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Rationale and Objectives: Studies that evaluate the lung nodule detection performance of radiologists or computerized methods depend on an initial inventory of the nodules within the thoracic images (the "truth"). The purpose of this study was to analyze (1) variability in the "truth" defined by different combinations of experienced thoracic radiologists and (2) variability in the performance of other experienced thoracic radiologists based on these definitions of "truth" in the context of lung nodule detection in computed tomographic (CT) scans.Materials and Methods: Twenty-five thoracic CT scans were reviewed by four thoracic radiologists, who independently marked lesions they considered to be nodules >or=3 mm in maximum diameter. Panel "truth" sets of nodules were then derived from the nodules marked by different combinations of two and three of these four radiologists. The nodule detection performance of the other radiologists was evaluated based on these panel "truth" sets.Results: The number of "true" nodules in the different panel "truth" sets ranged from 15 to 89 (mean 49.8 +/- 25.6). The mean radiologist nodule detection sensitivities across radiologists and panel "truth" sets for different panel "truth" conditions ranged from 51.0 to 83.2%; mean false-positive rates ranged from 0.33 to 1.39 per case.Conclusions: Substantial variability exists across radiologists in the task of lung nodule identification in CT scans. The definition of "truth" on which lung nodule detection studies are based must be carefully considered, because even experienced thoracic radiologists may not perform well when measured against the "truth" established by other experienced thoracic radiologists. [ABSTRACT FROM AUTHOR]

Objectives: Precision medicine requires the measurement, quantification, and cataloging of medical characteristics to identify the most effective medical intervention. However, the amount of available data exceeds our current capacity to extract meaningful information. We examine the informatics needs to achieve precision medicine from the perspective of quantitative imaging and oncology. Methods: The National Cancer Institute (NCI) organized several workshops on the topic of medical imaging and precision medicine. The observations and recommendations are summarized herein. Results: Recommendations include: use of standards in data collection and clinical correlates to promote interoperability; data sharing and validation of imaging tools; clinician's feedback in all phases of research and development; use of open-source architecture to encourage reproducibility and reusability; use of challenges which simulate real-world situations to incentivize innovation; partnership with industry to facilitate commercialization; and education in academic communities regarding the challenges involved with translation of technology from the research domain to clinical utility and the benefits of doing so. Conclusions: This article provides a survey of the role and priorities for imaging informatics to help advance quantitative imaging in the era of precision medicine. While these recommendations were drawn from oncology, they are relevant and applicable to other clinical domains where imaging aids precision medicine., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Georg Thieme Verlag KG Stuttgart.)

The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.

The purpose of this editorial is to provide a brief history of National Institutes of Health National Cancer Institute (NCI) workshops as related to quantitative imaging within the oncology setting. The editorial will then focus on the recently supported NCI initiatives, including the Quantitative Imaging Network (QIN) initiative and its organizational structure, including planned research goals and deliverables. The publications in this issue of Translational Oncology come from many of the current members of this QIN research network.

Rationale and Objectives: Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish "truth" for algorithm development, training, and testing. The integrity of this "truth," however, must be established before investigators commit to this "gold standard" as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the "truth" collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database., Materials and Methods: One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the "blinded read phase"), radiologists independently identified and annotated lesions, assigning each to one of three categories: "nodule >or=3 mm," "nodule <3 mm," or "non-nodule >or=3 mm." For the second read (the "unblinded read phase"), the same radiologists independently evaluated the same CT scans, but with all of the annotations from the previously performed blinded reads presented; each radiologist could add to, edit, or delete their own marks; change the lesion category of their own marks; or leave their marks unchanged. The post-unblinded read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of identification of potential errors introduced during the complete image annotation process and correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional., Results: A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process., Conclusions: The establishment of "truth" must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems.

Rationale and Objectives: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers., Materials and Methods: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading., Results: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future., Conclusions: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

Rationale and Objectives: The goal was to investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, because the latter are always qualified with respect to a given size range of nodules., Materials and Methods: This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium (LIDC) using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a unidimensional and two bidimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the interradiologist variation, while those with at least one marking were used to examine the difference between the metrics., Results: The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high interobserver variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges for the three-dimensional, unidimensional, and two bidimensional size metrics, respectively (in mm): 0.49-1.25, 0.67-2.55, 0.78-2.11, and 0.96-2.69. Also, a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on unidimensional, and the two bidimensional size measurements of 10 mm were 7.32, 7.72, and 6.29 mm, respectively., Conclusions: The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets.

Rationale and Objectives: The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on computed tomography (CT) scans and thereby to investigate variability in the establishment of the "truth" against which nodule-based studies are measured., Materials and Methods: Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial "blinded read" phase, radiologists independently marked lesions they identified as "nodule >or=3 mm (diameter)," "nodule <3 mm," or "non-nodule >or=3 mm." During the subsequent "unblinded read" phase, the blinded read results of all four radiologists were revealed to each radiologist, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist's own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus., Results: After the initial blinded read phase, 71 lesions received "nodule >or=3 mm" marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. After the unblinded reads, a total of 59 lesions were marked as "nodule >or=3 mm" by at least one radiologist. Twenty-seven (45.8%) of these lesions received such marks from all four radiologists, three (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist., Conclusion: The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules >or=3 mm. Nevertheless, substantial variability remains across radiologists in the task of lung nodule identification.

Rationale and Objectives: Integral to the mission of the National Institutes of Health-sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary., Materials and Methods: The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists' spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects., Results: Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively., Conclusion: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume.

A group of experts on very large databases, quantitative imaging, data format standards development, image management and communications, and related technologies for cancer imaging met at a recent workshop sponsored by the BIP and discussed the key issues confronting this field. The BIP received recommendations regarding steps that can be taken to advance the technology and take advantage of the opportunities to improve collaboration and utility in cancer imaging. There are tremendous opportunities to change radically the way we use image information. These opportunities are most obvious in clinical research, in which we seek to advance the dissemination and use of cancer image data in research and practice. Important opportunities and new modes of information use are provided by supporting the entire "information cycle" of creation, dissemination, and collaboration in addition to image organization, access, presentation, and preservation. To learn more, visit the BIP Web site at www3 .cancer.gov/bip/steer&#95;iasc.htm and join the image archive listserver at the NIH (ARCHIVE-COMM-L, available online at list.nihgov). These resources are open and available to all.

Preliminary clinical studies suggest that spiral computed tomography (CT) of the lungs can improve early detection of lung cancer in high-risk individuals. More clinical studies are needed, however, before public health recommendations can be proposed for population-based screening. Spiral CT generates large-volume data sets and thus poses problems in terms of implementation of efficient and cost-effective screening methods. Image processing algorithms such as computer assisted diagnostic (CAD) methods have the potential to assist in lesion (eg, nodule) detection on spiral CT studies. CAD methods may also be used to characterize nodules by either assessing the stability or change in size of lesions based on evaluation of serial CT studies, or quantitatively measuring the temporal parameters related to contrast dynamics when using contrast material-enhanced CT studies. CAD methods therefore have the potential to enhance the sensitivity and specificity of spiral CT lung screening studies. Lung cancer screening studies now under investigation create an opportunity to develop an image database that will allow comparison and optimization of CAD algorithms. This database could serve as an important national resource for the academic and industrial research community that is currently involved in the development of CAD methods. The National Cancer Institute request for applications (RFA) (CA-01-001) has already been announced (April 2000) to establish and support a consortium of academic centers to develop this database, the consortium to be referred to as the Lung Image Database Consortium (LIDC). This RFA is now closed. Five academic sites have been selected to be members of the LIDC, the first meeting of this consortium is planned for spring of 2001, and a public meeting is to be held in 2002. This report is abstracted from the previously published RFA to serve as an example of how an initiative is developed by the National Cancer Institute to support a research resource. For specific details of the RFA, please access the following Internet site: http://www. nci.nih.gov/bip/NCI-DIPinisumm.htm#a11.

Rationale and Objectives: The authors evaluated an algorithm for the automatic segmentation of microcalcification clusters (MCCs) at digital mammography. Two- and four-channel wavelet transforms were evaluated to determine whether sensitivity in the detection of MCCs can be improved and if the selective reconstruction of the higher-order M2 subimages allows better preservation of the segmented MCCs, which is required for their classification., Materials and Methods: The hybrid method involved the use of a nonlinear filter for image noise suppression coupled with wavelet transforms for image decomposition and an adaptive method for selective subimage reconstruction as a basis for segmentation of MCCs. The two- and four-channel wavelet transforms were implemented with different filter bank structures (i.e., polyphase quadrature mirror filters [QMFs], tree structure, and lattice structure) to determine if their computational efficiency can be improved while retaining properties such as near-perfect reconstruction. The hybrid wavelet transforms were applied to a common image database of biopsy-proved MCCs (100 images, 105-micron resolution, 12 bits deep; 52 cases with at least one MCC of varying subtlety [46 malignant and six benign cases] and eight normal cases)., Results: The two- and four-channel wavelet transforms yielded sensitivities of 93% and 94% and false-positive (PP) detection rates of 1.58 and 1.35 MCCs per image, respectively. The lattice structure provided greater than fivefold improvement in computational speed compared to the polyphase QMF structure, particularly for the higher order of channels (M = 4)., Conclusion: The four-channel wavelet transform provided better sensitivity and FP detection rates and greater image detail preservation for the segmented MCCs.

Rationale and Objectives: The authors evaluated computer-assisted diagnostic (CAD) methods used to detect suspicious areas on lung radiographs., Materials and Methods: The authors designed a fragmentary window filtering (FWF) algorithm for detecting lung nodule patterns, which generally appear as circular areas of high opacity on the chest radiograph. The FWF algorithm helps differentiate circular patterns from overlapping radiographic background. A multiscale analysis was performed to locate multiscale nodules. Receiver operating characteristic analysis was performed by using a lung nodule that was extracted from a chest radiograph. The nodule underwent scalings and subsequent superimposition onto 140 normal regions of interest from six chest radiographs., Results: The FWF method was superior to the matched filtering method in the detection of suspicious areas., Conclusion: The proposed FWF-based method should provide improved detection of lung nodules on chest radiographs.

Rationale and Objectives: The authors evaluated a modular computer-assisted diagnosis (CAD) method for mass detection that uses computation of features in three domains (gray level, morphology, and directional texture). Their objectives were to improve the sensitivity of detection and reduce the false-positive (FP) detection rate., Materials and Methods: The directional wavelet transform (DWT) method, which uses both multiorientation and multiresolution wavelet transforms to improve image preprocessing and segmentation of suspicious areas and to extract both morphologic and directional texture features, was evaluated with a previously reported image database containing 50 normal and 45 abnormal digitized screen-film mammograms. The mammograms contained all mass types and included 16 minimal cancers. This method was compared with the Markov random field (MRF) method to avoid issues related to case selection criteria. Free-response receiver operating characteristic curves were compared for both DWT and MRF methods., Results: For the DWT method, the sensitivity was 98% and the FP detection rate was 1.8 FP findings per image. For the MRF method, the sensitivity was 90% and the FP detection rate was 2.0 FP findings per image., Conclusion: The CAD method applied to the full mammographic image is automatic and independent of mass type. The segmentation of masses as performed with this method may potentially allow visual interpretation according to American College of Radiology criteria.

A multiresolution statistical method for identifying clinically normal tissue in digitized mammograms is used to construct an algorithm for separating normal regions from potentially abnormal regions; that is, small regions that may contain isolated calcifications. This is the initial phase of the development of a general method for the automatic recognition of normal mammograms. The first step is to decompose the image with a wavelet expansion that yields a sum of independent images, each containing different levels of image detail. When calcifications are present, there is strong empirical evidence that only some of the image components are necessary for the purpose of detecting a deviation from normal. The underlying statistic for each of the selected expansion components can be modeled with a simple parametric probability distribution function. This function serves as an instrument for the development of a statistical test that allows for the recognition of normal tissue regions. The distribution function depends on only one parameter, and this parameter itself has an underlying statistical distribution. The values of this parameter define a summary statistic that can be used to set detection error rates. Once the summary statistic is determined, spatial filters that are matched to resolution are applied independently to each selected expansion image. Regions of the image that correlate with the normal statistical model are discarded and regions in disagreement (suspicious areas) are flagged. These results are combined to produce a detection output image consisting only of suspicious areas. This type of detection output is amenable to further processing that may ultimately lead to a fully automated algorithm for the identification of normal mammograms.

Rationale and Objectives: The acceptance of filmless digital mammography is currently limited by digitization and display drawbacks, as well as bias toward hard-copy interpretation. In the current study, we evaluated a wavelet-based image enhancement method for the filmless interpretation of breast calcifications., Methods: A set of 100 mammograms (58 with calcification clusters) was digitized at 105 microns and 4,096 gray levels per pixel and was processed with nonlinear filters and wavelets. Standard receiver operating characteristic analysis was performed by four radiologists, who independently read the films, the unprocessed digital images, and unprocessed and wavelet-enhanced digital images presented simultaneously., Results: Statistical differences were observed between screen/film and unprocessed digitized mammography displayed on monitors. Differences were not significant when wavelet enhancement was included in the monitor display. Interobserver variation in the digitized reading was greater than in film reading, but the wavelet enhancement reduced the difference., Conclusion: Wavelet-enhanced digital mammograms may assist radiologists in diagnosing calcifications directly from computer monitors and may compensate for current technologic limitations. A study with a larger data-base is needed before this method is accepted for clinical use.

A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCCs) in digitized mammograms. The MFNN employs features computed in both the spatial and spectral domain and uses spectral entropy as a decision parameter. Backpropagation with Kalman filtering (KF) is employed to allow more efficient network training as required for evaluation of different features, input images, and related error analysis. A previously reported, wavelet-based image-enhancement method is also employed to enhance microcalcification clusters for improved detection. The relative performance of the MFNN for both the raw and enhanced images is evaluated using a common image database of 30 digitized mammograms, with 20 images containing 21 biopsy proven MCCs and ten normal cases. The computed sensitivity (true positive (TP) detection rate) was 90.1% with an average low false positive (FP) detection of 0.71 MCCs/image for the enhanced images using a modified k-fold validation error estimation technique. The corresponding computed sensitivity for the raw images was reduced to 81.4% and with 0.59 FP's MCCs/image. A relative comparison to an earlier neural network (NN) design, using only spatially related features, suggests the importance of the addition of spectral domain features when the raw image data is analyzed.

A technique is proposed for the detection of tumors in digital mammography. Detection is performed in two steps: segmentation and classification. In segmentation, regions of interest are first extracted from the images by adaptive thresholding. A further reliable segmentation is achieved by a modified Markov random field (MRF) model-based method. In classification, the MRF segmented regions are classified into suspicious and normal by a fuzzy binary decision tree based on a series of radiographic, density-related features. A set of normal (50) and abnormal (45) screen/film mammograms were tested. The latter contained 48 biopsy proven, malignant masses of various types and subtlety. The detection accuracy of the algorithm was evaluated by means of a free response receiver operating characteristic curve which shows the relationship between the detection of true positive masses and the number of false positive alarms per image. The results indicated that a 90% sensitivity can be achieved in the detection of different types of masses at the expense of two falsely detected signals per image. The algorithm was notably successful in the detection of minimal cancers manifested by masses

Multiresolution methods are reported for feature extraction in breast cancer screening using digital mammography. The initial application is directed at the detection of microcalcification clusters (MCCs). Quadrature mirror filter (QMF) banks, using both two and three channel are proposed for the first time for both multiresolution decomposition and reconstruction. These filters are specifically tailored for automatic extraction of MCCs. The QMF multiresolution methods are compared to two channel tree structured wavelet transforms (TSWTs) methods previously reported. The QMF filters are preceded by an advanced tree structured nonlinear filter for noise suppression, prior to feature extraction, in order to minimize the false positive (FP) detection rate in digital mammography. The relative performance of these methods were evaluated using both simulated images and fifteen representative digitized mammograms containing biopsy proven microcalcification clusters. Similar high sensitivity (true positive (TP) detection rate (100%) and high specificity (0.6 average false positive (FP) MCC's/image) were observed, substantially better than more traditional approaches using single scale filters. The three channel QMF method, however, demonstrated better detail preservation of MCC's extracted compared to the two channel method. Detail preservation is important for the characterization of MCC's or individual microcalcifications in cancer screening.

A new class of nonlinear filters with more robust characteristics for noise suppression and detail preservation is proposed for processing digital mammographic images. The new algorithm consists of two major filtering blocks: (a) a multistage tree-structured filter for image enhancement that uses central weighted median filters as basic sub-filtering blocks and (b) a dispersion edge detector. The design of the algorithm also included the use of linear and curved windows to determine whether variable shape windowing could improve detail preservation. First, the noise-suppressing properties of the tree-structured filter were compared to single filters, namely the median and the central weighted median with conventional square and variable shape adaptive windows; simulated images were used for this purpose. Second, the edge detection properties of the tree-structured filter cascaded with the dispersion edge detector were compared to the performance of the dispersion edge detector alone, the Sobel operator, and the single median filter cascaded with the dispersion edge detector. Selected mammographic images with representative biopsy-proven malignancies were processed with all methods and the results were visually evaluated by an expert mammographer. In all applications, the proposed filter suggested better detail preservation, noise suppression, and edge detection than all other approaches and it may prove to be a useful tool for computer-assisted diagnosis in digital mammography.

An order statistic and neural network hybrid filter (OSNNH) is proposed for the restoration of gamma camera images using the measured modulation transfer function. Planar images of beta-emitting radionuclides are used to evaluate the filter because they exhibit higher degradation than images of single photon emitters due to increased photon scattering and collimator septal penetration. The filter performance is quantitatively evaluated and compared to that of the Wiener filter by investigating the relationship between the externally measured counts from sources of phosphorous-32 ((32)P) at various depths in water. An effective linear attenuation coefficient for (32)P is determined to be equal to 0.13 cm(-1) and 0.14 cm(-1) for the OSNNH and the Wiener filters, respectively. Evaluation of phantom and patient filtered images demonstrates that the OSNNH filter avoids ring effects caused by the ill-conditioned blur matrix and noise overriding caused by matrix inversion, typical of other restoration filters such as the Wiener.

Local thresholding and region-growing algorithms are developed and applied to digitized mammograms to quantify the parenchymal densities. The algorithms are first evaluated and optimized on phantom images reflecting varying image contrast, X-ray exposure conditions, and time-related changes. The difference between the segmentation results of the two techniques is less than 6% on the phantom images and 11% on the mammograms. The agreement between the computerized procedures and a manual one is in the range of 74-98%, depending on the breast parenchymal pattern and segmentation algorithm. The results show that computerized parenchymal classification of digitized mammograms is possible and independent of exposure.

Magnetic resonance (MR) brain section images are segmented and then synthetically colored to give visual representations of the original data with three approaches: the literal and approximate fuzzy c-means unsupervised clustering algorithms, and a supervised computational neural network. Initial clinical results are presented on normal volunteers and selected patients with brain tumors surrounded by edema. Supervised and unsupervised segmentation techniques provide broadly similar results. Unsupervised fuzzy algorithms were visually observed to show better segmentation when compared with raw image data for volunteer studies. For a more complex segmentation problem with tumor/edema or cerebrospinal fluid boundary, where the tissues have similar MR relaxation behavior, inconsistency in rating among experts was observed, with fuzz-c-means approaches being slightly preferred over feedforward cascade correlation results. Various facets of both approaches, such as supervised versus unsupervised learning, time complexity, and utility for the diagnostic process, are compared.

Quantitative imaging of bremsstrahlung from pure beta emitters is proposed as a means for in vivo management of antibody therapy. The method involves the use of high-energy collimation, an empirically selected broad photon energy window to enhance detector sensitivity, and a Wiener restoration filter to compensate for system blur. The measured and filtered data were obtained for an idealized scattering medium and isolated spherical sources. An effective linear attenuation coefficient of about 0.13 cm-1 was determined from the raw image data of 32P. A coefficient of 0.14 cm-1 was determined after the images were restored using the Wiener filter. The measured attenuation was not significantly dependent on the size of the region of interest or the size of the source. Its variation was within the experimental error of measurement (+/- 5%). The measured sensitivity (6 x 10(-6) cps/Bq) was sufficient for imaging therapy doses of 32P or 90Y.

Iodine-123 (159 keV, T1/2 = 13.3 hr) has been proposed for renal investigations, as opposed to I-131 (364 keV, T1/2 = 8.06 days), because of its more practical photon energy and lower radiation dose to the patient. The cyclotron production method 124Te (p,2n) 123I for I-123 results in contamination with I-124 (T1/2 = 4.5 days). The latter emits high-energy photons whose relative abundance increases with time after end of bombardment (EOB). This paper is an evaluation of the effects of photon penetration, scatter, and attenuation on the phantom calibration measurements required for determining relative renal uptake using I-123. Measurements using I-131 were performed for comparison. Parameters investigated included: (a) the relationship between the integrated count and ROI size, (b) the magnitude of the "cross-talk" in counts between the kidneys, and (c) the attenuation corrections for source (kidney) depth. Phantom results obtained for I-123 suggest that this radionuclide will allow a better measurement of the activity in individual kidneys. Collimator penetration effects were greater for I-131 than for I-123. With I-123 for example, a higher fraction of the counts due to the activity in the kidney phantom were observed within the ROI enclosing its image. However, the attenuation corrections for source depth for I-123 were dependent on both the size of the ROI and time after EOB.

A theoretical and experimental analysis of the application of pulsed proton nuclear magnetic resonance (NMR) to the measurement of fluid flow has been made. Calculations of the detector signal line shapes, S(t), for a single-coil NMR laboratory experiment, were performed using a computer to identify the optimum choice of flow signature. The theoretical model included the influence of coil-field shape, coil length, and effects of flow phase. High flow rates, typical of arterial blood flow, were simulated. Good correlation between theory and experiment was observed. The maximum amplitude of the slope of the signal [dS(t)/dt] was found to be the optimum flow signature. The line shape S(t) was found to depend on flow phase. Comparison between theory and experiment provided a better understanding of the potential of NMR for measuring fluid or blood flow.

The response characteristics of two tomographic systems were compared for imaging of positron emitters: a) a SPECT system with a 3/8 in crystal and 511 keV detector shielding, equipped with a specially designed 511 keV collimator, and b) a PET V system using coincidence detection. SPECT transverse plane resolution was 19 mm FWHM and 35 mm FWTM for a radius of rotation of 16 cm. Corresponding resolution for PET was 14 mm FWHM and 28 mm FWTM. Transverse images through a phantom containing cylindrical sources of various cross sections and uniform activity were obtained for each detector. The measured count density or recovery coefficient was found to decrease with source size, the dependence being similar for both systems. The theoretical values for recovery coefficients were calculated by convolution of a Gaussian fit to the SPECT resolution (FWHM, FWTM) values with the uniform cross section of each source. This simple mathematical model confirmed that the recovery coefficient dependence on source size was primarily related to the limited resolution of the detector. Experimental measurements demonstrated that the SPECT resolution for volume sources was sufficient for quantitation, although some limitations exist with respect to source sizes smaller than the detector resolution.

Ammonia and 6-glutamine, labeled with N-13, were tested as imaging agents for a variety of spontaneous canine tumors. The imaging capabilities of these compounds were compared with each other, with other scanning agents, and with radiologic and pathologic procedures. Good agreement between positive gamma images and postmortem findings occurred in 11 of 15 cases with [13N] ammonia as the localizing agent. Eight of the nine scans using [13N] glutamine as the imaging agent showed positive correlation with postmortem findings. In cases where both ammonia and glutamine were used to image the same lesion, no qualitative differences in tumor uptake were found between the two.