The FPD provides radiographic images with excellent inherent physical image quality.

The information provided by functional images may be used to guide radiotherapy planning by identifying regions that require higher radiation dose. In this work we investigate the dosimetric feasibility of delivering dose to lung tumors in proportion to the fluorine-18-fluorodeoxyglucose activity distribution from positron emission tomography (FDG-PET). The rationale for delivering dose in proportion to the tumor FDG-PET activity distribution is based on studies showing that FDG uptake is correlated to tumor cell proliferation rate, which is shown to imply that this dose delivery strategy is theoretically capable of providing the same duration of local control at all voxels in tumor. Target dose delivery was constrained by single photon emission computed tomography (SPECT) maps of normal lung perfusion, which restricted irradiation of highly perfused lung and imposed dose-function constraints. Dose-volume constraints were imposed on all other critical structures. All dose-volume/function constraints were considered to be soft, i.e., critical structure doses corresponding to volume/function constraint levels were minimized while satisfying the target prescription, thus permitting critical structure doses to minimally exceed dose constraint levels. An intensity modulation optimization methodology was developed to deliver this radiation, and applied to two lung cancer patients. Dosimetric feasibility was assessed by comparing spatially normalized dose-volume histograms from the nonuniform dose prescription (FDG-PET proportional) to those from a uniform dose prescription with equivalent tumor integral dose. In both patients, the optimization was capable of delivering the nonuniform target prescription with the same ease as the uniform target prescription, despite SPECT restrictions that effectively diverted dose from high to low perfused normal lung. In one patient, both prescriptions incurred similar critical structure dosages, below dose-volume/function limits. However, in the other patient, critical structure dosage from the nonuniform dose prescription exceeded dose-volume/function limits, and greatly exceeded that from the uniform dose prescription. Strict compliance to dose-volume/ function limits would entail reducing dose proportionality to the FDG-PET activity distribution, thereby theoretically reducing the duration of local control. Thus, even though it appears feasible to tailor lung tumor dose to the FDG-PET activity distribution, despite SPECT restrictions, strict adherence to dose-volume/function limits could compromise the effectiveness of functional image guided radiotherapy.

In previous research, we have developed a computer-aided detection (CAD) system designed to detect masses in mammograms. The previous version of our system employed a simple but imprecise method to localize the masses. In this research, we present a more robust segmentation routine for use with mammographic masses. Our hypothesis is that by more accurately describing the morphology of the masses, we can improve the CAD system's ability to distinguish masses from other mammographic structures. To test this hypothesis, we incorporated the new segmentation routine into our CAD system and examined the change in performance. The developed iterative, linear segmentation routine is a gray level-based procedure. Using the identified regions from the previous CAD system as the initial seeds, the new segmentation algorithm refines the suspicious mass borders by making estimates of the interior and exterior pixels. These estimates are then passed to a linear discriminant, which determines the optimal threshold between the interior and exterior pixels. After applying the threshold and identifying the object's outline, two constraints on the border are applied to reduce the influence of background noise. After the border is constrained, the process repeats until a stopping criterion is reached. The segmentation routine was tested on a study database of 183 mammographic images extracted from the Digital Database for Screening Mammography. Eighty-three of the images contained 50 malignant and 50 benign masses; 100 images contained no masses. The previously developed CAD system was used to locate a set of suspicious regions of interest (ROIs) within the images. To assess the performance of the segmentation algorithm, a set of 20 features was measured from the suspicious regions before and after the application of the developed segmentation routine. Receiver operating characteristic (ROC) analysis was employed on the ROIs to examine the discriminatory capabilities of each individual feature before and after the segmentation routine. A statistically significant performance increase was found in many of the individual features, particularly those describing the mass borders. To examine how the incorporation of the segmentation routine affected the performance of the overall CAD system, free-response ROC (FROC) analysis was employed. When considering only malignant masses, the FROC performance of the system with the segmentation routine appeared better than the previous system. When detecting 90% of the malignant masses, the previous system achieved 4.9 false positives per image (FPpI) compared to the post-segmentation system's 4.2 FPpI. At 80% sensitivity, the respective FPpI were 3.5 and 1.6.

Bi-plane correlation imaging (BCI) is a new imaging approach that utilizes angular information from a bi-plane digital acquisition in conjunction with computer assisted detection (CAD) to reduce the degrading influence of anatomical noise in the detection of subtle lesions in planar images. An anthropomorphic chest phantom, supplemented with added nodule phantoms (5-13 mm at the image plane), was imaged from different posterior projections within a ±12 o range by moving the x-ray tube vertically and horizontally with respect to the detector. Each image was analyzed using a basic front-end single-view CAD algorithm. The correlation of the suspect lesions from the PA view with those from each of the oblique views was examined using a priori knowledge of the acquisition geometry. The correlated suspect lesions were registered as positive. Using an optimum -3 o vertical geometry and processing parameters, BCI resulted in 62.5% sensitivity, 1.5 FP/image, and 0.885 PPV. The corresponding values from the observer experiment were 56% sensitivity, 10.8 FP/image, and 0.45 PPV, respectively. Compared to single-view CAD results, the BCI reduced sensitivity by 20%. However, the corresponding reduction in FPs was notably higher (94%) leading to 140% improvement in the PPV. Changes in processing parameters could result in higher PPV and lower FP/image at the expense of lower sensitivity. Similar findings were indicated for small (5-9 mm) and large (9-13 mm) nodules, but the relative improvement was significantly higher for smaller nodules. (The research was supported by a grant from the NIH, R21CA91806.)