MR-based attenuation correction and scatter correction in neurological PET/MR imaging with 18F-FDG

The aim was to investigate the effects of MR-based attenuation correction (MRAC) and scatter correction to positron emission tomography (PET) image quantification in neurological PET/MR with 18F-FDG. A multi-center phantom study was conducted to investigate the effect of MRAC between PET/MR and PET/CT systems (I). An MRAC method to derive bone from T1-weighted MR images was developed (II, III). Finally, scatter correction accuracy with MRAC was investigated (IV). The results show that the quantitative accuracy in PET is well-comparable be-tween PET/MR and PET/CT systems when an attenuation correction method resembling CT-based attenuation correction (CTAC) is implemented. This al-lows achieving of a PET bias within standard uptake value (SUV) quantification repeatability (< 10 % error) and is within the repeatability of PET in most sys-tems and brain regions (< 5 % error). In addition, MRAC considering soft tissue, air and bone can be derived using T1-weighted images alone. The improved version of the MRAC method allows achieving a quantitative accuracy feasible for advanced applications (< 5 % error). MRAC has a minor effect on the scatter correction accuracy (< 3 % error), even when using MRAC without bone. In conclusion, MRAC can be considered the largest contributing factor to PET quantification bias in 18F-FDG neurological PET/MR. This finding is not explicitly limited only to 18F-FDG imaging. Once an MRAC method that performs close to CTAC is implemented, there is no reason why a PET/MR system would perform differently from a PET/CT system. Such an MRAC method has been developed and is freely available (http://bit.ly/2fx6Jjz). Scatter correction can be considered a non-issue in neurological PET/MR imaging when using 18F-FD

Evaluation of three methods for delineation and attenuation estimation of the sinus region in MR-based attenuation correction for brain PET-MR imaging

Background Attenuation correction is crucial in quantitative positron emission tomography-magnetic resonance (PET-MRI) imaging. We evaluated three methods to improve the segmentation and modelling of the attenuation coefficients in the nasal sinus region. Two methods (cuboid and template method) included a MRI-CT conversion model for assigning the attenuation coefficients in the nasal sinus region, whereas one used fixed attenuation coefficient assignment (bulk method). Methods The study population consisted of data of 10 subjects which had undergone PET-CT and PET-MRI. PET images were reconstructed with and without time-of-flight (TOF) using CT-based attenuation correction (CTAC) as reference. Comparison was done visually, using DICE coefficients, correlation, analyzing attenuation coefficients, and quantitative analysis of PET and bias atlas images. Results The median DICE coefficients were 0.824, 0.853, 0.849 for the bulk, cuboid and template method, respectively. The median attenuation coefficients were 0.0841 cm-1, 0.0876 cm-1, 0.0861 cm-1 and 0.0852 cm-1, for CTAC, bulk, cuboid and template method, respectively. The cuboid and template methods showed error of less than 2.5% in attenuation coefficients. An increased correlation to CTAC was shown with the cuboid and template methods. In the regional analysis, improvement in at least 49% and 80% of VOI was seen with non-TOF and TOF imaging. All methods showed errors less than 2.5% in non-TOF and less than 2% in TOF reconstructions. Conclusions We evaluated two proof-of-concept methods for improving quantitative accuracy in PET/MRI imaging and showed that bias can be further reduced by inclusion of TOF. Largest improvements were seen in the regions of olfactory bulb, Heschl's gyri, lingual gyrus and cerebellar vermis. However, the overall effect of inclusion of the sinus region as separate class in MRAC to PET quantification in the brain was considered modest.</p

Are Quantitative Errors Reduced with Time-of-Flight Reconstruction When Using Imperfect MR-Based Attenuation Maps for F-18-FDG PET/MR Neuroimaging?

We studied whether TOF reduces error propagation from attenuation correction to PET image reconstruction in PET/MR neuroimaging, by using imperfect attenuation maps in a clinical PET/MR system with 525 ps timing resolution. Ten subjects who had undergone F-18-FDG PET neuroimaging were included. Attenuation maps using a single value (0.100 cm(-1)) with and without air, and a 3-class attenuation map with soft tissue (0.096 cm(-1)), air and bone (0.151 cm(-1)) were used. CT-based attenuation correction was used as a reference. Volume-of-interest (VOI) analysis was conducted. Mean bias and standard deviation across the brain was studied. Regional correlations and concordance were evaluated. Statistical testing was conducted. Average bias and standard deviation were slightly reduced in the majority (23-26 out of 35) of the VOI with TOF. Bias was reduced near the cortex, nasal sinuses, and in the mid-brain with TOF. Bland-Altman and regression analysis showed small improvements with TOF. However, the overall effect of TOF to quantitative accuracy was small (3% at maximum) and significant only for two attenuation maps out of three at 525 ps timing resolution. In conclusion, TOF might reduce the quantitative errors due to attenuation correction in PET/MR neuroimaging, but this effect needs to be further investigated on systems with better timing resolution.</p

Assessment of MRI-based Attenuation Correction for MRI-only Radiotherapy Treatment Planning of the Brain

Magnetic resonance imaging-only radiotherapy treatment planning (MRI-only RTP) and positron emission tomography (PET)-MRI imaging require generation of synthetic computed tomography (sCT) images from MRI images. In this study, initial dosimetric evaluation was performed for a previously developed MRI-based attenuation correction (MRAC) method for use in MRI-only RTP of the brain. MRAC-based sCT images were retrospectively generated from Dixon MR images of 20 patients who had previously received external beam radiation therapy (EBRT). Bone segmentation performance and Dice similarity coefficient of the sCT conversion method were evaluated for bone volumes on CT images. Dose calculation accuracy was assessed by recalculating the CT-based EBRT plans using the sCT images as the base attenuation data. Dose comparison was done for the sCT- and CT-based EBRT plans in planning target volume (PTV) and organs at risk (OAR). Parametric dose comparison showed mean relative differences of <0.4% for PTV and <1.0% for OARs. Mean gamma index pass rates of 95.7% with the 2%/2 mm agreement criterion and 96.5% with the 1%/1 mm agreement criterion were determined for glioma and metastasis patients, respectively. Based on the results, MRI-only RTP using sCT images generated from MRAC images can be a feasible alternative for radiotherapy of the brain.publishedVersionPeer reviewe

Assessment of MRI-Based Attenuation Correction for MRI-Only Radiotherapy Treatment Planning of the Brain

Magnetic resonance imaging-only radiotherapy treatment planning (MRI-only RTP) and positron emission tomography (PET)-MRI imaging require generation of synthetic computed tomography (sCT) images from MRI images. In this study, initial dosimetric evaluation was performed for a previously developed MRI-based attenuation correction (MRAC) method for use in MRI-only RTP of the brain. MRAC-based sCT images were retrospectively generated from Dixon MR images of 20 patients who had previously received external beam radiation therapy (EBRT). Bone segmentation performance and Dice similarity coefficient of the sCT conversion method were evaluated for bone volumes on CT images. Dose calculation accuracy was assessed by recalculating the CT-based EBRT plans using the sCT images as the base attenuation data. Dose comparison was done for the sCT- and CT-based EBRT plans in planning target volume (PTV) and organs at risk (OAR). Parametric dose comparison showed mean relative differences of <0.4% for PTV and <1.0% for OARs. Mean gamma index pass rates of 95.7% with the 2%/2 mm agreement criterion and 96.5% with the 1%/1 mm agreement criterion were determined for glioma and metastasis patients, respectively. Based on the results, MRI-only RTP using sCT images generated from MRAC images can be a feasible alternative for radiotherapy of the brain

Classification of ischemia from myocardial polar maps in 15 O-H 2 O cardiac perfusion imaging using a convolutional neural network

We implemented a two-dimensional convolutional neural network (CNN) for classification of polar maps extracted from Carimas (Turku PET Centre, Finland) software used for myocardial perfusion analysis. 138 polar maps from O-15-H2O stress perfusion study in JPEG format from patients classified as ischemic or non-ischemic based on finding obstructive coronary artery disease (CAD) on invasive coronary artery angiography were used. The CNN was evaluated against the clinical interpretation. The classification accuracy was evaluated with: accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE) and precision (PRE). The CNN had a median ACC of 0.8261, AUC of 0.8058, F1S of 0.7647, SEN of 0.6500, SPE of 0.9615 and PRE of 0.9286. In comparison, clinical interpretation had ACC of 0.8696, AUC of 0.8558, F1S of 0.8333, SEN of 0.7500, SPE of 0.9615 and PRE of 0.9375. The CNN classified only 2 cases differently than the clinical interpretation. The clinical interpretation and CNN had similar accuracy in classifying false positives and true negatives. Classification of ischemia is feasible in 15O-H2O stress perfusion imaging using JPEG polar maps alone with a custom CNN and may be useful for the detection of obstructive CAD.</p

Myocardial perfusion reserve of kidney transplant patients is well preserved

Background: Chronic kidney disease (CKD) is associated with endothelial dysfunction and increased cardiovascular mortality. Endothelial dysfunction can be studied measuring myocardial perfusion reserve (MPR). MPR is the ratio of stress and rest myocardial perfusion (MP) and reflects the capacity of vascular bed to increase perfusion and microvascular responsiveness. In this pilot study, our aim was to assess MPR of 19 patients with kidney transplant (CKD stages 2–3) and of ten healthy controls with quantitative [15O]H2O positron emission tomography (PET) method.Results: Basal MP was statistically significantly higher at rest in the kidney transplant patients than in the healthy controls [1.3 (0.4) ml/min/g and 1.0 (0.2) ml/min/g, respectively, p = 0.0015]. After correction of basal MP by cardiac workload [MPcorr = basal MP/individual rate pressure product (RPP) × average RPP of the healthy controls], the difference between the groups disappeared [0.9 (0.2) ml/min/g and 1.0 (0.3) ml/min/g, respectively, p = 0.55)]. There was no difference in stress MP between the kidney transplant patients and the healthy subjects [3.8 (1.0) ml/min/g and 4.0 (0.9) ml/min/g, respectively, p = 0.53]. Although MPR was reduced, MPRcorr (stress MP/basal MPcorr) did not differ between the kidney transplant patients and the healthy controls [4.1 (1.1) and 4.3 (1.6), respectively, p = 0.8].Conclusions: MP during stress is preserved in kidney transplant patients with CKD stage 2–3. The reduced MPR appears to be explained by increased resting MP. This is likely linked with increased cardiac workload due to sympathetic overactivation in kidney transplant patients.</p

Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification

Background.Dual-gating reduces respiratory and cardiac motion effects but increases noise. With motion correction, motion is minimized and image quality preserved. We applied motion correction to create end-diastolic respiratory motion corrected images from dual-gated images.Methods.[F-18]-fluorodeoxyglucose ([F-18]-FDG) PET images of 13 subjects were reconstructed with 4 methods: non-gated, dual-gated, motion corrected, and motion corrected with 4D-CT (MoCo-4D). Image quality was evaluated using standardized uptake values, contrast ratio, signal-to-noise ratio, coefficient of variation, and contrast-to-noise ratio. Motion minimization was evaluated using myocardial wall thickness.Results.MoCo-4D showed improvement for contrast ratio (2.83 vs 2.76), signal-to-noise ratio (27.5 vs 20.3) and contrast-to-noise ratio (14.5 vs 11.1) compared to dual-gating. The uptake difference between MoCo-4D and non-gated images was non-significant (P > .05) for the myocardium (2.06 vs 2.15 g/mL), but significant (P Conclusions.End-diastolic respiratory motion correction and 4D-CT resulted in improved motion minimization and image quality over standard dual-gating.</p

Improvement of the female mouse computational model developed at CDTN

Small animals, such as mice, have been used in experiments involving ionizing radiation. New preclinical experimental methods often include extensive imaging (MicroCT and/or PET/SPECT) that can result in absorbed dose values considerably high. In addition, assays with theranosticsradiopharmaceuticals administered in small animals have been used to determine the main potential adverse effects and the therapeutic efficacy. For all these mentioned cases, the precise quantification of absorbed doses and the determination of energy deposition patterns are of fundamental importance to qualify or exclude potential radiobiological effects that may interfere with in vivo experiment results. Thus, the development and improvement of mouse phantoms is essential for good small animal dosimetry. In 2021, our group segmented and implemented a female C57BL mouse phantom, called FM_BRA, in the MCNP. The objective of this work was to review the segmentation of the FM_BRA computational model and to identify and segment new organs for an improved version of this phantom. Three different researchers segmented different organs of the model. The masses of the segmented organs were compared with those of the first version. Information on mass or volume of organs from different mouse strains, and more specifically from the C57BL strain, was also obtained from the literature for comparison and to aid in segmentation. The mice image representing a female mouse of the C57BL strain weighing 26 g were kindly provided by the Turku Center for Disease and were manually segmented. The software GIMP® 2.10 was used to select and segment each organ/tissue. The IMAIOS-VET Anatomy website was used as an anatomical basis for the identification of organs/tissues. The IMAGEJ® software was applied to assemble the segmented images into a 3D stack and to convert the segmented images into binary files. The volumes of the segmented organs were measured with a C++ in house program. Corresponding human tissue densities provided in ICRP 110 were used to calculate organ mass from the calculated volumes. Data were compared with literature reports. The number of segmented organs increased from 20 in the old model to 33 in the new models. The masses of the organs segmented in this work, by the different researchers, showed agreement in most cases. However, organs such as the small intestines, bones and trachea still deserve a new round of reviewing

Explainable deep-learning-based ischemia detection using hybrid O-15 H2O perfusion positron emission tomography and computed tomography imaging with clinical data

Background: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 H2O perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings. Methods: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image—and data-based classifier considering 35 clinical, CTA, and PET variables. Data from invasive coronary angiography were used as reference. Performance was evaluated with clinical classification using accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE), precision (PRE), net benefit, and Cohen's Kappa. Statistical testing was conducted using McNemar's test. Results: The DL model had a median ACC = 0.8478, AUC = 0.8481, F1S = 0.8293, SEN = 0.8500, SPE = 0.8846, and PRE = 0.8500. Improved detection of true-positive and false-negative cases, increased net benefit in thresholds up to 34%, and comparable Cohen's kappa was seen, reaching similar performance to clinical reading. Statistical testing revealed no significant differences between DL model and clinical reading. Conclusions: The combined DL model is a feasible and an effective method in detection of CAD, allowing to highlight important data findings individually in interpretable manner