Ganesh Saiprasad

Evaluation of Low-Contrast Detectability of Iterative Reconstruction across Multiple Institutions, CT Scanner Manufacturers, and Radiation Exposure Levels.

PURPOSE 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. MATERIALS AND METHODS 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. RESULTS 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 Bs 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%). CONCLUSION 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.

Algorithm Variability in the Estimation of Lung Nodule Volume From Phantom CT Scans: Results of the QIBA 3A Public Challenge

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.

Role of Computer Aided Detection (CAD) Integration: Case Study with Meniscal and Articular Cartilage CAD applications

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.

SU‐C‐217BCD‐02: Evaluating the Impact of Iterative Reconstruction for Three Major CT Vendors

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 vendors 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.

A concurrent computer aided detection (CAD) tool for articular cartilage disease of the knee on MR imaging using active shape models

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.

Parallel image registration with a thin client interface

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.