Faculty Bibliography

INTRODUCTION: Stereotactic ablative radiotherapy (SABR) offers a curative treatment for lung cancer in patients who are marginal surgical candidates. However, unlike traditional surgery the lung cancer remains in place after treatment. Thus, imaging follow-up for evaluation of recurrence is of paramount importance. MATERIALS AND METHODS: In this retrospective designed Institutional Review Board-approved study, follow-up contrast-enhanced computed tomography (CT) exams were performed on sixty one patients to evaluate enhancement pattern in the ablation zone at 1, 3, 6, and 12 months after SABR. RESULTS: Eleven patients had recurrence within the ablation zone after SABR. The postcontrast enhancement in the recurrence group showed a washin and washout phenomenon, whereas the radiation-induced lung injury group showed continuous enhancement suggesting an inflammatory process. CONCLUSIONS: The textural feature of the ablation zone of enhancement and perfusion as demonstrated in computed tomography nodule enhancement may allow early differentiation of recurrence from radiation-induced lung injury in patients' status after SABR or primary lung cancer.

To reduce radiation dose in X-ray computed tomography (CT) imaging, one common strategy is to lower the tube current and exposure time settings during projection data acquisition. However, this strategy would inevitably increase the projection data noise, and the resulting image by the conventional filtered back-projection (FBP) method may suffer from excessive noise and streak artifacts. The well-known edge-preserving nonlocal means (NLM) filtering can reduce the noise-induced artifacts in the FBP reconstructed image, but it sometimes cannot completely eliminate the artifacts, especially under the very low-dose circumstance when the image is severely degraded. Instead of taking NLM filtering, we proposed a NLM-regularized statistical image reconstruction scheme, which can effectively suppress the noise-induced artifacts and significantly improve the reconstructed image quality. From our previous investigation on NLM-based strategy, we noted that using a spatially invariant filtering parameter in the regularization was rarely optimal for the entire field of view (FOV). Therefore, in this study we developed a novel strategy for designing spatially variant filtering parameters which are adaptive to the local characteristics of the image to be reconstructed. This adaptive NLM-regularized statistical image reconstruction method was evaluated with low-contrast phantoms and clinical patient data to show (1) the necessity in introducing the spatial adaptivity and (2) the efficacy of the adaptivity in achieving superiority in reconstructing CT images from low-dose acquisitions.

OBJECTIVE: The objective of this study was to assess the potential of anterior in beam bismuth shields placed on the abdomen and the pelvis of a RANDO phantom to decrease radiation dose to a simulated fetus. METHODS: Thermal luminescent dosimeters irradiated in a RANDO phantom were used to measure radiation dose at 0 weeks and 18 weeks after conception. An 18-week gestation was simulated by adding the corresponding equivalent of super-stuff to account for additional subcutaneous tissue. To assess image quality, a 461A body phantom with low contrast resolution phantom insert was used. The minimum size of low contrast resolution target visualize was recorded with no shielding and 25-cm-wide shielding. RESULTS: With shields, there was a radiation dose reduction of 19% to 23% at 0-week gestation and 16% to 24% at 18-week gestation. No qualitative difference was found in low contrast detectability with the shields. CONCLUSIONS: These shields reduce fetal radiation exposure early in pregnancy with acceptable image quality.

PURPOSE: To evaluate retrospectively the long-term survival of patients with early-stage non-small cell lung cancer (NSCLC) treated with cryoablation. MATERIALS AND METHODS: Cryoablation was performed on 47 T1N0M0 NSCLCs in 45 consecutive patients between 2006 and January 2011. All ablative procedures were performed with 16-gauge or 13-gauge cryoprobes. The number of probes used was determined by the size and geometry of the tumor. Local and regional recurrence rates were monitored. Complications were assessed by the Society of Interventional Radiology (SIR) classification system. RESULTS: The 5-year survival rate was 67.8% +/- 15.3, the cancer-specific survival rate at 5 years was 56.6% +/- 16.5, and the 5-year progression-free survival rate was 87.9% +/- 9. The probe per unit tumor diameter was 1.4 probes/cm. In eight patients, 16-gauge cryoprobes were used. Two cases were performed with a single needle. The remaining cases were performed with 13-gauge cryoprobes except for one case in which both probe sizes were used. The combined local and regional recurrence rate was 36.2%. Major complications occurred in 6.4% of patients, including two cases of hemoptysis and a prolonged placement of a chest tube requiring mechanical sclerosis in one patient. There were no deaths in the first 30 days after treatment. CONCLUSIONS: Cryoablation is associated with a good overall long-term survival with minimally significant complications. Cryoablation is a potentially curative, viable therapeutic option for patients with stage I NSCLC who are deemed medically inoperable.

Computer-aided detection (CADe) of pulmonary nodules is critical to assisting radiologists in early identification of lung cancer from computed tomography (CT) scans. This paper proposes a novel CADe system based on a hierarchical vector quantization (VQ) scheme. Compared with the commonly-used simple thresholding approach, the high-level VQ yields a more accurate segmentation of the lungs from the chest volume. In identifying initial nodule candidates (INCs) within the lungs, the low-level VQ proves to be effective for INCs detection and segmentation, as well as computationally efficient compared to existing approaches. False-positive (FP) reduction is conducted via rule-based filtering operations in combination with a feature-based support vector machine classifier. The proposed system was validated on 205 patient cases from the publically available online Lung Image Database Consortium database, with each case having at least one juxta-pleural nodule annotation. Experimental results demonstrated that our CADe system obtained an overall sensitivity of 82.7% at a specificity of 4 FPs/scan. Especially for the performance on juxta-pleural nodules, we observed 89.2% sensitivity at 4.14 FPs/scan. With respect to comparable CADe systems, the proposed system shows outperformance and demonstrates its potential for fast and adaptive detection of pulmonary nodules via CT imaging.

Differentiation of malignant and benign pulmonary nodules is of paramount clinical importance. Texture features of pulmonary nodules in CT images reflect a powerful character of the malignancy in addition to the geometry-related measures. This study first compared three well-known types of two-dimensional (2D) texture features (Haralick, Gabor, and local binary patterns or local binary pattern features) on CADx of lung nodules using the largest public database founded by Lung Image Database Consortium and Image Database Resource Initiative and then investigated extension from 2D to three-dimensional (3D) space. Quantitative comparison measures were made by the well-established support vector machine (SVM) classifier, the area under the receiver operating characteristic curves (AUC) and the p values from hypothesis t tests. While the three feature types showed about 90% differentiation rate, the Haralick features achieved the highest AUC value of 92.70% at an adequate image slice thickness, where a thinner or thicker thickness will deteriorate the performance due to excessive image noise or loss of axial details. Gain was observed when calculating 2D features on all image slices as compared to the single largest slice. The 3D extension revealed potential gain when an optimal number of directions can be found. All the observations from this systematic investigation study on the three feature types can lead to the conclusions that the Haralick feature type is a better choice, the use of the full 3D data is beneficial, and an adequate tradeoff between image thickness and noise is desired for an optimal CADx performance. These conclusions provide a guideline for further research on lung nodule differentiation using CT imaging.

Graphene nanoparticle dispersions show immense potential as multifunctional agents for in vivo biomedical applications. Herein, we follow regulatory guidelines for pharmaceuticals that recommend safety pharmacology assessment at least 10-100 times higher than the projected therapeutic dose, and present comprehensive single dose response, expanded acute toxicology, toxicokinetics, and respiratory/cardiovascular safety pharmacology results for intravenously administered dextran-coated graphene oxide nanoplatelet (GNP-Dex) formulations to rats at doses between 1 and 500 mg/kg. Our results indicate that the maximum tolerable dose (MTD) of GNP-Dex is between 50 mg/kg /=250 mg/kg in the heart, liver, lung, spleen, and kidney; we found no changes in the brain and no GNP-Dex related effects in the cardiovascular parameters or hematological factors (blood, lipid, and metabolic panels) at doses < 125 mg/kg. The results open avenues for pivotal preclinical single and repeat dose safety studies following good laboratory practices (GLP) as required by regulatory agencies for investigational new drug (IND) application.

PURPOSE: Repeated computed tomography (CT) scans are required for some clinical applications such as image-guided interventions. To optimize radiation dose utility, a normal-dose scan is often first performed to set up reference, followed by a series of low-dose scans for intervention. One common strategy to achieve the low-dose scan is to lower the x-ray tube current and exposure time (mAs) or tube voltage (kVp) setting in the scanning protocol, but the resulted image quality by the conventional filtered back-projection (FBP) method may be severely degraded due to the excessive noise. Penalized weighted least-squares (PWLS) image reconstruction has shown the potential to significantly improve the image quality from low-mAs acquisitions, where the penalty plays an important role. In this work, the authors' explore an adaptive Markov random field (MRF)-based penalty term by utilizing previous normal-dose scan to improve the subsequent low-dose scans image reconstruction. METHODS: In this work, the authors employ the widely-used quadratic-form MRF as the penalty model and explore a novel idea of using the previous normal-dose scan to obtain the MRF coefficients for adaptive reconstruction of the low-dose images. In the coefficients determination, the authors further explore another novel idea of using the normal-dose scan to obtain a scale map, which describes an optimal neighborhood for the coefficients determination such that a local uniform region has a small spread of frequency spectrum and, therefore, a small MRF window, and vice versa. The proposed penalty term is incorporated into the PWLS image reconstruction framework, and the low-dose images are reconstructed via the PWLS minimization. RESULTS: The presented adaptive MRF based PWLS algorithm was validated by physical phantom and patient data. The experimental results demonstrated that the presented algorithm is superior to the PWLS reconstruction using the conventional Gaussian MRF penalty or the edge-preserving Huber penalty and the conventional FBP method, in terms of image noise reduction and edge/detail/contrast preservation. CONCLUSIONS: This study demonstrated the feasibility and efficacy of the proposed scheme in utilizing previous normal-dose CT scan to improve the subsequent low-dose scans.

Previous studies have shown that by minimizing the total variation (TV) of the to-be-estimated image with some data and/or other constraints, a piecewise-smooth X-ray computed tomography image can be reconstructed from sparse-view projection data. However, due to the piecewise constant assumption for the TV model, the reconstructed images are frequently reported to suffer from the blocky or patchy artifacts. To eliminate this drawback, we present a total variation-stokes-projection onto convex sets (TVS-POCS) reconstruction method in this paper. The TVS model is derived by introducing isophote directions for the purpose of recovering possible missing information in the sparse-view data situation. Thus the desired consistencies along both the normal and the tangent directions are preserved in the resulting images. Compared to the previous TV-based image reconstruction algorithms, the preserved consistencies by the TVS-POCS method are expected to generate noticeable gains in terms of eliminating the patchy artifacts and preserving subtle structures. To evaluate the presented TVS-POCS method, both qualitative and quantitative studies were performed using digital phantom, physical phantom and clinical data experiments. The results reveal that the presented method can yield images with several noticeable gains, measured by the universal quality index and the full-width-at-half-maximum merit, as compared to its corresponding TV-based algorithms. In addition, the results further indicate that the TVS-POCS method approaches to the gold standard result of the filtered back-projection reconstruction in the full-view data case as theoretically expected, while most previous iterative methods may fail in the full-view case because of their artificial textures in the results.