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To compare image resolution from iterative reconstruction with resolution from filtered back projection for low-contrast objects on phantom computed tomographic (CT) images across vendors and exposure levels.Randomized repeat scans of an American College of Radiology CT accreditation phantom (module 2, low contrast) were performed for multiple radiation exposures, vendors, and vendor iterative reconstruction algorithms. Eleven volunteers were presented with 900 images by using a custom-designed graphical user interface to perform a task created specifically for this reader study. Results were analyzed by using statistical graphics and analysis of variance.Across three vendors (blinded as A, B, and C) and across three exposure levels, the mean correct classification rate was higher for iterative reconstruction than filtered back projection (P.01): 87.4% iterative reconstruction and 81.3% filtered back projection at 20 mGy, 70.3% iterative reconstruction and 63.9% filtered back projection at 12 mGy, and 61.0% iterative reconstruction and 56.4% filtered back projection at 7.2 mGy. There was a significant difference in mean correct classification rate between vendor B and the other two vendors. Across all exposure levels, images obtained by using vendor B's scanner outperformed the other vendors, with a mean correct classification rate of 74.4%, while the mean correct classification rate for vendors A and C was 68.1% and 68.3%, respectively. Across all readers, the mean correct classification rate for iterative reconstruction (73.0%) was higher compared with the mean correct classification rate for filtered back projection (67.0%).The potential exists to reduce radiation dose without compromising low-contrast detectability by using iterative reconstruction instead of filtered back projection. There is substantial variability across vendor reconstruction algorithms.

Rationale and Objectives Quantifying changes in lung tumor volume is important for diagnosis, therapy planning, and evaluation of response to therapy. The aim of this study was to assess the performance of multiple algorithms on a reference data set. The study was organized by the Quantitative Imaging Biomarker Alliance (QIBA). Materials and Methods The study was organized as a public challenge. Computed tomography scans of synthetic lung tumors in an anthropomorphic phantom were acquired by the Food and Drug Administration. Tumors varied in size, shape, and radiodensity. Participants applied their own semi-automated volume estimation algorithms that either did not allow or allowed post-segmentation correction (type 1 or 2, respectively). Statistical analysis of accuracy (percent bias) and precision (repeatability and reproducibility) was conducted across algorithms, as well as across nodule characteristics, slice thickness, and algorithm type. Results Eighty-four percent of volume measurements of QIBA-compliant tumors were within 15% of the true volume, ranging from 66% to 93% across algorithms, compared to 61% of volume measurements for all tumors (ranging from 37% to 84%). Algorithm type did not affect bias substantially; however, it was an important factor in measurement precision. Algorithm precision was notably better as tumor size increased, worse for irregularly shaped tumors, and on the average better for type 1 algorithms. Over all nodules meeting the QIBA Profile, precision, as measured by the repeatability coefficient, was 9.0% compared to 18.4% overall. Conclusion The results achieved in this study, using a heterogeneous set of measurement algorithms, support QIBA quantitative performance claims in terms of volume measurement repeatability for nodules meeting the QIBA Profile criteria.

Knee-related injuries including meniscal tears are common in both young athletes and the aging population, and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists' experienced skills, confidence in detection of meniscal tears can be quite high. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively, obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the developed CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of the knee.

To develop and validate a metric of computed tomographic (CT) image quality that incorporates the noise texture and resolution properties of an image.Images of the American College of Radiology CT quality assurance phantom were acquired by using three commercial CT systems at seven dose levels with filtered back projection (FBP) and iterative reconstruction (IR). Image quality was characterized by the contrast-to-noise ratio (CNR) and a detectability index (d') that incorporated noise texture and spatial resolution. The measured CNR and d' were compared with a corresponding observer study by using the Spearman rank correlation coefficient to determine how well each metric reflects the ability of an observer to detect subtle lesions. Statistical significance of the correlation between each metric and observer performance was determined by using a Student t distribution; P values less than .05 indicated a significant correlation. Additionally, each metric was used to estimate the dose reduction potential of IR algorithms while maintaining image quality.Across all dose levels, scanner models, and reconstruction algorithms, the d' correlated strongly with observer performance in the corresponding observer study (ρ = 0.95; P.001), whereas the CNR correlated weakly with observer performance (ρ = 0.31; P = .21). Furthermore, the d' showed that the dose-reduction capabilities differed between clinical implementations (range, 12%-35%) and were less than those predicted from the CNR (range, 50%-54%).The strong correlation between the observer performance and the d' indicates that the d' is superior to the CNR for the evaluation of CT image quality. Moreover, the results of this study indicate that the d' improves less than the CNR with the use of IR, which indicates less potential for IR dose reduction than previously thought.

Adrenal abnormalities are commonly identified on computed tomography (CT) and are seen in at least 5 % of CT examinations of the thorax and abdomen. Previous studies have suggested that evaluation of Hounsfield units within a region of interest or a histogram analysis of a region of interest can be used to determine the likelihood that an adrenal gland is abnormal. However, the selection of a region of interest can be arbitrary and operator dependent. We hypothesize that segmenting the entire adrenal gland automatically without any human intervention and then performing a histogram analysis can accurately detect adrenal abnormality. We use the random forest classification framework to automatically perform a pixel-wise classification of an entire CT volume (abdomen and pelvis) into three classes namely right adrenal, left adrenal, and background. Once we obtain this classification, we perform histogram analysis to detect adrenal abnormality. The combination of these methods resulted in a sensitivity and specificity of 80 and 90 %, respectively, when analyzing 20 adrenal glands seen on volumetric CT datasets for abnormality.

Despite its high significance, the clinical utilization of image registration remains limited because of its lengthy execution time and a lack of easy access. The focus of this work was twofold. First, we accelerated our course-to-fine, volume subdivision-based image registration algorithm by a novel parallel implementation that maintains the accuracy of our uniprocessor implementation. Second, we developed a thin-client computing model with a user-friendly interface to perform rigid and nonrigid image registration. Our novel parallel computing model uses the message passing interface model on a 32-core cluster. The results show that, compared with the uniprocessor implementation, the parallel implementation of our image registration algorithm is approximately 5 times faster for rigid image registration and approximately 9 times faster for nonrigid registration for the images used. To test the viability of such systems for clinical use, we developed a thin client in the form of a plug-in in OsiriX, a well-known open source PACS workstation and DICOM viewer, and used it for two applications. The first application registered the baseline and follow-up MR brain images, whose subtraction was used to track progression of multiple sclerosis. The second application registered pretreatment PET and intratreatment CT of radiofrequency ablation patients to demonstrate a new capability of multimodality imaging guidance. The registration acceleration coupled with the remote implementation using a thin client should ultimately increase accuracy, speed, and access of image registration-based interpretations in a number of diagnostic and interventional applications.

Osteoarthritis (OA) is the most common form of arthritis and a major cause of morbidity affecting millions of adults in the US and world wide. In the knee, OA begins with the degeneration of joint articular cartilage, eventually resulting in the femur and tibia coming in contact, and leading to severe pain and stiffness. There has been extensive research examining 3D MR imaging sequences and automatic/semi-automatic techniques for 2D/3D articular cartilage extraction. However, in routine clinical practice the most popular technique still remain radiographic examination and qualitative assessment of the joint space. This may be in large part because of a lack of tools that can provide clinically relevant diagnosis in adjunct (in near real time fashion) with the radiologist and which can serve the needs of the radiologists and reduce inter-observer variation. Our work aims to fill this void by developing a CAD application that can generate clinically relevant diagnosis of the articular cartilage damage in near real time fashion. The algorithm features a 2D Active Shape Model (ASM) for modeling the bone-cartilage interface on all the slices of a Double Echo Steady State (DESS) MR sequence, followed by measurement of the cartilage thickness from the surface of the bone, and finally by the identification of regions of abnormal thinness and focal/degenerative lesions. A preliminary evaluation of CAD tool was carried out on 10 cases taken from the Osteoarthritis Initiative (OAI) database. When compared with 2 board-certified musculoskeletal radiologists, the automatic CAD application was able to get segmentation/thickness maps in little over 60 seconds for all of the cases. This observation poses interesting possibilities for increasing radiologist productivity and confidence, improving patient outcomes, and applying more sophisticated CAD algorithms to routine orthopedic imaging tasks.

Knee-related injuries involving the meniscal or articular cartilage are common and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and experience, confidence in detection of meniscal tears and articular cartilage abnormalities can be quite high. However, for radiologists without musculoskeletal training, diagnosis of such abnormalities can be challenging. In this paper, the potential of improving diagnosis through integration of computer-aided detection (CAD) algorithms for automatic detection of meniscal tears and articular cartilage injuries of the knees is studied. An integrated approach in which the results of algorithms evaluating either meniscal tears or articular cartilage injuries provide feedback to each other is believed to improve the diagnostic accuracy of the individual CAD algorithms due to the known association between abnormalities in these distinct anatomic structures. The correlation between meniscal tears and articular cartilage injuries is exploited to improve the final diagnostic results of the individual algorithms. Preliminary results from the integrated application are encouraging and more comprehensive tests are being planned.

Knee-related injuries including meniscal tears are common in both young athletes and the aging population and require accurate diagnosis and surgical intervention when appropriate. With proper techniques and radiologists’ experienced skills, confidence in detection of meniscal tears can be quite high. However, for radiologists without musculoskeletal training, diagnosis of meniscal tears can be challenging. This paper develops a novel computer-aided detection (CAD) diagnostic system for automatic detection of meniscal tears in the knee. Evaluation of this CAD system using an archived database of images from 40 individuals with suspected knee injuries indicates that the sensitivity and specificity of the proposed CAD system are 83.87% and 75.19%, respectively, compared to the mean sensitivity and specificity of 77.41% and 81.39%, respectively obtained by experienced radiologists in routine diagnosis without using the CAD. The experimental results suggest that the develo ped CAD system has great potential and promise in automatic detection of both simple and complex meniscal tears of knees. Keywords: Methods: Shape Analysis, Modalities: Magnetic Resonan ce, Diagnostic Task: Detection, Meniscal Tears, CAD, Orthopedics.

Purpose: Various vendors have promoted iterative reconstruction as an effective way to reduce CT radiation dose while maintaining image quality. The purpose of our exhibit is to demonstrate the effectiveness of various vendor reconstruction approaches on image quality based on a multi‐ institutional study utilizing the ACR phantom as a source of quantitative analysis.Methods:CT scans of the ACR CT QA phantom were acquired using three CT scanners (A: GE Discovery CT750 HD, B: Philips iCT, and C: Siemens FLASH). Images acquired at seven dose levels ranging from 1 to 20 mGy were reconstructed using both FBP and IR. The data acquisition was randomized and duplicated five times to reduce the effect of systematic variations. The images were reconstructed utilizing the kernel of reconstruction recommended by each manufacturer for abdominal CT studies utilizing both filtered back projection and the vendor's iterative reconstruction techniques. The phantom images were quantitatively evaluated for a number of parameters that determined spatial and contrast resolution as well as signal to noise levels. The data were entered into a spreadsheet and subsequently into a statistical package for analysis.Results: The success of iterative reconstruction varied substantially among the three vendors for the low dose CT protocols but did have a positive impact on image quality. The positive impact of iterative reconstruction was greatest for the lowest dose studies. The specific differences will be discussed in detail. Conclusions: In addition to subjective evaluation of image quality which can be affected by many parameters, it is also important to determine the impact of the newly developed CT iterative reconstruction algorithms in a quantitative manner utilizing phantoms. Our study suggests that the quantitative improvements in spatial resolution are modest. However, improvements in contrast to noise ratio are in the neighborhood of 35 to 58% depending on the exact implementation. Funding and Technical Support from National Institute of Standards and Technology is gratefully acknowledged. Technical support from each CT scanner manufacturer is also acknowledged.

Purpose: Iterative reconstruction (IR) has the potential to reduce patient dose while maintaining image quality comparable to filtered back projection (FBP). There are several different IR algorithms, however, with each offering different resolution and noise texture. The goal of this project is to compare image quality of FBP and IR images across three CTscanner models using metrics that incorporate system resolution and noise texture. Methods: The American College of Radiology CTquality assurance phantom was scanned using three CTscanner models (model A: GE Discovery CT750 HD, model B: Philips iCT, and model C: Siemens FLASH). Images acquired at seven dose levels ranging from 1 to 20 mGy were reconstructed using both FBP and IR. The data acquisition was randomized and duplicated five times to reduce the effect of systematic variables. The MTF was measured from the edges of the cylindrical inserts in the phantom. Square regions taken from the uniform section of the phantom were used to compute the NPS. From the MTF and NPS for each series, the d' was computed for a 5 mm object. Results: At a typical clinical dose level of 12 mGy, the difference in MTF10 between IR and FBP was 5%, 5%, and 8% for models A, B, and C, respectively. Additionally, the peak of the NPS for IR, when compared to FBP, was shifted to lower frequencies by 30%, 13%, and 22% for models A, B, and C respectively. Preliminary detectability index values were 20–40% higher for IR compared to FBP. Conclusions: Using IR increased the d' compared to FBP for all scanner models. The percent dose reduction achievable while maintaining image quality, however, varied across scanner models. This data provides a new perspective to define and optimize CT protocols across commercial scanner models.