Nikolaos Dikaios

STIR: software for tomographic image reconstruction release 2

We present an update to STIR, an Open Source object-oriented library in C++ for 3D PET reconstruction. This library has been designed so that it can be used for many algorithms and scanner geometries, while being portable to various computing platforms. This second release enhances its flexibility and modular design, but also adds extra capabilities such as list mode reconstruction, more data formats etc.

MRI-based motion correction of thoracic PET: initial comparison of acquisition protocols and correction strategies suitable for simultaneous PET/MRI systems

ObjectivesMagnetic resonance imaging (MRI) acquired on equipment capable of simultaneous MRI and positron emission tomography (PET) could potentially provide the gold standard method for motion correction of PET. To assess the latter, in this study we compared fast 2D and 3D MRI of the torso and used deformation parameters from real MRI data to correct simulated PET data for respiratory motion.MethodsPET sinogram data were simulated using SimSET from a 4D pseudo-PET image series created by segmenting MR images acquired over a respiratory cycle. Motion-corrected PET images were produced using post-reconstruction registration (PRR) and motion-compensated image reconstruction (MCIR).ResultsMRI-based motion correction improved PET image quality at the lung-liver and lung-spleen boundaries and in the heart but little improvement was obtained where MRI contrast was low. The root mean square error in SUV units per voxel compared to a motion-free image was reduced from 0.0271 (no motion correction) to 0.0264 (PRR) and 0.0250 (MCIR).ConclusionsMotion correction using MRI can improve thoracic PET images but there are limitations due to the quality of fast MRI.

Respiratory motion correction in dynamic MRI using robust data decomposition registration - application to DCE-MRI.

Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC60) at early enhancement.

Microstructural characterization of normal and malignant human prostate tissue with vascular, extracellular, and restricted diffusion for cytometry in tumours magnetic resonance imaging.

ObjectiveThe aim of this study was to demonstrate the feasibility of the recently introduced Vascular, Extracellular, and Restricted Diffusion for Cytometry in Tumours (VERDICT) framework for imaging prostate cancer with diffusion-weighted magnetic resonance imaging (DW-MRI) within a clinical setting. Materials and MethodsThe VERDICT framework is a noninvasive microstructure imaging technique that combines an in-depth diffusion MRI acquisition with a mathematical model to estimate and map microstructural tissue parameters such as cell size and density and vascular perfusion. In total, 8 patients underwent 3-T MRI using 9 different b values (100–3000 s/mm2). All patients were imaged before undergoing biopsy. Experiments with VERDICT analyzed DW-MRI data from patients with histologically confirmed prostate cancer in areas of cancerous and benign peripheral zone tissue. For comparison, we also fitted commonly used diffusion models such as the apparent diffusion coefficient (ADC), the intravoxel incoherent motion (IVIM), and the kurtosis model. We also investigated correlations of ADC and kurtosis with VERDICT parameters to gain some biophysical insight into the various parameter values. ResultsEight patients had prostate cancer in the peripheral zone, with Gleason score 3 + 3 (n = 1), 3 + 4 (n = 6), and 4 + 3 (n = 1). The VERDICT model identified a significant increase in the intracellular and vascular volume fraction estimates in cancerous compared with benign peripheral zone, as well as a significant decrease in the volume of the extracellular-extravascular space (EES) (P = 0.05). This is in agreement with manual segmentation of the biopsies for prostate tissue component analysis, which found proliferation of epithelium, loss of surrounding stroma, and an increase in vasculature. The standard ADC and kurtosis parameters were also significantly different (P = 0.05) between tissue types. There was no significant difference in any of the IVIM parameters (P = 0.11 to 0.29). The VERDICT parametric maps from voxel-by-voxel fitting clearly differentiated cancer from benign regions. Kurtosis and ADC parameters correlated most strongly with VERDICT’s intracellular volume fraction but also moderately with the EES and vascular fractions. ConclusionsThe VERDICT model distinguished tumor from benign areas, while revealing differences in microstructure descriptors such as cellular, vascular, and EES fractions. The parameters of ADC and kurtosis models also discriminated between cancer and benign regions. However, VERDICT provides more specific information that disentangles the various microstructural features underlying the changes in ADC and kurtosis. These results highlight the clinical potential of the VERDICT framework and motivate the construction of a shorter, clinically viable imaging protocol to enable larger trials leading to widespread translation of the method.

Dynamic MR Image Reconstruction–Separation From Undersampled (

Dynamic magnetic resonance imaging (MRI) is used in multiple clinical applications, but can still benefit from higher spatial or temporal resolution. A dynamic MR image reconstruction method from partial ( k, t)-space measurements is introduced that recovers and inherently separates the information in the dynamic scene. The reconstruction model is based on a low-rank plus sparse decomposition prior, which is related to robust principal component analysis. An algorithm is proposed to solve the convex optimization problem based on an alternating direction method of multipliers. The method is validated with numerical phantom simulations and cardiac MRI data against state of the art dynamic MRI reconstruction methods. Results suggest that using the proposed approach as a means of regularizing the inverse problem remains competitive with state of the art reconstruction techniques. Additionally, the decomposition induced by the reconstruction is shown to help in the context of motion estimation in dynamic contrast enhanced MRI.

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Background:Multiparametric magnetic resonance imaging (mp-MRI) is increasingly advocated for prostate cancer detection. There are limited reports of its use in the setting of radiorecurrent disease. Our aim was to assess mp-MRI for detection of radiorecurrent prostate cancer and examine the added value of its functional sequences.Methods:Thirty-seven men with mean age of 69.7 (interquartile range, 66–74) with biochemical failure after external beam radiotherapy underwent mp-MRI (T2-weighted, high b-value, multi-b-value apparent diffusion coefficient (ADC) and dynamic contrast-enhanced (DCE) imaging); then transperineal systematic template prostate mapping (TPM) biopsy. Using a locked sequential read paradigm (with the sequence order above), two experienced radiologists independently reported mp-MRI studies using score 1–5. Radiologist scores were matched with TPM histopathology at the hemigland level (n=74). Accuracy statistics were derived for each reader. Interobserver agreement was evaluated using kappa statistics.Results:Receiver–operator characteristic area under curve (AUC) for readers 1 and 2 increased from 0.67 (95% confidence interval (CI), 0.55–0.80) to 0.80 (95% CI, 0.69–0.91) and from 0.67 (95% CI, 0.55–0.80) to 0.84 (95% CI, 0.76–0.93), respectively, between T2-weighted imaging alone and full mp-MRI reads. Addition of ADC maps and DCE imaging to the examination did not significantly improve AUC for either reader (P=0.08 and 0.47 after adding ADC, P=0.90 and 0.27 after adding DCE imaging) compared with T2+high b-value review. Inter-reader agreement increased from k=0.39 to k=0.65 between T2 and full mp-MRI review.Conclusions:mp-MRI can detect radiorecurrent prostate cancer. The optimal examination included T2-weighted imaging and high b-value DWI; adding ADC maps and DCE imaging did not significantly improve the diagnostic accuracy.

)-Space via Low-Rank Plus Sparse Prior

OBJECTIVE To determine whether multiparametric magnetic resonance imaging (mp-MRI) has a role in reducing the uncertainty in risk stratification by transrectal ultrasound (TRUS) biopsy, using histology at transperineal template-guided prostate mapping (TPM) biopsy as the reference test. MATERIALS AND METHODS Overall, 194 patients underwent TRUS biopsy, who were followed up in less than 18 months by means of (a) mp-MRI with pelvic phased array using T2-weighted, diffusion-weighted and dynamic contrast-enhanced sequences and (b) TPM biopsy. Of those patients, low risk on TRUS biopsy was defined in 4 different ways--(a) definition 1: Gleason 3+3 (any cancer core length) (n = 137), (b) definition 2: maximum cancer core length (MCCL)<50% (any Gleason score) (n = 62), (c) definition 3: Gleason 3+3 and MCCL<50% (n = 52), and (d) definition 4: Gleason 3+3, MCCL<50%, prostate-specific antigen level<10 ng/ml, and<50% positive cores (n = 28). Mp-MRI was scored for the likelihood of cancer from 1 (cancer very unlikely) to 5 (cancer very likely). Binary logistic regression analysis was performed to evaluate the association between MRI scores and TPM histology. RESULTS Median prostate-specific antigen level was 7 ng/ml (range: 0.9-29), median time between TRUS biopsy and mp-MRI was 120 days (range: 41-480), and median time between mp-MRI and TPM biopsy was 60 days (range: 1-420). A median of 48 cores (range: 20-118) were taken at TPM biopsy. Gleason score was upgraded in 62 of 137 (45%) patients at TPM biopsy. The negative predictive values of mp-MRI score 1 to 2 for predicting that cancer remained low risk (according to each definition) were 75%, 100%, 83%, and 100% for definitions 1, 2, 3, and 4, respectively. An mp-MRI score of 4 to 5 had positive predictive values for upgrade or upsize of 59%, 67%, 75%, and 69% for definitions 1, 2, 3, and 4, respectively. CONCLUSION The presence of an mp-MRI lesion in men with low-risk prostate cancer on TRUS biopsy confers, in most patients, a high likelihood that higher-risk disease will be present (either Gleason pattern 4 or a significant cancer burden). Conversely, if a lesion is not seen on mp-MRI, the attribution of low-risk grade or cancer burden is much more likely to be correct. Mp-MRI might therefore be used to triage men for resampling biopsies before entering active surveillance.

Multiparametric MRI for detection of radiorecurrent prostate cancer: added value of apparent diffusion coefficient maps and dynamic contrast-enhanced images.

PURPOSE One limitation of positron emission tomography (PET) imaging of the torso is patient motion. Motion-compensated image reconstruction (MCIR) is one method employed to reduce the deleterious effects of motion. Existing MCIR algorithms use a single sensitivity correction term, which provides inexact normalization for multigate data. Consequently, in this study, the authors derive and examine the performance of an MCIR algorithm with sensitivity correction per gate. In addition, they demonstrate an approximate tube-of-response (TOR) backprojector. METHODS Simulated data from the NCAT phantom with six lesions added were used to compare MCIR algorithms with and without the incorporation of sensitivity correction per gate and TOR backprojection to postreconstruction registration (PRR) and images reconstructed without motion correction. To make the simulations more realistic, intragate motion was included. Deformation fields were determined from NCAT anatomical images using a free-form deformation approach with bending energy regularization. RESULTS Sensitivity correction per gate and TOR backprojection improved mean lesion contrast-to-noise ratio by 6%-8%, with the maximum increase (21%-23%) found for the smallest lesion. These increases were obtained despite a small increase (3%) in noise as measured by standard deviation in a uniform lung region. Sensitivity correction per gate comes at no extra computational cost, whilst replacing line-of-response backprojection with TOR backprojection increased the overall computation time by ∼20%. In addition, MCIR was found to be superior to PRR, with one factor contributing to this difference being the differential impact of interpolation following deformation. MCIR was also shown to exhibit super-resolution. CONCLUSIONS Replacing a single sensitivity correction term in MCIR with sensitivity correction per gate improves lesion detectability. For a small increase in computational expense, further improvements are achieved using an approximate TOR backprojector rather than line-of-response backprojection.

Can multiparametric magnetic resonance imaging predict upgrading of transrectal ultrasound biopsy results at more definitive histology

Background:To determine the evolution of prostatic multi-parametric magnetic resonance imaging (mp-MRI) signal following transrectal ultrasound (TRUS)-guided biopsy.Methods:Local ethical permission and informed written consent was obtained from all the participants (n=14, aged 43–69, mean 64 years). Patients with a clinical suspicion of prostate cancer (PSA range 2.2–11.7, mean 6.2) and a negative (PIRAD 1–2/5) pre-biopsy mp-MRI (pre-contrast T1, T2, diffusion-weighted and dynamic-contrast-enhanced MRI) who underwent 10-core TRUS-guided biopsy were recruited for additional mp-MRI examinations performed at 1, 2 and 6 months post biopsy. We quantified mp-MRI peripheral zone (PZ) and transition zone (TZ) normalized T2 signal intensity (nT2-SI); T1 relaxation time (T10); diffusion-weighted MRI, apparent diffusion coefficient (ADC); dynamic contrast-enhanced MRI, maximum enhancement (ME); slope of enhancement (SoE) and area-under-the-contrast-enhancement-curve at 120 s (AUC120). Significant changes in mp-MRI parameters were identified by analysis of variance with Dunnett’s post testing.Results:Diffuse signal changes were observed post-biopsy throughout the PZ. No significant signal change occurred following biopsy within the TZ. Left and right PZ mean nT2-SI (left PZ: 5.73, 5.16, 4.90 and 5.12; right PZ: 5.80, 5.10, 4.84 and 5.05 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) and mean T10 (left PZ: 1.02, 0.67, 0.78, 0.85; right PZ: 1.29, 0.64, 0.78, 0.87 at pre-biopsy, 1, 2 and 6 months post biopsy, respectively) were reduced significantly (P<0.05) from pre-biopsy values for up to 6 months post biopsy. Significant changes (P<0.05) of PZ-ME and AUC120 were observed at 1 month but resolved by 2 months post biopsy. PZ ADC did not change significantly following biopsy (P=0.23–1.0). There was no significant change of any TZ mp-MRI parameter at any time point following biopsy (P=0.1–1.0).Conclusions:Significant PZ (but not TZ) T2 signal changes persist up to 6 months post biopsy, whereas PZ and TZ ADC is not significantly altered as early as 1 month post biopsy. Caution must be exercised when interpreting T1- and T2-weighted imaging early post biopsy, whereas ADC images are more likely to maintain clinical efficacy.

Improved motion-compensated image reconstruction for PET using sensitivity correction per respiratory gate and an approximate tube-of-response backprojector.

OBJECTIVE To investigate the effect of tumour necrosis factor (TNF)-α antagonists on MRI dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) parameters in Crohns disease (CD). METHODS 42 patients with CD (median age 24 years; 22 females) commencing anti-TNF-α therapy with baseline and follow-up (median 51 weeks) 1.5-T MR enterography (MRE) were retrospectively identified. MRE included DCE (n = 20) and/or multi-b-value DWI (n = 17). Slope of enhancement (SoE), maximum enhancement (ME), area under the time-intensity curve (AUC), Ktrans (transfer constant), ve (fractional volume of the extravascular-extracellular space), apparent diffusion coefficient (ADC) and ADCfast/slow were derived from the most inflamed bowel segments. A physician global assessment of disease activity (remission, mild, moderate and severe) at the time of MRE was assigned, and the cohort was divided into responders and non-responders. Data were compared using Mann-Whitney U test and analysis of variance. RESULTS Follow-up Ktrans, ME, SoE, AUC and ADCME changed significantly in clinical responders but not in non-responders, baseline {[median [interquartile range (IQR)]: 0.42 (0.38), 1.24 (0.52), 0.18 (0.17), 17.68 (4.70) and 1.56 mm(2) s(-1) (0.39 mm(2) s(-1)) vs follow-up [median (IQR): 0.15 (0.22), 0.50 (0.54), 0.07 (0.1), 14.73 (2.06) and 2.14 mm(2) s(-1) (0.62 mm(2) s(-1)), for responders, respectively, p = 0.006 to p = 0.037}. SoE was higher and ME and AUC lower for patients in remission than for those with severe activity [mean (standard deviation): 0.55 (0.46), 0.49 (0.28), 14.32 (1.32)] vs [0.32 (0.37), 2.21 (2.43) and 23.05 (13.66), respectively p = 0.017 to 0.033]. ADC was significantly higher for patients in remission [2.34 mm(2) s(-1) (0.67 mm(2) s(-1))] than for those with moderate [1.59 mm(2) s(-1) (0.26 mm(2) s(-1))] (p = 0.005) and severe disease [1.63 mm(2) s(-1) (0.21 mm(2) s(-1))] (p = 0.038). CONCLUSION DCE and DWI parameters change significantly in responders to TNF-α antagonists and are significantly different according to clinically defined disease activity status. ADVANCES IN KNOWLEDGE DCE and DWI parameters change significantly in responders to TNF-α antagonists in CD, suggesting an effect on bowel wall vascularity.