Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring

A review of PET attenuation correction methods for PET-MR

Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.

MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation

Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.

Segmentation and visualization of the human cranial bone by T2* approximation using ultra-short echo time (UTE) magnetic resonance imaging

Segmentation of the human cranial bone from MRI data is challenging, because compact bone is characterized by very short transverse relaxation times and typically produces no signal when using conventional magnetic resonance imaging (MRI) sequences. In this work, we propose a fully automated segmentation algorithm, which uses dual-echo, ultra-short echo-time (UTE) MRI data. The segmentation was initialized by interval thresholding of approximated T₂* relaxation time maps in the range of 1ms<T₂*<3ms. This parameter range was derived from a manual region-of-interest analysis of high resolution data of the cranial layers, resulting in average T₂* relaxation times of 1.7±0.3ms in the lamina externa, 2.5±0.3ms in the diploe and 1.7±0.2ms in the lamina interna. Segmentations were performed based on data of 8 healthy volunteers that were acquired with different acquisition parameters and spatial resolutions to test the stability of the algorithm. Comparison with computed tomography data demonstrated close agreement with the segmented UTE MRI data. Visualization of the segmented cranial bone was performed by volumetric rendering and by using the approximated T₂* values for color coding, clearly visualizing the cranial sutures as well as their intersections.

Accurate hybrid template-based and MR-based attenuation correction using UTE images for simultaneous PET/MR brain imaging applications

Background

Attenuation correction is one of the most crucial correction factors for accurate PET data quantitation in hybrid PET/MR scanners, and computing accurate attenuation coefficient maps from MR brain acquisitions is challenging. Here, we develop a method for accurate bone and air segmentation using MR ultrashort echo time (UTE) images.

Methods

MR UTE images from simultaneous MR and PET imaging of five healthy volunteers was used to generate a whole head, bone and air template image for inclusion into an improved MR derived attenuation correction map, and applied to PET image data for quantitative analysis. Bone, air and soft tissue were segmented based on Gaussian Mixture Models with probabilistic tissue maps as a priori information. We present results for two approaches for bone attenuation coefficient assignments: one using a constant attenuation correction value; and another using an estimated continuous attenuation value based on a calibration fit. Quantitative comparisons were performed to evaluate the accuracy of the reconstructed PET images, with respect to a reference image reconstructed with manually segmented attenuation maps.

Results

The DICE coefficient analysis for the air and bone regions in the images demonstrated improvements compared to the UTE approach, and other state-of-the-art techniques. The most accurate whole brain and regional brain analyses were obtained using constant bone attenuation coefficient values.

Conclusions

A novel attenuation correction method for PET data reconstruction is proposed. Analyses show improvements in the quantitative accuracy of the reconstructed PET images compared to other state-of-the-art AC methods for simultaneous PET/MR scanners. Further evaluation is needed with radiopharmaceuticals other than FDG, and in larger cohorts of participants.

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: 22.4±1.9  dB , 82.6±26.1 HU, and 0.91±0.03 for the 14 patients. The SIs for air, soft-tissue, and bone regions are 0.98±0.01 , 0.88±0.03 , and 0.69±0.08 . 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.

Rapid dual-echo ramped hybrid encoding MR-based attenuation correction (dRHE-MRAC) for PET/MR

PURPOSE

In this study, we propose a rapid acquisition for MR-based attenuation correction (MRAC) in positron emission tomography (PET)/MR imaging, in which an ultrashort echo time (UTE) image and an out-of-phase echo image are obtained within a single rapid scan (35 s) at high spatial resolution (1 mm3 ), which allows accurate estimation of a pseudo CT image using 4-class tissue classification (discrete bone, discrete air, continuous fat, and continuous water).

METHODS

In dual-echo ramped hybrid encoding (dRHE), a UTE echo is directly followed by a second out-of-phase echo, in which hybrid spatial encoding combining single-point imaging and 3-dimensional radial frequency encoding is used to improve the quality of both images. Two-point Dixon reconstruction is used to estimate fat- and water-separated images, and UTE images are used to estimate bone. Air and bone segmentation is improved by using multiple UTE images with an advanced hybrid-encoding scheme that allows reconstruction of multiple UTE images. To evaluate the proposed method, dRHE-MRAC PET/MR brain imaging was performed in 10 subjects. Dice coefficients and PET reconstruction errors relative to CT-based attenuation correction were compared with existing system MRAC approaches.

RESULTS

In dRHE-MRAC, the Dice coefficients for soft tissue, air, and bone were respectively 0.95 ± 0.01, 0.62 ± 0.06, and 0.78 ± 0.05, which was a significantly improved result compared with existing approaches. In most brain regions, dRHE-MRAC showed significantly reduced PET error (less than 1%) with P values less than 0.05.

CONCLUSIONS

Dual-echo ramped hybrid encoding enables rapid and robust imaging for MRAC with a very rapid acquisition. Magn Reson Med 79:2912-2922, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

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

PURPOSE

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).

METHODS

Proton density-weighted ZTE images were acquired as input for MR-based pseudo-CT conversion, providing (1) efficient capture of short-lived bone signals, (2) flat soft-tissue contrast, and (3) fast and robust 3D MR imaging. After bias correction and normalization, the images were segmented into bone, soft-tissue, and air by means of thresholding and morphological refinements. Fixed Hounsfield replacement values were assigned for air (-1000 HU) and soft-tissue (+42 HU), whereas continuous linear mapping was used for bone.

RESULTS

The obtained ZTE-derived pseudo-CT images accurately resembled the true CT images (i.e., Dice coefficient for bone overlap of 0.73 ± 0.08 and mean absolute error of 123 ± 25 HU evaluated over the whole head, including errors from residual registration mismatches in the neck and mouth regions). The linear bone mapping accounted for bone density variations. Averaged across five patients, ZTE-based AC demonstrated a PET error of -0.04 ± 1.68% relative to CT-based AC. Similarly, for RTP assessed in eight patients, the absolute dose difference over the target volume was found to be 0.23 ± 0.42%.

CONCLUSION

The described method enables MR to pseudo-CT image conversion for the head in an accurate, robust, and fast manner without relying on anatomical prior knowledge. Potential applications include PET/MR-AC, and MR-guided RTP.

A review of substitute CT generation for MRI-only radiation therapy

Radiotherapy based on magnetic resonance imaging as the sole modality (MRI-only RT) is an area of growing scientific interest due to the increasing use of MRI for both target and normal tissue delineation and the development of MR based delivery systems. One major issue in MRI-only RT is the assignment of electron densities (ED) to MRI scans for dose calculation and a similar need for attenuation correction can be found for hybrid PET/MR systems. The ED assigned MRI scan is here named a substitute CT (sCT). In this review, we report on a collection of typical performance values for a number of main approaches encountered in the literature for sCT generation as compared to CT. A literature search in the Scopus database resulted in 254 papers which were included in this investigation. A final number of 50 contributions which fulfilled all inclusion criteria were categorized according to applied method, MRI sequence/contrast involved, number of subjects included and anatomical site investigated. The latter included brain, torso, prostate and phantoms. The contributions geometric and/or dosimetric performance metrics were also noted. The majority of studies are carried out on the brain for 5–10 patients with PET/MR applications in mind using a voxel based method. T1 weighted images are most commonly applied. The overall dosimetric agreement is in the order of 0.3–2.5%. A strict gamma criterion of 1% and 1mm has a range of passing rates from 68 to 94% while less strict criteria show pass rates > 98%. The mean absolute error (MAE) is between 80 and 200 HU for the brain and around 40 HU for the prostate. The Dice score for bone is between 0.5 and 0.95. The specificity and sensitivity is reported in the upper 80s% for both quantities and correctly classified voxels average around 84%. The review shows that a variety of promising approaches exist that seem clinical acceptable even with standard clinical MRI sequences. A consistent reference frame for method benchmarking is probably necessary to move the field further towards a widespread clinical implementation.

A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients

AIM

To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor-implemented MR-AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas-based, segmentation-based, and reconstruction-based. These proposed methods in general demonstrated improvements compared to vendor-implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT-AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT-based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use.

METHODS

In total, 11 AC methods were evaluated: two vendor-implemented (MR-AC_DIXON and MR-AC_UTE), five based on template/atlas information (MR-AC_SEGBONE (Koesters et al., 2016), MR-AC_ONTARIO (Anazodo et al., 2014), MR-AC_BOSTON (Izquierdo-Garcia et al., 2014), MR-AC_UCL (Burgos et al., 2014), and MR-AC_MAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR-AC_MLAA (Benoit et al., 2015)), and three based on image-segmentation (MR-AC_MUNICH (Cabello et al., 2015), MR-AC_CAR-RiDR (Juttukonda et al., 2015), and MR-AC_RESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [¹⁸F]FDG (210), [¹¹C]PiB (51), and [¹⁸F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis.

RESULTS

The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR-AC_DIXON (-11.3±3.5)%, MR-AC_UTE (-5.7±2.0)%, MR-AC_ONTARIO (-4.3±3.6)%, MR-AC_MUNICH (3.7±2.1)%, MR-AC_MLAA (-1.9±2.6)%, MR-AC_SEGBONE (-1.7±3.6)%, MR-AC_UCL (0.8±1.2)%, MR-AC_CAR-RiDR (-0.4±1.9)%, MR-AC_MAXPROB (-0.4±1.6)%, MR-AC_BOSTON (-0.3±1.8)%, and MR-AC_RESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR-AC_BOSTON, MR-AC_MAXPROB, MR-AC_RESOLUTE and MR-AC_UCL, ordered alphabetically) showed regional average errors within ±3% of PET with CT-AC in all regions of the brain with FDG, and the same four methods, as well as MR-AC_CAR-RiDR, showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir.

CONCLUSIONS

All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT-based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR-ACMUNICH and MR-ACCAR-RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR-ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor-provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR-AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging.

Comparison between MRI-based attenuation correction methods for brain PET in dementia patients

INTRODUCTION

The combination of Positron Emission Tomography (PET) with magnetic resonance imaging (MRI) in hybrid PET/MRI scanners offers a number of advantages in investigating brain structure and function. A critical step of PET data reconstruction is attenuation correction (AC). Accounting for bone in attenuation maps (μ-map) was shown to be important in brain PET studies. While there are a number of MRI-based AC methods, no systematic comparison between them has been performed so far. The aim of this work was to study the different performance obtained by some of the recent methods presented in the literature. To perform such a comparison, we focused on [¹⁸F]-Fluorodeoxyglucose-PET/MRI neurodegenerative dementing disorders, which are known to exhibit reduced levels of glucose metabolism in certain brain regions.

METHODS

Four novel methods were used to calculate μ-maps from MRI data of 15 patients with Alzheimer's dementia (AD). The methods cover two atlas-based methods, a segmentation method, and a hybrid template/segmentation method. Additionally, the Dixon-based and a UTE-based method, offered by a vendor, were included in the comparison. Performance was assessed at three levels: tissue identification accuracy in the μ-map, quantitative accuracy of reconstructed PET data in specific brain regions, and precision in diagnostic images at identifying hypometabolic areas.

RESULTS

Quantitative regional errors of -20--10 % were obtained using the vendor's AC methods, whereas the novel methods produced errors in a margin of ±5 %. The obtained precision at identifying areas with abnormally low levels of glucose uptake, potentially regions affected by AD, were 62.9 and 79.5 % for the two vendor AC methods, the former ignoring bone and the latter including bone information. The precision increased to 87.5-93.3 % in average for the four new methods, exhibiting similar performances.

CONCLUSION

We confirm that the AC methods based on the Dixon and UTE sequences provided by the vendor are inferior to alternative techniques. As a novel finding, there was no substantial difference between the recently proposed atlas-based, template-based and segmentation-based methods.

Attenuation Correction for Magnetic Resonance Coils in Combined PET/MR Imaging: A Review

With the introduction of clinical PET/magnetic resonance (MR) systems, novel attenuation correction methods are needed, as there are no direct MR methods to measure the attenuation of the objects in the field of view (FOV). A unique challenge for PET/MR attenuation correction is that coils for MR data acquisition are located in the FOV of the PET camera and could induce significant quantitative errors. In this review, current methods and techniques to correct for the attenuation of a variety of coils are summarized and evaluated.

Ultrashort echo-time MRI versus CT for skull aberration correction in MR-guided transcranial focused ultrasound: In vitro comparison on human calvaria

PURPOSE

Transcranial magnetic resonance-guided focused ultrasound (TcMRgFUS) brain treatment systems compensate for skull-induced beam aberrations by adjusting the phase and amplitude of individual ultrasound transducer elements. These corrections are currently calculated based on a preacquired computed tomography (CT) scan of the patient's head. The purpose of the work presented here is to demonstrate the feasibility of using ultrashort echo-time magnetic resonance imaging (UTE MRI) instead of CT to calculate and apply aberration corrections on a clinical TcMRgFUS system.

METHODS

Phantom experiments were performed in three ex-vivo human skulls filled with tissue-mimicking hydrogel. Each skull phantom was imaged with both CT and UTE MRI. The MR images were then segmented into "skull" and "not-skull" pixels using a computationally efficient, threshold-based algorithm, and the resulting 3D binary skull map was converted into a series of 2D virtual CT images. Each skull was mounted in the head transducer of a clinical TcMRgFUS system (ExAblate Neuro, Insightec, Israel), and transcranial sonications were performed using a power setting of approximately 750 acoustic watts at several different target locations within the electronic steering range of the transducer. Each target location was sonicated three times: once using aberration corrections calculated from the actual CT scan, once using corrections calculated from the MRI-derived virtual CT scan, and once without applying any aberration correction. MR thermometry was performed in conjunction with each 10-s sonication, and the highest single-pixel temperature rise and surrounding-pixel mean were recorded for each sonication.

RESULTS

The measured temperature rises were ∼ 45% larger for aberration-corrected sonications than for noncorrected sonications. This improvement was highly significant (p < 10(-4)). The difference between the single-pixel peak temperature rise and the surrounding-pixel mean, which reflects the sharpness of the thermal focus, was also significantly larger for aberration-corrected sonications. There was no significant difference between the sonication results achieved using CT-based and MR-based aberration correction.

CONCLUSIONS

The authors have demonstrated that transcranial focal heating can be significantly improved in vitro by using UTE MRI to compute skull-induced ultrasound aberration corrections. Their results suggest that UTE MRI could be used instead of CT to implement such corrections on current 0.7 MHz clinical TcMRgFUS devices. The MR image acquisition and segmentation procedure demonstrated here would add less than 15 min to a clinical MRgFUS treatment session.

The accuracy of ultrashort echo time MRI sequences for medical additive manufacturing

OBJECTIVES

Additively manufactured bone models, implants and drill guides are becoming increasingly popular amongst maxillofacial surgeons and dentists. To date, such constructs are commonly manufactured using CT technology that induces ionizing radiation. Recently, ultrashort echo time (UTE) MRI sequences have been developed that allow radiation-free imaging of facial bones. The aim of the present study was to assess the feasibility of UTE MRI sequences for medical additive manufacturing (AM).

METHODS

Three morphologically different dry human mandibles were scanned using a CT and MRI scanner. Additionally, optical scans of all three mandibles were made to acquire a "gold standard". All CT and MRI scans were converted into Standard Tessellation Language (STL) models and geometrically compared with the gold standard. To quantify the accuracy of the AM process, the CT, MRI and gold-standard STL models of one of the mandibles were additively manufactured, optically scanned and compared with the original gold-standard STL model.

RESULTS

Geometric differences between all three CT-derived STL models and the gold standard were <1.0 mm. All three MRI-derived STL models generally presented deviations <1.5 mm in the symphyseal and mandibular area. The AM process introduced minor deviations of <0.5 mm.

CONCLUSIONS

This study demonstrates that MRI using UTE sequences is a feasible alternative to CT in generating STL models of the mandible and would therefore be suitable for surgical planning and AM. Further in vivo studies are necessary to assess the usability of UTE MRI sequences in clinical settings.

Dixon Sequence with Superimposed Model-Based Bone Compartment Provides Highly Accurate PET/MR Attenuation Correction of the Brain

Simultaneous PET/MR of the brain is a promising technology for characterizing patients with suspected cognitive impairment or epilepsy. Unlike CT, however, MR signal intensities do not correlate directly with PET photon attenuation correction (AC), and inaccurate radiotracer SUV estimation can limit future PET/MR clinical applications. We tested a novel AC method that supplements standard Dixon-based tissue segmentation with a superimposed model-based bone compartment.

METHODS

We directly compared SUV estimation between MR-based AC and reference CT AC in 16 patients undergoing same-day PET/CT and PET/MR with a single (18)F-FDG dose for suspected neurodegeneration. Three Dixon-based MR AC methods were compared with CT: standard Dixon 4-compartment segmentation alone, Dixon with a superimposed model-based bone compartment, and Dixon with a superimposed bone compartment and linear AC optimized specifically for brain tissue. The brain was segmented using a 3-dimensional T1-weighted volumetric MR sequence, and SUV estimations were compared with CT AC for whole-image, whole-brain, and 91 FreeSurfer-based regions of interest.

RESULTS

Modifying the linear AC value specifically for brain and superimposing a model-based bone compartment reduced the whole-brain SUV estimation bias of Dixon-based PET/MR AC by 95% compared with reference CT AC (P < 0.05), resulting in a residual -0.3% whole-brain SUVmean bias. Further, brain regional analysis demonstrated only 3 frontal lobe regions with an SUV estimation bias of 5% or greater (P < 0.05). These biases appeared to correlate with high individual variability in frontal bone thickness and pneumatization.

CONCLUSION

Bone compartment and linear AC modifications result in a highly accurate MR AC method in subjects with suspected neurodegeneration. This prototype MR AC solution appears equivalent to other recently proposed solutions and does not require additional MR sequences and scanning time. These data also suggest that exclusively model-based MR AC approaches may be adversely affected by common individual variations in skull anatomy.

MR Imaging-Guided Attenuation Correction of PET Data in PET/MR Imaging

Attenuation correction (AC) is one of the most important challenges in the recently introduced combined PET/magnetic resonance (MR) scanners. PET/MR AC (MR-AC) approaches aim to develop methods that allow accurate estimation of the linear attenuation coefficients of the tissues and other components located in the PET field of view. MR-AC methods can be divided into 3 categories: segmentation, atlas, and PET based. This review provides a comprehensive list of the state-of-the-art MR-AC approaches and their pros and cons. The main sources of artifacts are presented. Finally, this review discusses the current status of MR-AC approaches for clinical applications.

Joint Estimation of Activity and Attenuation in Whole-Body TOF PET/MRI Using Constrained Gaussian Mixture Models

It has recently been shown that the attenuation map can be estimated from time-of-flight (TOF) PET emission data using joint maximum likelihood reconstruction of attenuation and activity (MLAA). In this work, we propose a novel MRI-guided MLAA algorithm for emission-based attenuation correction in whole-body PET/MR imaging. The algorithm imposes MR spatial and CT statistical constraints on the MLAA estimation of attenuation maps using a constrained Gaussian mixture model (GMM) and a Markov random field smoothness prior. Dixon water and fat MR images were segmented into outside air, lung, fat and soft-tissue classes and an MR low-intensity (unknown) class corresponding to air cavities, cortical bone and susceptibility artifacts. The attenuation coefficients over the unknown class were estimated using a mixture of four Gaussians, and those over the known tissue classes using unimodal Gaussians, parameterized over a patient population. To eliminate misclassification of spongy bones with surrounding tissues, and thus include them in the unknown class, we heuristically suppressed fat in water images and also used a co-registered bone probability map. The proposed MLAA-GMM algorithm was compared with the MLAA algorithms proposed by Rezaei and Salomon using simulation and clinical studies with two different tracer distributions. The results showed that our proposed algorithm outperforms its counterparts in suppressing the cross-talk and scaling problems of activity and attenuation and thus produces PET images of improved quantitative accuracy. It can be concluded that the proposed algorithm effectively exploits the MR information and can pave the way toward accurate emission-based attenuation correction in TOF PET/MRI.

Multi-contrast attenuation map synthesis for PET/MR scanners: assessment on FDG and Florbetapir PET tracers

Positron Emission Tomography/Magnetic Resonance Imaging (PET/MR) scanners are expected to offer a new range of clinical applications. Attenuation correction is an essential requirement for quantification of PET data but MRI images do not directly provide a patient-specific attenuation map.

Methods We further validate and extend a Computed Tomography (CT) and attenuation map (μ-map) synthesis method based on pre-acquired MRI-CT image pairs. The validation consists of comparing the CT images synthesised with the proposed method to the original CT images. PET images were acquired using two different tracers (¹⁸F-FDG and ¹⁸F-florbetapir). They were then reconstructed and corrected for attenuation using the synthetic μ-maps and compared to the reference PET images corrected with the CT-based μ-maps. During the validation, we observed that the CT synthesis was inaccurate in areas such as the neck and the cerebellum, and propose a refinement to mitigate these problems, as well as an extension of the method to multi-contrast MRI data.

Results With the improvements proposed, a significant enhancement in CT synthesis, which results in a reduced absolute error and a decrease in the bias when reconstructing PET images, was observed. For both tracers, on average, the absolute difference between the reference PET images and the PET images corrected with the proposed method was less than 2%, with a bias inferior to 1%.

Conclusion With the proposed method, attenuation information can be accurately derived from MRI images by synthesising CT using routine anatomical sequences. MRI sequences, or combination of sequences, can be used to synthesise CT images, as long as they provide sufficient anatomical information.

PET/MR brain imaging: evaluation of clinical UTE-based attenuation correction

One of the greatest challenges in PET/MR imaging is that of accurate MR-based attenuation correction (AC) of the acquired PET data, which must be solved if the PET/MR modality is to reach its full potential. The aim of this study was to investigate the performance of Siemens' most recent version (VB20P) of MR-based AC of head PET data, by comparing it to CT-based AC.

METHODS

(18)F-FDG PET data from seven lymphoma and twelve lung cancer patients examined with a Biograph mMR PET/MR system were reconstructed with both CT-based and MR-based AC, avoiding sources of error arising when comparing PET data from different systems. The resulting images were compared quantitatively by measuring changes in mean SUV in ten different brain regions in both hemispheres, as well as the brainstem. In addition, the attenuation maps (μ maps) were compared regarding volume and localization of cranial bone.

RESULTS

The UTE μ maps clearly overestimate the amount of bone in the neck, while slightly underestimating the amount of bone in the cranium, and the localization of bone in the cranial region also differ from the CT μ maps. In air/tissue interfaces in the sinuses and ears, the MRAC method struggles to correctly classify the different tissues. The misclassification of tissue is most likely caused by a combination of artefacts and the insufficiency of the UTE method to accurately separate bone. Quantitatively, this results in a combination of overestimation (0.5-3.6 %) and underestimation (2.7-5.2 %) of PET activity throughout the brain, depending on the proximity to the inaccurate regions.

CONCLUSIONS

Our results indicate that the performance of the UTE method as implemented in VB20P is close to the theoretical maximum of such an MRAC method in the brain, while it does not perform satisfactorily in the neck or face/nasal area. Further improvement of the UTE MRAC or other available methods for more accurate segmentation of bone should be incorporated.

Short T2 tissue imaging with the Pointwise Encoding Time reduction with Radial Acquisition (PETRA) sequence: the additional value of fat saturation and subtraction in the meniscus

PURPOSE

The purposes of this study were (1) to compare single-echo PETRA with dual-echo PETRA using in vivo MR imaging, (2) to compare non-fat-saturated PETRA with fat-saturated PETRA using a 3-T clinical MR scanner, and (3) to determine the effect of the adequate sequence and post-processing method.

MATERIALS AND METHODS

Twenty-two patients underwent dual-echo 3D PETRA sequence knee MR imagining (TE of 70μs and 2.3ms) with and without fat-saturation using a 3T clinical MR scanner (Magnetom Trio, Siemens, Erlangen, Germany). The study population was classified into two groups: (1) normal meniscus on conventional MR images with no related physical examination on medical records and (2) meniscal degeneration or tear. We reformatted four image sets: (1) ultrashort TE signal without fat-saturation, (2) ultrashort TE signal with fat-saturation, (3) weighted-subtraction image of dual-echo PETRA images without fat-saturation, and (4) weighted-subtraction image with fat-saturation. For the weighted-subtraction images, the ultrashort TE image was rescaled relative to the second echo image with weighting factors from 0.6 based on SNR and CNR analyses. For quantitative assessment, the mean signal intensities inside user-drawn regions of interest (ROIs) were drawn and recorded. For statistical analyses, the t-test was used to compare the two groups and effect size was used for comparison of the discrimination power.

RESULTS

In all image sets, the mean signal intensity values were lower in patients with meniscal degeneration/tear compared to controls on both fat-saturated and non-fat-saturated MR images. The single-echo ultrashort TE images showed higher effect sizes than the weighted-subtraction image of dual-echo images.

CONCLUSION

We demonstrated that there was significantly lower signal intensity on ultrashort TE PETRA images in the patients with meniscal pathologies. In addition, the single-echo of the ultrashort TE PETRA images echo time could be a more sensitive indicator between normal and pathologic meniscus conditions in patients.

Zero TE MR bone imaging in the head

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.

Integrated whole body MR/PET: where are we?

Whole body integrated magnetic resonance imaging (MR)/positron emission tomography (PET) imaging systems have recently become available for clinical use and are currently being used to explore whether the combined anatomic and functional capabilities of MR imaging and the metabolic information of PET provide new insight into disease phenotypes and biology, and provide a better assessment of oncologic diseases at a lower radiation dose than a CT. This review provides an overview of the technical background of combined MR/PET systems, a discussion of the potential advantages and technical challenges of hybrid MR/PET instrumentation, as well as collection of possible solutions. Various early clinical applications of integrated MR/PET are also addressed. Finally, the workflow issues of integrated MR/PET, including maximizing diagnostic information while minimizing acquisition time are discussed.

Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies

Attenuation correction is an essential requirement for quantification of positron emission tomography (PET) data. In PET/CT acquisition systems, attenuation maps are derived from computed tomography (CT) images. However, in hybrid PET/MR scanners, magnetic resonance imaging (MRI) images do not directly provide a patient-specific attenuation map. The aim of the proposed work is to improve attenuation correction for PET/MR scanners by generating synthetic CTs and attenuation maps. The synthetic images are generated through a multi-atlas information propagation scheme, locally matching the MRI-derived patient's morphology to a database of MRI/CT pairs, using a local image similarity measure. Results show significant improvements in CT synthesis and PET reconstruction accuracy when compared to a segmentation method using an ultrashort-echo-time MRI sequence and to a simplified atlas-based method.

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring

PURPOSE

Ultrashort echo time (UTE) MRI has been proposed as a way to produce segmented attenuation maps for PET, as it provides contrast between bone, air, and soft tissue. However, UTE sequences require samples to be acquired during rapidly changing gradient fields, which makes the resulting images prone to eddy current artifacts. In this work it is demonstrated that this can lead to misclassification of tissues in segmented attenuation maps (AC maps) and that these effects can be corrected for by measuring the true k-space trajectories using a magnetic field camera.

METHODS

The k-space trajectories during a dual echo UTE sequence were measured using a dynamic magnetic field camera. UTE images were reconstructed using nominal trajectories and again using the measured trajectories. A numerical phantom was used to demonstrate the effect of reconstructing with incorrect trajectories. Images of an ovine leg phantom were reconstructed and segmented and the resulting attenuation maps were compared to a segmented map derived from a CT scan of the same phantom, using the Dice similarity measure. The feasibility of the proposed method was demonstrated in in vivo cranial imaging in five healthy volunteers. Simulated PET data were generated for one volunteer to show the impact of misclassifications on the PET reconstruction.

RESULTS

Images of the numerical phantom exhibited blurring and edge artifacts on the bone-tissue and air-tissue interfaces when nominal k-space trajectories were used, leading to misclassification of soft tissue as bone and misclassification of bone as air. Images of the tissue phantom and the in vivo cranial images exhibited the same artifacts. The artifacts were greatly reduced when the measured trajectories were used. For the tissue phantom, the Dice coefficient for bone in MR relative to CT was 0.616 using the nominal trajectories and 0.814 using the measured trajectories. The Dice coefficients for soft tissue were 0.933 and 0.934 for the nominal and measured cases, respectively. For air the corresponding figures were 0.991 and 0.993. Compared to an unattenuated reference image, the mean error in simulated PET uptake in the brain was 9.16% when AC maps derived from nominal trajectories was used, with errors in the SUV max for simulated lesions in the range of 7.17%-12.19%. Corresponding figures when AC maps derived from measured trajectories were used were 0.34% (mean error) and -0.21% to +1.81% (lesions).

CONCLUSIONS

Eddy current artifacts in UTE imaging can be corrected for by measuring the true k-space trajectories during a calibration scan and using them in subsequent image reconstructions. This improves the accuracy of segmented PET attenuation maps derived from UTE sequences and subsequent PET reconstruction.