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Author

Sandeep Kaushik

Other affiliations: GE Healthcare
Bio: Sandeep Kaushik is an academic researcher from General Electric. The author has contributed to research in topics: Correction for attenuation & Attenuation. The author has an hindex of 12, co-authored 26 publications receiving 768 citations. Previous affiliations of Sandeep Kaushik include GE Healthcare.

Papers
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Journal ArticleDOI
TL;DR: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.
Abstract: Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone is neglected. Ultrashort-echo-time or zero-echo-time (ZTE) pulse sequences can capture bone information. We propose the use of patient-specific multiparametric MRI consisting of Dixon MRI and proton-density–weighted ZTE MRI to directly synthesize pseudo-CT images with a deep learning model: we call this method ZTE and Dixon deep pseudo-CT (ZeDD CT). Methods: Twenty-six patients were scanned using an integrated 3-T time-of-flight PET/MRI system. Helical CT images of the patients were acquired separately. A deep convolutional neural network was trained to transform ZTE and Dixon MR images into pseudo-CT images. Ten patients were used for model training, and 16 patients were used for evaluation. Bone and soft-tissue lesions were identified, and the SUVmax was measured. The root-mean-squared error (RMSE) was used to compare the MR-based attenuation correction with the ground-truth CT attenuation correction. Results: In total, 30 bone lesions and 60 soft-tissue lesions were evaluated. The RMSE in PET quantification was reduced by a factor of 4 for bone lesions (10.24% for Dixon PET and 2.68% for ZeDD PET) and by a factor of 1.5 for soft-tissue lesions (6.24% for Dixon PET and 4.07% for ZeDD PET). Conclusion: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.

217 citations

Journal ArticleDOI
TL;DR: To investigate proton density‐weighted zero TE (ZT) imaging for morphological depiction and segmentation of cranial bone structures, proton densities are measured through X-ray diffraction and radiolysis.
Abstract: Purpose To investigate proton density (PD)-weighted zero TE (ZT) imaging for morphological depiction and segmentation of cranial bone structures. Methods A rotating ultra-fast imaging sequence (RUFIS) type ZT pulse sequence was developed and optimized for 1) efficient capture of short T2 bone signals and 2) flat PD response for soft-tissues. An inverse logarithmic image scaling (i.e., −log(image)) was used to highlight bone and differentiate it from surrounding soft-tissue and air. Furthermore, a histogram-based bias-correction method was developed for subsequent threshold-based air, soft-tissue, and bone segmentation. Results PD-weighted ZT imaging in combination with an inverse logarithmic scaling was found to provide excellent depiction of cranial bone structures. In combination with bias correction, also excellent segmentation results were achieved. A two-dimensional histogram analysis demonstrates a strong, approximately linear correlation between inverse log-scaled ZT and low-dose CT for Hounsfield units (HU) between −300 HU and 1,500 HU (corresponding to soft-tissue and bone). Conclusions PD-weighted ZT imaging provides robust and efficient depiction of bone structures in the head, with an excellent contrast between air, soft-tissue, and bone. Besides structural bone imaging, the presented method is expected to be of relevance for attenuation correction in positron emission tomography (PET)/MR and MR-based radiation therapy planning. Magn Reson Med 75:107–114, 2016. © 2015 Wiley Periodicals, Inc.

196 citations

Journal ArticleDOI
TL;DR: This is the first, to the authors' knowledge, clinical evaluation of skull bone identification based on a ZTE sequence and the results suggest that proton density–weighted ZTE imaging is an efficient means of obtaining high-resolution maps of bone tissue with sufficient anatomic accuracy for, for example, PET attenuation correction.
Abstract: MR-based attenuation correction is instrumental for integrated PET/MR imaging. It is generally achieved by segmenting MR images into a set of tissue classes with known attenuation properties (e.g., air, lung, bone, fat, soft tissue). Bone identification with MR imaging is, however, quite challenging, because of the low proton density and fast decay time of bone tissue. The clinical evaluation of a novel, recently published method for zero-echo-time (ZTE)–based MR bone depiction and segmentation in the head is presented here. Methods: A new paradigm for MR imaging bone segmentation, based on proton density–weighted ZTE imaging, was disclosed earlier in 2014. In this study, we reviewed the bone maps obtained with this method on 15 clinical datasets acquired with a PET/CT/MR trimodality setup. The CT scans acquired for PET attenuation-correction purposes were used as reference for the evaluation. Quantitative measurements based on the Jaccard distance between ZTE and CT bone masks and qualitative scoring of anatomic accuracy by an experienced radiologist and nuclear medicine physician were performed. Results: The average Jaccard distance between ZTE and CT bone masks evaluated over the entire head was 52% ± 6% (range, 38%–63%). When only the cranium was considered, the distance was 39% ± 4% (range, 32%–49%). These results surpass previously reported attempts with dual-echo ultrashort echo time, for which the Jaccard distance was in the 47%–79% range (parietal and nasal regions, respectively). Anatomically, the calvaria is consistently well segmented, with frequent but isolated voxel misclassifications. Air cavity walls and bone/fluid interfaces with high anatomic detail, such as the inner ear, remain a challenge. Conclusion: This is the first, to our knowledge, clinical evaluation of skull bone identification based on a ZTE sequence. The results suggest that proton density–weighted ZTE imaging is an efficient means of obtaining high-resolution maps of bone tissue with sufficient anatomic accuracy for, for example, PET attenuation correction.

113 citations

Journal ArticleDOI
01 Oct 2012
TL;DR: It is demonstrated that both 4class and continuousFat/water AC methods provided adequate quantitation in the body, and that the continuous fat/water method was within 5.7% on average for SUV mean in liver and 1.6% onaverage for SUV max for FDG-avid features.
Abstract: The goal of this study was to compare two approaches for MR-based PET patient attenuation correction (AC) in whole-body FDG-PET imaging using a tri-modality PET/CT & MR setup. Sixteen clinical whole-body FDG patients were included in this study. Mean standard uptake values (SUV) were measured for liver and lung volumes-of-interest for comparison. Maximum SUV values were measured in 18 FDGavid features in ten of the patients. The AC methods compared to gold-standard CT-based AC were segmentation of the CT (air, lung, fat, water), MR image segmentation with 4 tissue classes (air, lung, fat, water) and segmentation with air, lung and a continuous fat/water method. Results: The magnitude of uptake value differences induced by CT-based image segmentation were similar but lower on average than those found using the MRderived AC methods. The average liver SUV difference with that found using CTAC was 1.3%, 10.4% and 5.7% for 4-class segmented CT, 4-class MRAC and continuous fat/water MRAC methods, respectively. The average FDG-avid feature SUV max difference was -0.5%,1.7% and -1.6% for 4-class segmented CT, 4-class MRAC and continuous fat/water MRAC methods, respectively. Conclusion: The results demonstrated that both 4class and continuous fat/water AC methods provided adequate quantitation in the body, and that the continuous fat/water method was within 5.7% on average for SUV mean in liver and 1.6% on average for SUV max for FDG-avid features.

92 citations

Journal ArticleDOI
TL;DR: A method for converting Zero TE MR images into X‐ray attenuation information in the form of pseudo‐CT images is described and its performance for attenuation correction in PET/MR and dose planning in MR‐guided radiation therapy planning (RTP) is demonstrated.
Abstract: Purpose: To describe a method for converting Zero TE (ZTE) MR images into Xray attenuation information in the form of pseudo-CT images and demonstrate its performance for (1) attenuation correction ...

80 citations


Cited by
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Journal ArticleDOI
TL;DR: The general principles of DL and convolutional neural networks are introduced, five major areas of application of DL in medical imaging and radiation therapy are surveyed, common themes are identified, methods for dataset expansion are discussed, and lessons learned, remaining challenges, and future directions are summarized.
Abstract: The goals of this review paper on deep learning (DL) in medical imaging and radiation therapy are to (a) summarize what has been achieved to date; (b) identify common and unique challenges, and strategies that researchers have taken to address these challenges; and (c) identify some of the promising avenues for the future both in terms of applications as well as technical innovations. We introduce the general principles of DL and convolutional neural networks, survey five major areas of application of DL in medical imaging and radiation therapy, identify common themes, discuss methods for dataset expansion, and conclude by summarizing lessons learned, remaining challenges, and future directions.

525 citations

Journal ArticleDOI
TL;DR: An introduction to deep learning technology is provided and the stages that are entailed in the design process of deep learning radiology research are presented and the results of a survey of the application of convolutional neural networks to radiologic imaging are detailed.
Abstract: This article is a guide to convolutional neural network technologies and their clinical applications in the analysis of radiologic images.

287 citations

Journal ArticleDOI
TL;DR: Basic technical knowledge regarding deep learning with CNNs along the actual course is illustrated (collecting data, implementing CNNs, and training and testing phases).
Abstract: Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

238 citations

Journal ArticleDOI
TL;DR: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.
Abstract: Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone is neglected. Ultrashort-echo-time or zero-echo-time (ZTE) pulse sequences can capture bone information. We propose the use of patient-specific multiparametric MRI consisting of Dixon MRI and proton-density–weighted ZTE MRI to directly synthesize pseudo-CT images with a deep learning model: we call this method ZTE and Dixon deep pseudo-CT (ZeDD CT). Methods: Twenty-six patients were scanned using an integrated 3-T time-of-flight PET/MRI system. Helical CT images of the patients were acquired separately. A deep convolutional neural network was trained to transform ZTE and Dixon MR images into pseudo-CT images. Ten patients were used for model training, and 16 patients were used for evaluation. Bone and soft-tissue lesions were identified, and the SUVmax was measured. The root-mean-squared error (RMSE) was used to compare the MR-based attenuation correction with the ground-truth CT attenuation correction. Results: In total, 30 bone lesions and 60 soft-tissue lesions were evaluated. The RMSE in PET quantification was reduced by a factor of 4 for bone lesions (10.24% for Dixon PET and 2.68% for ZeDD PET) and by a factor of 1.5 for soft-tissue lesions (6.24% for Dixon PET and 4.07% for ZeDD PET). Conclusion: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.

217 citations

Journal ArticleDOI
TL;DR: Performance measurements of the ToF-PET whole body GE SIGNA PET/MR system indicate that it is a promising new simultaneous imaging platform.
Abstract: Purpose: The GE SIGNA PET/MR is a new whole body integrated time-of-flight (ToF)-PET/MR scanner from GE Healthcare. The system is capable of simultaneous PET and MR image acquisition with sub-400 ps coincidence time resolution. Simultaneous PET/MR holds great potential as a method of interrogating molecular, functional, and anatomical parameters in clinical disease in one study. Despite the complementary imaging capabilities of PET and MRI, their respective hardware tends to be incompatible due to mutual interference. In this work, the GE SIGNA PET/MR is evaluated in terms of PET performance and the potential effects of interference from MRI operation. Methods: The NEMA NU 2-2012 protocol was followed to measure PET performance parameters including spatial resolution, noise equivalent count rate, sensitivity, accuracy, and image quality. Each of these tests was performed both with the MR subsystem idle and with continuous MR pulsing for the duration of the PET data acquisition. Most measurements were repeated at three separate test sites where the system is installed. Results: The scanner has achieved an average of 4.4, 4.1, and 5.3 mm full width at half maximum radial, tangential, and axial spatial resolutions, respectively, at 1 cm from the transaxial FOV center. The peak noise equivalent count rate (NECR) of 218 kcps and a scatter fraction of 43.6% are reached at an activity concentration of 17.8 kBq/ml. Sensitivity at the center position is 23.3 cps/kBq. The maximum relative slice count rate error below peak NECR was 3.3%, and the residual error from attenuation and scatter corrections was 3.6%. Continuous MR pulsing had either no effect or a minor effect on each measurement. Conclusions: Performance measurements of the ToF-PET whole body GE SIGNA PET/MR system indicate that it is a promising new simultaneous imaging platform.

199 citations