PET Attenuation Correction Research Articles

Prospective non-invasive evaluation of CXCR4 expression for the diagnosis of MALT lymphoma using [68Ga]Ga-Pentixafor-PET/MRI.

MALT lymphomas express the chemokine receptor CXCR4 on a regular basis, and [68Ga]Ga-Pentixafor-PET has been shown to quantify CXCR4 expression non-invasively. We, therefore, aimed to evaluate [68Ga]Ga-Pentixafor-PET/MRI for the non-invasive assessment of MALT lymphomas.Methods: We included 36 MALT lymphoma patients, who had not undergone previous systemic or radiation therapy, in our prospective, IRB-approved, proof-of-concept study. Involved anatomic regions were the orbit (n=14), stomach (n=10), lungs (n=5), and other sites (soft-tissues n=3; adrenal gland, tonsils, parotid gland, and urinary bladder n=1, respectively). MRI sequences included an axial 2-point Dixon T1 VIBE SPAIR 3D sequence for PET attenuation correction; a coronal T2 HASTE sequence; and an axial echo-planar imaging SPAIR-based diffusion-weighted sequence (DWI) obtained during free-breathing (b-values, 50 and 800), with corresponding ADC (apparent diffusion coefficient) maps.Results: In 33/36 patients, there were MALT lymphomas with an increased uptake of [68Ga]Ga-Pentixafor; all current lymphoma manifestations showed an increased uptake and, accordingly, were positive on the PET/MRI. The remaining three patients had undergone surgery for their orbital MALT lymphomas prior to PET/MRI. Mean SUVmax was 8.6 ± 4.7, mean SUVmean was 4.7 ± 1.8, and mean SUVpeak was 8.0 ± 4.2. The mean SUVmax of the liver was 1.8, and the mean tumor-to-liver ratio was 2.9 ± 2.0. There were no significant differences in SUVmax (P=0.22), SUVmean (P=0.53), SUVpeak (P=0.29), or SUVt/l (P=0.92) between the four anatomic regions (orbit, stomach, lungs, other). The mean tumor volume was 146 ± 499.Conclusions: Our results thus indicate that [68Ga]Ga-Pentixafor-PET is feasible for the assessment of MALT lymphomas, with a good tumor-to-background ratio in terms of radiotracer uptake.

A dual-tuned 13 C/1 H head coil for PET/MR hybrid neuroimaging: Development, attenuation correction, and first evaluation.

This study aims to develop, implement, and evaluate a dual-tuned 13 C/1 H head coil for integrated positron emission tomography/magnetic resonance (PET/MR) neuroimaging. The radiofrequency (RF) head coil is designed for optimized MR imaging performance and PET transparency and attenuation correction (AC) is applied for accurate PET quantification. A dual-tuned 13 C/1 H RF head coil featuring a 16-rung birdcage was designed to be used for integrated PET/MR hybrid imaging. While the open birdcage design can be considered inherently PET transparent, all further electronic RF components were placed as far as possible outside of the field-of-view (FOV) of the PET detectors. The RF coil features a rigid geometry and thin-walled casing. Attenuation correction of the RF head coil is performed by generating and applying a dedicated 3D CT-based template attenuation map (μmap). Attenuation correction was systematically evaluated in phantom experiments using a large-volume cylindrical emission phantom filled with 18-F-Fluordesoxyglucose (FDG) radiotracer. The PET/MR imaging performance and PET attenuation correction were then evaluated in a patient study including six patients. The dual-tuned RF head coil causes a mean relative attenuation difference of 8.8% across the volume of the cylindrical phantom, while the local relative differences range between 1% and 25%. Applying attenuation correction, the relative difference between the two measurements with and without RF coil is reduced to mean value of 0.5%, with local differences of ±3.6%. The quantitative results of the phantom measurements were corroborated by patient PET/MR measurements. Patient scans using the RF head coil show a decrease of PET signal of 5.17% ± 0.81% when compared to the setup without RF head coil in place, which served as a reference scan. When applying attenuation correction of the RF coil in the patient measurements, the mean difference to a measurement without RF coil was reduced to -0.87% ± 0.65%. A dual-tuned 13 C/1 H RF head coil was designed and evaluated regarding its potential use in integrated PET/MR hybrid imaging. Attenuation correction was successfully applied. In conclusion, the RF head coil was successfully integrated into PET/MR hybrid imaging and can now be used for 13 C/1 H multinuclear hybrid neuroimaging in future studies.

PET-MR Attenuation Correction in Dynamic Brain PET Using [11C]Cimbi-36: A Direct Comparison With PET-CT

PET-MR is increasingly used in a range of clinical indications, though its use for quantitative brain PET research has not been widely reported. To isolate and evaluate MR-based attenuation correction (MR-AC) effects on fitted PET kinetic model parameters, we used a previously reported atlas-based method to synthesize CTs from structural MR scans, substituted for the CTs acquired in a dynamic PET-CT study where [11C]Cimbi-36 was used to quantify 5-HT2 A receptor binding in the brain. The resulting small differences (<5%) in kinetic outcome measures using MR-AC compared to CT-AC were consistent with the observed differences between the MR-AC and CT attenuation maps and within the test-retest variability of the radioligand. This supports the use of MR-AC for quantitative PET imaging of the brain.

Illustrations pratiques — Pièges et artefacts/Trucs et astuces

Artifacts result from discrepancy between the estimated density values coming from the reconstructed CT image and the real value. They may come from many reasons (patient, device, algorithm, RX physical) and lead to deterioration of image quality and bad attenuation correction for SPECT or PET.

Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.

Magnetic resonance imaging (MRI) provides a number of advantages over computed tomography (CT) for radiation therapy treatment planning; however, MRI lacks the key electron density information necessary for accurate dose calculation. We propose a dictionary-learning-based method to derive electron density information from MRIs. Specifically, we first partition a given MR image into a set of patches, for which we used a joint dictionary learning method to directly predict a CT patch as a structured output. Then a feature selection method is used to ensure prediction robustness. Finally, we combine all the predicted CT patches to obtain the final prediction for the given MR image. This prediction technique was validated for a clinical application using 14 patients with brain MR and CT images. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), normalized cross-correlation (NCC) indices and similarity index (SI) for air, soft-tissue and bone region were used to quantify the prediction accuracy. The mean ± std of PSNR, MAE, and NCC were: , HU, and for the 14 patients. The SIs for air, soft-tissue, and bone regions are , , and . These indices demonstrate the CT prediction accuracy of the proposed learning-based method. This CT image prediction technique could be used as a tool for MRI-based radiation treatment planning, or for PET attenuation correction in a PET/MRI scanner.

Technical Note: Deep learning based MRAC using rapid ultrashort echo time imaging.

In this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition. MR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35s). Tissue labeling of air, soft tissue, and bone in the UTE image is accomplished via a deep learning network that was pre-trained with T1-weighted MR images. UTE images are used as input to the network, which was trained using labels derived from co-registered CT images. The tissue labels estimated by deep learning are refined by a conditional random field based correction. The soft tissue labels are further separated into fat and water components using the two-point Dixon method. The estimated bone, air, fat, and water images are then assigned appropriate Hounsfield units, resulting in a pseudo CT image for PET attenuation correction. To evaluate the proposed MRAC method, PET/MR imaging of the head was performed on eight human subjects, where Dice similarity coefficients of the estimated tissue labels and relative PET errors were evaluated through comparison to a registered CT image. Dice coefficients for air (within the head), soft tissue, and bone labels were 0.76±0.03, 0.96±0.006, and 0.88±0.01. In PET quantitation, the proposed MRAC method produced relative PET errors less than 1% within most brain regions. The proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantitation with accurate and rapid pseudo CT generation.

Technical Note: Adapting a GE SIGNA PET/MR scanner for radiotherapy.

Simultaneous collection of PET and MR data for radiotherapy purposes are useful for, for example, target definition and dose escalations. However, a prerequisite for using PET/MR in the radiotherapy workflow is the ability to image the patient in treatment position. The aim of this work was to adapt a GE SIGNA PET/MR scanner to image patients for radiotherapy treatment planning and evaluate the impact on signal-to-noise (SNR) of the MR images, and the accuracy of the PET attenuation correction. A flat tabletop and a coil holder were developed to image patients in the treatment position, avoid patient contour deformation, and facilitate attenuation correction of flex coils. Attenuation corrections for the developed hardware and an anterior array flex coil were also measured and implemented to the PET/MR system to minimize PET quantitation errors. The reduction of SNR in the MR images due to the added distance between the coils and the patient was evaluated using a large homogenous saline-doped water phantom, and the activity quantitation errors in PET imaging were evaluated with and without the developed attenuation corrections. We showed that the activity quantitation errors in PET imaging were within ±5% when correcting for attenuation of the flat tabletop, coil holder, and flex coil. The SNR of the MRI images were reduced to 74% using the tabletop, and 66% using the tabletop and coil holders. We present a tabletop and coil holder for an anterior array coil to be used with a GE SIGNA PET/MR scanner, for scanning patients in the radiotherapy work flow. Implementing attenuation correction of the added hardware from the radiotherapy setup leads to acceptable PET image quantitation. The drop in SNR in MR images may require adjustment of the imaging protocols.

Respiratory-resolved MR-based attenuation correction for motion-compensated cardiac PET-MR

Respiratory motion during cardiac PET acquisitions can cause image blurring and erroneous uptake quantification. In particular the misalignment of attenuation correction (AC) maps and PET emission data can lead to severe quantification errors, because the AC value of the heart is five times higher than of the surrounding lung tissue. Standard PET-MR approaches assume accurate alignment between breathhold MR-based AC maps and free-breathing PET emission data but cannot necessarily ensure it. Here we propose a 75 s free-breathing MR-acquisition, which provides respiratory-resolved AC maps (ACDyn) and non-rigid respiratory motion information. This approach ensures accurate AC for free-breathing PET data and the motion information can be utilized to reduce image blurring caused by respiratory motion. 3D multi-echo MR data was acquired during a 75 s free-breathing scan in six patients. Both a respiratory-resolved dynamic AC map (ACDyn) and a non-rigid respiratory motion field are provided by the MR scan. ACDyn yielded AC values for different breathing phases ensuring accurate AC for each respiratory phase of the free-breathing PET data. In addition, motion-corrected image reconstruction (MCIR) of MR and PET data was used to minimize breathing artefacts. Motion amplitudes in the left ventricle were 8.2 ± 2.9 mm with a dominant motion direction along the anterior-anterolateral and inferior-inferoseptal axis of the heart. The proposed ACDyn-MCIR technique led to significant signal recovery of PET tracer uptake by 24 ± 5% (p < 0.05). The maximum improvement was observed in patients with large misalignment between standard breathhold MR-based AC maps and PET emission data. PET image resolution was improved by 20 ± 12% (p < 0.05). We have presented an efficient MR-scan, which ensures accurate motion information and AC values to improve PET quantification for cardiac PET-MR scans. The short scan time of 75 s makes this free-breathing acquisition easy to integrate into standard clinical PET-MR protocols.

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.

Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight information is a promising method for PET attenuation correction. However, it still suffers from several problems, including crosstalk artifacts, slow convergence speed, and noisy attenuation maps (μ-maps). In this work, we developed deep convolutional neural networks (CNNs) to overcome these MLAA limitations, and we verified their feasibility using a clinical brain PET dataset. Methods: We applied the proposed method to one of the most challenging PET cases for simultaneous image reconstruction (18F-fluorinated-N-3-fluoropropyl-2-β-carboxymethoxy-3-β-(4-iodophenyl)nortropane [18F-FP-CIT] PET scans with highly specific binding to striatum of the brain). Three different CNN architectures (convolutional autoencoder [CAE], Unet, and Hybrid of CAE) were designed and trained to learn a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution and μ-map (μ-MLAA). The PET/CT data of 40 patients with suspected Parkinson disease were used for 5-fold cross-validation. For the training of CNNs, 800,000 transverse PET and CT slices augmented from 32 patient datasets were used. The similarity to μ-CT of the CNN-generated μ-maps (μ-CAE, μ-Unet, and μ-Hybrid) and μ-MLAA was compared using Dice similarity coefficients. In addition, we compared the activity concentration of specific (striatum) and nonspecific (cerebellum and occipital cortex) binding regions and the binding ratios in the striatum in the PET activity images reconstructed using those μ-maps. Results: The CNNs generated less noisy and more uniform μ-maps than the original μ-MLAA. Moreover, the air cavities and bones were better resolved in the proposed CNN outputs. In addition, the proposed deep learning approach was useful for mitigating the crosstalk problem in the MLAA reconstruction. The Hybrid network of CAE and Unet yielded the most similar μ-maps to μ-CT (Dice similarity coefficient in the whole head = 0.79 in the bone and 0.72 in air cavities), resulting in only about a 5% error in activity and binding ratio quantification. Conclusion: The proposed deep learning approach is promising for accurate attenuation correction of activity distribution in time-of-flight PET systems.

Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data.

Respiratory motion degrades the detection and quantification capabilities of PET/CT imaging. Moreover, mismatch between a fast helical CT image and a time-averaged PET image due to respiratory motion results in additional attenuation correction artifacts and inaccurate localization. Current motion compensation approaches typically have 3 limitations: the mismatch among respiration-gated PET images and the CT attenuation correction (CTAC) map can introduce artifacts in the gated PET reconstructions that can subsequently affect the accuracy of the motion estimation; sinogram-based correction approaches do not correct for intragate motion due to intracycle and intercycle breathing variations; and the mismatch between the PET motion compensation reference gate and the CT image can cause an additional CT-mismatch artifact. In this study, we established a motion correction framework to address these limitations. Methods: In the proposed framework, the combined emission-transmission reconstruction algorithm was used for phase-matched gated PET reconstructions to facilitate the motion model building. An event-by-event nonrigid respiratory motion compensation method with correlations between internal organ motion and external respiratory signals was used to correct both intracycle and intercycle breathing variations. The PET reference gate was automatically determined by a newly proposed CT-matching algorithm. We applied the new framework to 13 human datasets with 3 different radiotracers and 323 lesions and compared its performance with CTAC and non-attenuation correction (NAC) approaches. Validation using 4-dimensional CT was performed for one lung cancer dataset. Results: For the 10 18F-FDG studies, the proposed method outperformed (P < 0.006) both the CTAC and the NAC methods in terms of region-of-interest-based SUVmean, SUVmax, and SUV ratio improvements over no motion correction (SUVmean: 19.9% vs. 14.0% vs. 13.2%; SUVmax: 15.5% vs. 10.8% vs. 10.6%; SUV ratio: 24.1% vs. 17.6% vs. 16.2%, for the proposed, CTAC, and NAC methods, respectively). The proposed method increased SUV ratios over no motion correction for 94.4% of lesions, compared with 84.8% and 86.4% using the CTAC and NAC methods, respectively. For the 2 18F-fluoropropyl-(+)-dihydrotetrabenazine studies, the proposed method reduced the CT-mismatch artifacts in the lower lung where the CTAC approach failed and maintained the quantification accuracy of bone marrow where the NAC approach failed. For the 18F-FMISO study, the proposed method outperformed both the CTAC and the NAC methods in terms of motion estimation accuracy at 2 lung lesion locations. Conclusion: The proposed PET/CT respiratory event-by-event motion-correction framework with motion information derived from matched attenuation-corrected PET data provides image quality superior to that of the CTAC and NAC methods for multiple tracers.

PET attenuation correction for flexible MRI surface coils in hybrid PET/MRI using a 3D depth camera

PET attenuation correction for flexible MRI radio frequency surface coils in hybrid PET/MRI is still a challenging task, as position and shape of these coils conform to large inter-patient variabilities. The purpose of this feasibility study is to develop a novel method for the incorporation of attenuation information about flexible surface coils in PET reconstruction using the Microsoft Kinect V2 depth camera. The depth information is used to determine a dense point cloud of the coil’s surface representing the shape of the coil. From a CT template—acquired once in advance—surface information of the coil is extracted likewise and converted into a point cloud. The two point clouds are then registered using a combination of an iterative-closest-point (ICP) method and a partially rigid registration step. Using the transformation derived through the point clouds, the CT template is warped and thereby adapted to the PET/MRI scan setup. The transformed CT template is converted into an attenuation map from Hounsfield units into linear attenuation coefficients. The resulting fitted attenuation map is then integrated into the MRI-based patient-specific DIXON-based attenuation map of the actual PET/MRI scan. A reconstruction of phantom PET data acquired with the coil present in the field-of-view (FoV), but without the corresponding coil attenuation map, shows large artifacts in regions close to the coil. The overall count loss is determined to be around 13% compared to a PET scan without the coil present in the FoV. A reconstruction using the new μ-map resulted in strongly reduced artifacts as well as increased overall PET intensities with a remaining relative difference of about 1% to a PET scan without the coil in the FoV.

Impact of improved attenuation correction featuring a bone atlas and truncation correction on PET quantification in whole-body PET/MR.

Recent studies have shown an excellent correlation between PET/MR and PET/CT hybrid imaging in detecting lesions. However, a systematic underestimation of PET quantification in PET/MR has been observed. This is attributable to two methodological challenges of MR-based attenuation correction (AC): (1) lack of bone information, and (2) truncation of the MR-based AC maps (μmaps) along the patient arms. The aim of this study was to evaluate the impact of improved AC featuring a bone atlas and truncation correction on PET quantification in whole-body PET/MR. The MR-based Dixon method provides four-compartment μmaps (background air, lungs, fat, soft tissue) which served as a reference for PET/MR AC in this study. A model-based bone atlas provided bone tissue as a fifth compartment, while the HUGE method provided truncation correction. The study population comprised 51 patients with oncological diseases, all of whom underwent a whole-body PET/MR examination. Each whole-body PET dataset was reconstructed four times using standard four-compartment μmaps, five-compartment μmaps, four-compartment μmaps+HUGE, and five-compartment μmaps+HUGE. The SUVmax for each lesion was measured to assess the impact of each μmap on PET quantification. All four μmaps in each patient provided robust results for reconstruction of the AC PET data. Overall, SUVmax was quantified in 99 tumours and lesions. Compared to the reference four-compartment μmap, the mean SUVmax of all 99 lesions increased by 1.4±2.5% when bone was added, by 2.1±3.5% when HUGE was added, and by 4.4±5.7% when bone+HUGE was added. Larger quantification bias of up to 35% was found for single lesions when bone and truncation correction were added to the μmaps, depending on their individual location in the body. The novel AC method, featuring a bone model and truncation correction, improved PET quantification in whole-body PET/MR imaging. Short reconstruction times, straightforward reconstruction workflow, and robust AC quality justify further routine clinical application of this method.

An artefact of PET attenuation correction caused by iron overload of the liver in clinical PET-MRI

BackgroundAttenuation correction is one of the most important steps in producing quantitative PET image data. In hybrid PET-MRI systems, this correction is far from trivial, as MRI data are not correlated to PET attenuation properties of the scanned object. Commercially available systems often employ correction schemes based on segmenting the body into different tissue classes (air, lung tissue, fat-, and water-like soft tissue), e.g. by using a dual time-point Dixon sequence. However, several pitfalls are known for this approach. Here a specific artefact of MR-based PET attenuation correction is reported, caused by misidentifying the liver as lung tissue due to iron overload.Case presentationA patient with a history of hematopoietic stem cell transplantation underwent a whole-body [18F]FDG PET-MRI scan. Markedly low liver uptake values were noted in the PET images, seemingly caused by an erroneous assignment of lung tissue attenuation values to the liver. A closer investigation demonstrated markedly low MRI intensity values of the liver, indicative of secondary hemochromatosis (iron overload) most probably due to a history of multiple blood transfusions. Manual assignment of adequate liver attenuation values resulted in more realistic PET images.ConclusionsIron overload of the liver was identified as a cause of a specific attenuation correction artefact. It remains to be seen how frequent this artefact will be encountered; however, this case highlights that attenuation maps should always be checked during PET image interpretation in hybrid PET-MRI.

Technical Note: Comparison of megavoltage, dual-energy, and single-energy CT-based μ-maps for a four-channel breast coil in PET/MRI.

The purpose of this study was to describe and evaluate methods for calculating a megavoltage computed tomography (MVCT)-derived MR hardware attenuation map (μ-map) and dual-energy CT (DECT) for 511 keV photons. Phantom measurements were acquired on a whole-body hybrid PET/MRI system, using a four-channel receive-only MR radiofrequency (RF) breast coil. Two acquisitions were performed: with the phantoms positioned in the four-channel RF breast coil, and without the breast coil. PET attenuation from the breast coil was corrected using three different CT-derived hardware μ-maps: (a) Single-energy CT (SECT), (b) DECT, and (c) MVCT. Each attenuation-corrected (AC) PET volume was evaluated and compared with the acquisition performed without the breast coil. The breast coil attenuated PET photons by 10% overall. Threshold values were applied to the SECT μ-map to reduce the effects of metal artifacts, but overcorrection occurred in more highly attenuated regions. The DECT-derived virtual monochromatic image reduced beam-hardening artifacts, but other metal artifacts remained. Despite the remaining metal artifacts in the DECT image, it led to an improvement in the more attenuated regions. The MVCT images appear to be free from metal artifacts leading to an artifact-free μ-map and a further improvement AC-PET images. Our MVCT-based approach for creating μ-maps for MR RF coils greatly reduces artifacts produced by metal in a SECT approach. This eliminates the need for other artifact reduction methods, including the application of a threshold of narrow beam attenuation coefficients, or disassembling hardware to remove high-Z components before imaging with a kilovoltage source.

Dose optimization: a review of CT imaging for PET attenuation correction

Review limitations and benefits of various options for CT-based attenuation correction for PET/CT studies. With the combination of PET and CT, came the combination of patient radiation dose from the two imaging modalities. Modern advances in CT technology provide opportunities to design CT acquisitions for attenuation correction allowing for the optimization of the attenuation correction acquisition for specific clinical purposes. We reviewed published literature, accepted practices, and the authors’ experience, to identify and classify various tailored approaches for CT attenuation correction of PET emission data. ImageWisely recommends three broad categories for dose optimization of attenuation correction in PET/CT imaging: (1) attenuation correction at diagnostic CT quality (PET/CTD); (2) attenuation correction for anatomic localization only of PET images (PET/CTAL); and (3) for PET attenuation correction only (PET/CTAC). The advantages, disadvantages, and dosimetry for each approach are considered. Modern dose reduction techniques allow some CTAL and CTAC exams to have a sufficient image quality for anatomic localization at radiation dose levels equivalent to the previous radionuclide attenuation correction method with [68-Ge/68-Ga] transmission sources. Thus, the major justifications for combining diagnostic quality CTD with PET would be to provide excellent anatomic image correlation in the cases, where subtle functional or lesion uptake requires greater localization refinement, and to reduce patient dose by removing the CTAC or CTAL exam in conjunction with a separate diagnostic CT in lieu of a single CTD exam. The choice of method depends on a variety of factors, including institutional and physician preference and experience, and technology availability.

Evaluation of a direct motion estimation/correction method in respiratory-gated PET/MRI with motion-adjusted attenuation.

Respiratory motion compensation in PET/CT and PET/MRI is essential as motion is a source of image degradation (motion blur, attenuation artifacts). In previous work, we developed a direct method for joint image reconstruction/motion estimation (JRM) for attenuation-corrected (AC) respiratory-gated PET, which uses a single attenuation-map (μ-map). This approach was successfully implemented for respiratory-gated PET/CT, but since it relied on an accurate μ-map for motion estimation, the question of its applicability in PET/MRI is open. The purpose of this work is to investigate the feasibility of JRM in PET/MRI and to assess the robustness of the motion estimation when a degraded μ-map is used. We performed a series of JRM reconstructions from simulated PET data using a range of simulated Dixon MRI sequence derived μ-maps with wrong attenuation values in the lungs, from -100% (no attenuation) to +100% (double attenuation), as well as truncated arms. We compared the estimated motions with the one obtained from JRM in ideal conditions (no noise, true μ-map as an input). We also applied JRM on 4 patient datasets of the chest, 3 of them containing hot lesions. Patient list-mode data were gated using a principal component analysis method. We compared SUVmax values of the JRM reconstructed activity images and non motion-corrected images. We also assessed the estimated motion fields by comparing the deformed JRM-reconstructed activity with individually non-AC reconstructed gates. Experiments on simulated data showed that JRM-motion estimation is robust to μ-map degradation in the sense that it produces motion fields similar to the ones obtained when using the true μ-map, regardless of the attenuation errors in the lungs (< 0.5% mean absolute difference with the reference motion field). When using a μ-map with truncated arms, JRM estimates a motion field that stretches the μ-map in order to match the projection data. Results on patient datasets showed that using JRM improves the SUVmax values of hot lesions significantly and suppresses motion blur. When the estimated motion fields are applied to the reconstructed activity, the deformed images are geometrically similar to the non-AC individually reconstructed gates. Motion estimation by JRM is robust to variation of the attenuation values in the lungs. JRM successfully compensates for motion when applied to PET/MRI clinical datasets. It provides a potential alternative to existing methods where the motion fields are pre-estimated from separate MRI measurements.

Evaluation of Sinus/Edge-Corrected Zero-Echo-Time-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.

Effect of gradient field nonlinearity distortions in MRI-based attenuation maps for PET reconstruction

PurposeAttenuation correction is a requirement for quantification of the activity distribution in PET. The need to base attenuation correction on MRI instead of CT has arisen with the introduction of integrated PET/MRI systems. The aim was to describe the effect of residual gradient field nonlinearity distortions on PET attenuation correction. MethodsMRI distortions caused by gradient field nonlinearity were simulated in CT images used for attenuation correction in PET reconstructions. The simulations yielded radial distortion of up to ±2.3mm at 15cm from the scanner isocentre for distortion corrected images. The mean radial distortion of uncorrected images were 6.3mm at the same distance. Reconstructions of PET data were performed using the distortion corrected images as well as the images where no correction had been applied. ResultsThe mean relative difference in reconstructed PET uptake intensity due to incomplete distortion correction was less than ±5%. The magnitude of this difference varied between patients and the size of the distortions remaining after distortion correction. ConclusionsRadial distortions of 2mm at 15cm radius from the scanner isocentre lead to PET attenuation correction errors smaller than 5%. Keeping the gradient field nonlinearity distortions below this limit can be a reasonable goal for MRI systems used for attenuation correction in PET for quantification purposes. A higher geometrical accuracy may, however, be warranted for quantification of peripheral lesions. These distortions can, e.g., be controlled at acceptance testing and subsequent quality assurance intervals.

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

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. 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. 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. 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.

Direct Reconstruction of CT-based Attenuation Correction Images for PET with Cluster-Based Penalties.

Extremely low-dose CT acquisitions used for PET attenuation correction have high levels of noise and potential bias artifacts due to photon starvation. This work explores the use of a priori knowledge for iterative image reconstruction of the CT-based attenuation map. We investigate a maximum a posteriori framework with cluster-based multinomial penalty for direct iterative coordinate decent (dICD) reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction used a Poisson log-likelihood data fit term and evaluated two image penalty terms of spatial and mixture distributions. The spatial regularization is based on a quadratic penalty. For the mixture penalty, we assumed that the attenuation map may consist of four material clusters: air+background, lung, soft tissue, and bone. Using simulated noisy sinogram data, dICD reconstruction was performed with different strengths of the spatial and mixture penalties. The combined spatial and mixture penalties reduced the RMSE by roughly 2 times compared to a weighted least square and filtered backprojection reconstruction of CT images. The combined spatial and mixture penalties resulted in only slightly lower RMSE compared to a spatial quadratic penalty alone. For direct PET attenuation map reconstruction from ultra-low dose CT acquisitions, the combination of spatial and mixture penalties offers regularization of both variance and bias and is a potential method to reconstruct attenuation maps with negligible patient dose. The presented results, using a best-case histogram suggest that the mixture penalty does not offer a substantive benefit over conventional quadratic regularization and diminishes enthusiasm for exploring future application of the mixture penalty.