curve while the lateral side of the RV InB was extrapolated using B-spline. The intersection points of the fitted curves were identified as the Ant and Post IS which, when assembled from all short-axis images, formed entire IS. The two IS extraction methods were applied to realistic CG MP PET images simulated from the 4D XCAT phantom at PET system resolution from 0.6mm to 4.5mm for 4 CG frames. The accuracy of the extracted IS were compared with the true IS from the XCAT. For both methods, the errors of the extracted IS locations increased with poorer PET system resolution with the Ant IS showing lower accuracy than the Post IS. Method 1 achieved lower accuracy than Method 2 and failed to provide reliable estimates at 4.5mm system resolution. We conclude the B-spline based interpolation and extrapolation curve fitting method was capable of extracting the IS with high reliability and accuracy from the 4D CG MP PET images obtained from state-of-the-art and PET systems and will be useful in the cardiac motion estimation.

A constrained feature-based cardiac motion estimation method for cardiac PET

The goal is to develop and evaluate a new constrained feature-based cardiac motion estimation (ME) method for cardiac gated (CG) myocardial perfusion (MP) PET images to improve the accuracy of the estimated cardiac motion vector field (MVF). CG-MP PET projection data were generated from the 4D XCAT phantom with realistic anatomical structures and cardiac MVF models, and reconstructed using the STIR simulation and reconstruction software. The interventricular sulcus (IS) was extracted from each CG-MP PET image by applying B-spline extrapolation and interpolation methods to the extracted edges of the left (LV) and right ventricular (RV) walls. The estimated MVFs of the extracted ISs were calculated between adjacent CG frames. In the previously feature-based cardiac ME algorithm, the estimated IS MVF was used as an initial estimate in the conventional optical-flow ME algorithm. The information was found to reduce the aperture problem effect and provide more accurate cardiac MVF estimate as compared to without the information, using the cardiac MVF of the XCAT as the truth. In the new algorithm, it was used as an additional constraint to restrict the range of the search for the cardiac MVF estimate. The new approach was evaluated in terms of accuracy of the estimated cardiac MVF and compared with those using the previous methods. The evaluation results showed the estimated cardiac MVF obtained from using the IS as an initial estimate (S-initial) was more accurate than that using no initial estimate (0-initial) and was comparable to that using the truth MVF as the initial estimate (T-initial). The estimation accuracy was further improved with the S-initial and the IS motion as an additional constraint. In conclusion, we developed and evaluated a new constrained feature-based cardiac ME method for cardiac PET. We demonstrated the new method provided more accurate estimation of the cardiac MVF as compared to the conventional and a previously developed feature-based cardiac ME method for CG-MP PET.