Doctor of Philosophy, Massachusetts Institute of Technology (2017)
B.S.E., National Taiwan University (2010)
Gregory Zaharchuk, Postdoctoral Faculty Sponsor
View details for DOI 10.1002/jmri.26000
To propose an MR-based method for generating continuous-valued head attenuation maps and to assess its accuracy and reproducibility. Demonstrating that novel MR-based photon attenuation correction methods are both accurate and reproducible is essential prior to using them routinely in research and clinical studies on integrated PET/MR scanners.Continuous-valued linear attenuation coefficient maps ("?-maps") were generated by combining atlases that provided the prior probability of voxel positions belonging to a certain tissue class (air, soft tissue, or bone) and an MR intensity-based likelihood classifier to produce posterior probability maps of tissue classes. These probabilities were used as weights to generate the ?-maps. The accuracy of this probabilistic atlas-based continuous-valued ?-map ("PAC-map") generation method was assessed by calculating the voxel-wise absolute relative change (RC) between the MR-based and scaled CT-based attenuation-corrected PET images. To assess reproducibility, we performed pair-wise comparisons of the RC values obtained from the PET images reconstructed using the ?-maps generated from the data acquired at three time points.The proposed method produced continuous-valued ?-maps that qualitatively reflected the variable anatomy in patients with brain tumor and agreed well with the scaled CT-based ?-maps. The absolute RC comparing the resulting PET volumes was 1.76?±?2.33 %, quantitatively demonstrating that the method is accurate. Additionally, we also showed that the method is highly reproducible, the mean RC value for the PET images reconstructed using the ?-maps obtained at the three visits being 0.65?±?0.95 %.Accurate and highly reproducible continuous-valued head ?-maps can be generated from MR data using a probabilistic atlas-based approach.
View details for DOI 10.1007/s00259-016-3489-z
View details for PubMedID 27573639
View details for PubMedCentralID PMC5285302
This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization algorithm.
View details for DOI 10.1088/0031-9155/61/18/6668
View details for Web of Science ID 000398071000009
View details for PubMedID 27541810
View details for PubMedCentralID PMC5095621
We present an approach for head MR-based attenuation correction (AC) based on the Statistical Parametric Mapping 8 (SPM8) software, which combines segmentation- and atlas-based features to provide a robust technique to generate attenuation maps (? maps) from MR data in integrated PET/MR scanners.Coregistered anatomic MR and CT images of 15 glioblastoma subjects were used to generate the templates. The MR images from these subjects were first segmented into 6 tissue classes (gray matter, white matter, cerebrospinal fluid, bone, soft tissue, and air), which were then nonrigidly coregistered using a diffeomorphic approach. A similar procedure was used to coregister the anatomic MR data for a new subject to the template. Finally, the CT-like images obtained by applying the inverse transformations were converted to linear attenuation coefficients to be used for AC of PET data. The method was validated on 16 new subjects with brain tumors (n = 12) or mild cognitive impairment (n = 4) who underwent CT and PET/MR scans. The ? maps and corresponding reconstructed PET images were compared with those obtained using the gold standard CT-based approach and the Dixon-based method available on the Biograph mMR scanner. Relative change (RC) images were generated in each case, and voxel- and region-of-interest-based analyses were performed.The leave-one-out cross-validation analysis of the data from the 15 atlas-generation subjects showed small errors in brain linear attenuation coefficients (RC, 1.38% ± 4.52%) compared with the gold standard. Similar results (RC, 1.86% ± 4.06%) were obtained from the analysis of the atlas-validation datasets. The voxel- and region-of-interest-based analysis of the corresponding reconstructed PET images revealed quantification errors of 3.87% ± 5.0% and 2.74% ± 2.28%, respectively. The Dixon-based method performed substantially worse (the mean RC values were 13.0% ± 10.25% and 9.38% ± 4.97%, respectively). Areas closer to the skull showed the largest improvement.We have presented an SPM8-based approach for deriving the head ? map from MR data to be used for PET AC in integrated PET/MR scanners. Its implementation is straightforward and requires only the morphologic data acquired with a single MR sequence. The method is accurate and robust, combining the strengths of both segmentation- and atlas-based approaches while minimizing their drawbacks.
View details for DOI 10.2967/jnumed.113.136341
View details for Web of Science ID 000344209200013
View details for PubMedID 25278515
View details for PubMedCentralID PMC4246705
DNA methylation plays an important role in regulating cell growth and disease development. Methylation profiles are examined by bisulfite conversion; however, the lack of markers for bisulfite conversion efficiency and appropriate internal control genes remains a major challenge. To address these issues, we utilized two bioinformatics approaches, coefficients of variances and resampling tests, to identify probes showing stable methylation levels from several independent microarray datasets. Mass spectrometry validated the consistently high methylation levels of the five probes (N4BP2, EGFL8, CTRB1, TSPAN3, and ZNF690) in 13 human tissue types from 24 cell lines. Linear associations between detected methylation levels and methyl concentrations of DNA samples were further demonstrated in three genes (N4BP2, EGFL8, and CTRB1). To summarize, we identified five genes which may serve as internal controls for methylation studies by analyzing large-scale microarray data, and three of them can be used as markers for evaluating the efficiency of bisulfite conversion.
View details for DOI 10.1038/srep04351
View details for Web of Science ID 000332621500003
View details for PubMedID 24619003
View details for PubMedCentralID PMC3950633
We present a new MRI-based attenuation correction (AC) approach for integrated PET/MRI systems that combines both segmentation- and atlas-based methods by incorporating dual-echo ultra-short echo-time (DUTE) and T1-weighted (T1w) MRI data and a probabilistic atlas. Segmented atlases were constructed from CT training data using a leave-one-out framework and combined with T1w, DUTE, and CT data to train a classifier that computes the probability of air/soft tissue/bone at each voxel. This classifier was applied to segment the MRI of the subject of interest and attenuation maps (?-maps) were generated by assigning specific linear attenuation coefficients (LACs) to each tissue class. The ?-maps generated with this "Atlas-T1w-DUTE" approach were compared to those obtained from DUTE data using a previously proposed method. For validation of the segmentation results, segmented CT ?-maps were considered to the "silver standard"; the segmentation accuracy was assessed qualitatively and quantitatively through calculation of the Dice similarity coefficient (DSC). Relative change (RC) maps between the CT and MRI-based attenuation corrected PET volumes were also calculated for a global voxel-wise assessment of the reconstruction results. The ?-maps obtained using the Atlas-T1w-DUTE classifier agreed well with those derived from CT; the mean DSCs for the Atlas-T1w-DUTE-based ?-maps across all subjects were higher than those for DUTE-based ?-maps; the atlas-based ?-maps also showed a lower percentage of misclassified voxels across all subjects. RC maps from the atlas-based technique also demonstrated improvement in the PET data compared to the DUTE method, both globally as well as regionally.
View details for PubMedID 24753982
View details for PubMedCentralID PMC3992209