Sandeep Suryanarayana Kaushik

Zero TE MR bone imaging in the head

To investigate proton density (PD)‐weighted zero TE (ZT) imaging for morphological depiction and segmentation of cranial bone structures.

Comparison of 4-class and continuous fat/water methods for whole-body, MR-based PET attenuation correction

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.

Clinical Evaluation of Zero-Echo-Time MR Imaging for the Segmentation of the Skull

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.

Hybrid ZTE/Dixon MR‐based attenuation correction for quantitative uptake estimation of pelvic lesions in PET/MRI

Purpose: This study introduces a new hybrid ZTE/Dixon MR‐based attenuation correction (MRAC) method including bone density estimation for PET/MRI and quantifies the effects of bone attenuation on metastatic lesion uptake in the pelvis. Methods: Six patients with pelvic lesions were scanned using fluorodeoxyglucose (18F‐FDG) in an integrated time‐of‐flight (TOF) PET/MRI system. For PET attenuation correction, MR imaging consisted of two‐point Dixon and zero echo‐time (ZTE) pulse sequences. A continuous‐value fat and water pseudoCT was generated from a two‐point Dixon MRI. Bone was segmented from the ZTE images and converted to Hounsfield units (HU) using a continuous two‐segment piecewise linear model based on ZTE MRI intensity. The HU values were converted to linear attenuation coefficients (LAC) using a bilinear model. The bone voxels of the Dixon‐based pseudoCT were replaced by the ZTE‐derived bone to produce the hybrid ZTE/Dixon pseudoCT. The three different AC maps (Dixon, hybrid ZTE/Dixon, CTAC) were used to reconstruct PET images using a TOF‐ordered subset expectation maximization algorithm with a point‐spread function model. Metastatic lesions were separated into two classes, bone lesions and soft tissue lesions, and analyzed. The MRAC methods were compared using a root‐mean‐squared error (RMSE), where the registered CTAC was taken as ground truth. Results: The RMSE of the maximum standardized uptake values (SUVmax) is 11.02% and 7.79% for bone (N = 6) and soft tissue lesions (N = 8), respectively, using Dixon MRAC. The RMSE of SUVmax for these lesions is significantly reduced to 3.28% and 3.94% when using the new hybrid ZTE/Dixon MRAC. Additionally, the RMSE for PET SUVs across the entire pelvis and all patients are 8.76% and 4.18%, for the Dixon and hybrid ZTE/Dixon MRAC methods, respectively. Conclusion: A hybrid ZTE/Dixon MRAC method was developed and applied to pelvic regions in an integrated TOF PET/MRI, demonstrating improved MRAC. This new method included bone density estimation, through which PET quantification is improved.

Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI

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.

Evaluation of sinus/edge corrected ZTE-based attenuation correction in brain PET/MRI

In brain PET/MRI, the major challenge of zero-echo-time (ZTE)–based attenuation correction (ZTAC) is the misclassification of air/tissue/bone mixtures or their boundaries. Our study aimed to evaluate a sinus/edge-corrected (SEC) ZTAC (ZTACSEC), relative to an uncorrected (UC) ZTAC (ZTACUC) and a CT atlas-based attenuation correction (ATAC). Methods: Whole-body 18F-FDG PET/MRI scans were obtained for 12 patients after PET/CT scans. Only data acquired at a bed station that included the head were used for this study. Using PET data from PET/MRI, we applied ZTACUC, ZTACSEC, ATAC, and reference CT-based attenuation correction (CTAC) to PET attenuation correction. For ZTACUC, the bias-corrected and normalized ZTE was converted to pseudo-CT with air (−1,000 HU for ZTE < 0.2), soft-tissue (42 HU for ZTE > 0.75), and bone (−2,000 × [ZTE − 1] + 42 HU for 0.2 ≤ ZTE ≤ 0.75). Afterward, in the pseudo-CT, sinus/edges were automatically estimated as a binary mask through morphologic processing and edge detection. In the binary mask, the overestimated values were rescaled below 42 HU for ZTACSEC. For ATAC, the atlas deformed to MR in-phase was segmented to air, inner air, soft tissue, and continuous bone. For the quantitative evaluation, PET mean uptake values were measured in twenty 1-mL volumes of interest distributed throughout brain tissues. The PET uptake was compared using a paired t test. An error histogram was used to show the distribution of voxel-based PET uptake differences. Results: Compared with CTAC, ZTACSEC achieved the overall PET quantification accuracy (0.2% ± 2.4%, P = 0.23) similar to CTAC, in comparison with ZTACUC (5.6% ± 3.5%, P < 0.01) and ATAC (−0.9% ± 5.0%, P = 0.03). Specifically, a substantial improvement with ZTACSEC (0.6% ± 2.7%, P < 0.01) was found in the cerebellum, in comparison with ZTACUC (8.1% ± 3.5%, P < 0.01) and ATAC (−4.1% ± 4.3%, P < 0.01). The histogram of voxel-based uptake differences demonstrated that ZTACSEC reduced the magnitude and variation of errors substantially, compared with ZTACUC and ATAC. Conclusion: ZTACSEC can provide an accurate PET quantification in brain PET/MRI, comparable to the accuracy achieved by CTAC, particularly in the cerebellum.

Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers

Accurate and robust attenuation correction remains challenging in hybrid PET/MR particularly for torsos because it is difficult to segment bones, lungs and internal air in MR images. Additionally, MR suffers from susceptibility artifacts when a metallic implant is present. Recently, joint estimation (JE) of activity and attenuation based on PET data, also known as maximum likelihood reconstruction of activity and attenuation, has gained considerable interest because of (1) its promise to address the challenges in MR-based attenuation correction (MRAC), and (2) recent advances in time-of-flight (TOF) technology, which is known to be the key to the success of JE. In this paper, we implement a JE algorithm using an MR-based prior and evaluate the algorithm using whole-body PET/MR patient data, for both FDG and non-FDG tracers, acquired from GE SIGNA PET/MR scanners with TOF capability. The weight of the MR-based prior is spatially modulated, based on MR signal strength, to control the balance between MRAC and JE. Large prior weights are used in strong MR signal regions such as soft tissue and fat (i.e. MR tissue classification with a high degree of certainty) and small weights are used in low MR signal regions (i.e. MR tissue classification with a low degree of certainty). The MR-based prior is pragmatic in the sense that it is convex and does not require training or population statistics while exploiting synergies between MRAC and JE. We demonstrate the JE algorithm has the potential to improve the robustness and accuracy of MRAC by recovering the attenuation of metallic implants, internal air and some bones and by better delineating lung boundaries, not only for FDG but also for more specific non-FDG tracers such as 68Ga-DOTATOC and 18F-Fluoride.

Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning

To describe a method for converting Zero TE (ZTE) MR images into X‐ray attenuation information in the form of pseudo‐CT images and demonstrate its performance for (1) attenuation correction (AC) in PET/MR and (2) dose planning in MR‐guided radiation therapy planning (RTP).

Improving PET/MR brain quantitation with template-enhanced ZTE

Purpose: The impact of MR‐based attenuation correction on PET quantitation accuracy is an ongoing cause of concern for advanced brain research with PET/MR. The purpose of this study was to evaluate a new, template‐enhanced zero‐echo‐time attenuation correction method for PET/MR scanners. Methods: 30 subjects underwent a clinically‐indicated 18F‐FDG‐PET/CT, followed by PET/MR on a GE SIGNA PET/MR. For each patient, a 42‐s zero echo time (ZTE) sequence was used to generate two attenuation maps: one with the standard ZTE segmentation‐based method; and another with a modification of the method, wherein pre‐registered anatomical templates and CT data were used to enhance the segmentation. CT data, was used as gold standard. Reconstructed PET images were qualified visually and quantified in 68 volumes‐of‐interest using a standardized brain atlas. Results: Attenuation maps were successfully generated in all cases, without manual intervention or parameter tuning. One patient was excluded from the quantitative analysis due to the presence of multiple brain metastases. The PET bias with template‐enhanced ZTE attenuation correction was measured to be −0.9%±0.9%, compared with −1.4%±1.1% with regular ZTE attenuation correction. In terms of absolute bias, the new method yielded 1.1%±0.7%, compared with 1.6%±0.9% with regular ZTE. Statistically significant bias reduction was obtained in the frontal region (from −2.0% to −1.0%), temporal (from −1.2% to −0.2%), parietal (from −1.9% to −1.1%), occipital (from −2.0% to −1.1%) and insula (from −1.4% to −1.1%). Conclusion: These results indicate that the co‐registration of pre‐recorded anatomical templates to ZTE data is feasible in clinical practice and can be effectively used to improve the performance of segmentation‐based attenuation correction. HIGHLIGHTS:Co‐registration of anatomical templates to ZTE data is feasible in clinical practice and can improve the performance of segmentation‐based attenuation correction.The overall uptake bias was ˜35% lower when using template‐enhanced ZTE instead of regular ZTE attenuation correction.Statistically significant bias reduction was obtained in the frontal region (from ‐2.0% to ‐1.0%), temporal (from ‐1.2% to ‐0.2%), parietal (from ‐1.9% to ‐1.1%), occipital (from ‐2.0% to ‐1.1%) and insula (from ‐1.4% to ‐1.1%).

GPNLPerf: Robust 4d Non-rigid Motion Correction for Myocardial Perfusion Analysis

Since the introduction of wide cone detector systems, CT myocardial perfusion has been an area of increased interest, for which non-rigid registration [NRR] is a key step to further analysis. We propose a novel motion management pipeline for perfusion data, GPNLPerf (Group-wise, non-local, NRR for perfusion analysis) centering on group-wise NRR using non-local spatio-temporal constraints. The proposed pipeline deals with the NRR challenges for 4D perfusion data and results in generating clinically relevant perfusion parameters. We demonstrate results on 9 dynamic perfusion exams comparing results quantitatively with ANTs NRR and also show qualitative results on perfusion maps.