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Abstract In cochlear implant surgery, insertion of perimodiolar electrode arrays into the scala tympani can be complicated by trauma or even accidental translocation of the electrode array within the cochlea. In patients with partial hearing loss, cochlear trauma can not only negatively affect implant performance, but also reduce residual hearing function. These events have been related to suboptimal positioning of the cochlear implant electrode array with respect to critical cochlear walls of the scala tympani (modiolar wall, osseous spiral lamina and basilar membrane). Currently, the position of the electrode array in relation to these walls cannot be assessed during the insertion and the surgeon depends on tactile feedback, which is unreliable and often comes too late. This study presents an image-guided cochlear implant device with an integrated, fiber-optic imaging probe that provides real-time feedback using optical coherence tomography during insertion into the human cochlea. This novel device enables the surgeon to accurately detect and identify the cochlear walls ahead and to adjust the insertion trajectory, avoiding collision and trauma. The functionality of this prototype has been demonstrated in a series of insertion experiments, conducted by experienced cochlear implant surgeons on fresh-frozen human cadaveric cochleae.

Abstract Purpose Selective internal radiation therapy (SIRT) requires a good liver registration of multi-modality images to obtain precise dose prediction and measurement. This study investigated the feasibility of liver registration of CT and MR images, guided by segmentation of the liver and its landmarks. The influence of the resulting lesion registration on dose estimation was evaluated. Methods The liver segmentation was done with a convolutional neural network (CNN), and the landmarks were segmented manually. Our image-based registration software and its liver-segmentation-guided extension (CNN-guided) were tuned and evaluated with 49 CT and 26 MR images from 20 SIRT patients. Each liver registration was evaluated by the root mean square distance (RMSD) of mean surface distance between manually delineated liver contours and mass center distance between manually delineated landmarks (lesions, clips, etc.). The root mean square of RMSDs (RRMSD) was used to evaluate all liver registrations. The CNN-guided registration was further extended by incorporating landmark segmentations (CNN&LM-guided) to assess the value of additional landmark guidance. To evaluate the influence of segmentation-guided registration on dose estimation, mean dose and volume percentages receiving at least 70 Gy (V70) estimated on the 99mTc-labeled macro-aggregated albumin (99mTc-MAA) SPECT were computed, either based on lesions from the reference 99mTc-MAA CT (reference lesions) or from the registered floating CT or MR images (registered lesions) using the CNN- or CNN&LM-guided algorithms. Results The RRMSD decreased for the floating CTs and MRs by 1.0 mm (11%) and 3.4 mm (34%) using CNN guidance for the image-based registration and by 2.1 mm (26%) and 1.4 mm (21%) using landmark guidance for the CNN-guided registration. The quartiles for the relative mean dose difference (the V70 difference) between the reference and registered lesions and their correlations [25th, 75th; r] are as follows: [− 5.5% (− 1.3%), 5.6% (3.4%); 0.97 (0.95)] and [− 12.3% (− 2.1%), 14.8% (2.9%); 0.96 (0.97)] for the CNN&LM- and CNN-guided CT to CT registrations, [− 7.7% (− 6.6%), 7.0% (3.1%); 0.97 (0.90)] and [− 15.1% (− 11.3%), 2.4% (2.5%); 0.91 (0.78)] for the CNN&LM- and CNN-guided MR to CT registrations. Conclusion Guidance by CNN liver segmentations and landmarks markedly improves the performance of the image-based registration. The small mean dose change between the reference and registered lesions demonstrates the feasibility of applying the CNN&LM- or CNN-guided registration to volume-level dose prediction. The CNN&LM- and CNN-guided registrations for CTs can be applied to voxel-level dose prediction according to their small V70 change for most lesions. The CNN-guided MR to CT registration still needs to incorporate landmark guidance for smaller change of voxel-level dose estimation.

Abstract Background Selective internal radiation therapy (SIRT) is a promising treatment for unresectable hepatic malignancies. Predictive dose calculation based on a simulation using 99m Tc-labeled macro-aggregated albumin (99m Tc-MAA) before the treatment is considered as a potential tool for patient-specific treatment planning. Post-treatment dose measurement is mainly performed to confirm the planned absorbed dose to the tumor and non-tumor liver volumes. This study compared the predicted and measured absorbed dose distributions. Methods Thirty-one patients (67 tumors) treated by SIRT with resin microspheres were analyzed. Predicted and delivered absorbed dose was calculated using 99m Tc-MAA-SPECT and 90Y-TOF-PET imaging. The voxel-level dose distribution was derived using the local deposition model. Liver perfusion territories and tumors have been delineated on contrast-enhanced CBCT images, which have been acquired during the 99m Tc-MAA work-up. Several dose-volume histogram (DVH) parameters together with the mean dose for liver perfusion territories and non-tumoral and tumoral compartments were evaluated. Results A strong correlation between the predicted and measured mean dose for non-tumoral volume was observed (r = 0.937). The ratio of measured and predicted mean dose to this volume has a first, second, and third interquartile range of 0.83, 1.05, and 1.25. The difference between the measured and predicted mean dose did not exceed 11 Gy. The correlation between predicted and measured mean dose to the tumor was moderate (r = 0.623) with a mean difference of − 9.3 Gy. The ratio of measured and predicted tumor mean dose had a median of 1.01 with the first and third interquartile ranges of 0.58 and 1.59, respectively. Our results suggest that 99m Tc-MAA-based dosimetry could predict under or over dosing of the non-tumoral liver parenchyma for almost all cases. For more than two thirds of the tumors, a predictive absorbed dose correctly indicated either good tumor dose coverage or under-dosing of the tumor. Conclusion Our results highlight the predictive value of 99m Tc-MAA-based dose estimation to predict non-tumor liver irradiation, which can be applied to prescribe an optimized activity aiming at avoiding liver toxicity. Compared to non-tumoral tissue, a poorer agreement between predicted and measured absorbed dose is observed for tumors.

Abstract Background In order to better understand the concept of mechanical dyssynchrony, a promising hallmark of cardiac resynchronization therapy (CRT) response, we investigated its effect on regional myocardial metabolism and myocardial blood flow (MBF) in non-ischemic CRT candidates. Results Thirty consecutive non-ischemic CRT eligible patients underwent static 18F-FDG and resting dynamic 13N-NH3 PET/CT. 18F-FDG uptake and MBF for septal and lateral wall were analysed and septal-to-lateral wall ratios (SLR) were calculated. Based on the presence of mechanical dyssynchrony (septal flash and/or apical rocking) on echocardiography, patients were divided into 2 groups, with (n = 23) and without (n = 7) mechanical dyssynchrony. Patients with mechanical dyssynchrony had significantly lower 18F-FDG SUVmean in the septum compared with the lateral wall (5.58 ± 2.65 vs 11.19 ± 4.10, p

Abstract Purpose We have developed a multi-modal imaging approach for SIRT, combining 99mTc-MAA SPECT/CT and/or 90Y PET, 18F-FDG PET/CT, and contrast-enhanced CBCT for voxel-based dosimetry, as a tool for treatment planning and verification. For radiation dose prediction calculations, a segmentation of the total liver volume and of the liver perfusion territories is required. Method In this paper, we proposed a procedure for multi-modal image analysis to assist SIRT treatment planning. The pre-treatment 18F-FDG PET/CT, 99mTc-MAA SPECT/CT, and contrast-enhanced CBCT images were registered to a common space using an initial rigid, followed by a deformable registration. The registration was scored by an expert using Likert scores. The total liver was segmented semi-automatically based on the PET/CT and SPECT/CT images, and the liver perfusion territories were determined based on the CBCT images. The segmentations of the liver and liver lobes were compared to the manual segmentations by an expert on a CT image. Result Our methodology showed that multi-modal image analysis can be used for determination of the liver and perfusion territories using CBCT in SIRT using all pre-treatment studies. The results for image registration showed acceptable alignment with limited impact on dosimetry. The image registration performs well according to the expert reviewer (scored as perfect or with little misalignment in 94% of the cases). The semi-automatic liver segmentation agreed well with manual liver segmentation (dice coefficient of 0.92 and an average Hausdorff distance of 3.04 mm). The results showed disagreement between lobe segmentation using CBCT images compared to lobe segmentation based on CT images (average Hausdorff distance of 14.18 mm), with a high impact on the dosimetry (differences up to 9 Gy for right and 21 Gy for the left liver lobe). Conclusion This methodology can be used for pre-treatment dosimetry and for SIRT planning including the determination of the activity that should be administered to achieve the therapeutical goal. The inclusion of perfusion CBCT enables perfusion-based definition of the liver lobes, which was shown to be markedly different from the anatomical definition in some of the patients.

In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET reconstructions are not yet available for use in routine clinical because of several reasons. In light of this, we investigate whether the improvements of anatomically-guided PET reconstruction methods can be achieved entirely in the image domain with a convolutional neural network (CNN). An entirely image-based CNN post-reconstruction approach has the advantage that no access to PET raw data is needed and, moreover, that the prediction times of trained CNNs are extremely fast on state of the art GPUs which will substantially facilitate the evaluation, fine-tuning and application of anatomically-guided PET reconstruction in real-world clinical settings.In this work, we demonstrate that anatomically-guided PET reconstruction using the asymmetric Bowsher prior can be well-approximated by a purely shift-invariant convolutional neural network in image space allowing the generation of anatomically-guided PET images in almost real-time. We show that by applying dedicated data augmentation techniques in the training phase, in which 16 [18F]FDG and 10 [18F]PE2I data sets were used, lead to a CNN that is robust against the used PET tracer, the noise level of the input PET images and the input MRI contrast. A detailed analysis of our CNN in 36 [18F]FDG, 18 [18F]PE2I, and 7 [18F]FET test data sets demonstrates that the image quality of our trained CNN is very close to the one of the target reconstructions in terms of regional mean recovery and regional structural similarity.

Accurate MR-based attenuation correction (MRAC) is essential for quantitative PET/MR imaging of the brain. In this study, we analyze the regional bias caused by MRAC based on Zero-Echo-Time MR images (ZTEAC) compared to CT-based AC (CTAC) static and dynamic PET imaging. In addition, the results are compared to the performance of the Atlas-based AC (AtlasAC) implemented in the GE SIGNA PET/MR software version MP24.Methods: Thirty static [18F]FDG and eleven dynamic [18F]PE2I acquisitions from a SIGNA PET/MR were reconstructed using ZTEAC (using a research tool, GE Healthcare), single-subject AtlasAC (the default AC in SIGNA PET/MR software version MP24) and CTAC (from a PET/CT acquisition of the same day). In the 30 static [18F]FDG reconstructions, the bias caused by ZTEAC and AtlasAC in the mean uptake of 85 anatomical volumes of interest (VOIs) of the Hammers' atlas was analyzed in PMOD. For the 11 dynamic [18F]PE2I reconstructions, the bias caused by ZTEAC and AtlasAC in the non displaceable binding potential BPnd in the striatum was calculated with cerebellum as the reference region and a simplified reference tissue model.Results: The regional bias caused by ZTEAC in the static [18F]FDG reconstructions ranged from −8.0 to +7.7% (mean 0.1%, SD 2.0%). For AtlasAC this bias ranged from −31.6 to +16.6% (mean −0.4%, SD 4.3%). The bias caused by AtlasAC showed a clear gradient in the cranio-caudal direction (−4.2% in the cerebellum, +6.6% in the left superior frontal gyrus). The bias in the striatal BPnd for the [18F]PE2I reconstructions ranged from −0.8 to +4.8% (mean 1.5%, SD 1.4%) using ZTEAC and from −0.6 to +9.4% using AtlasAC (mean 4.2%, SD 2.6%).Conclusion: ZTEAC provides excellent quantitative accuracy for static and dynamic brain PET/MR, comparable to CTAC, and is clearly superior to the default AtlasAC implemented in the SIGNA PET/MR software version MP24.

Quality control (QC) of medical imaging devices is essential to ensure their proper function and to gain accurate and quantitative results. Therefore, several international bodies have published QC guidelines and recommendations for a wide range of imaging modalities to ensure adequate performance of the systems. Hybrid imaging systems such as positron emission tomography/computed tomography (PET/CT) or PET/magnetic resonance imaging (PET/MRI), in particular, present additional challenges caused by differences between the combined modalities. However, despite the increasing use of this hybrid imaging modality in recent years, there are no dedicated QC recommendations for PET/MRI. Therefore, this work aims at collecting information on QC procedures across a European PET/MRI network, presenting quality assurance procedures implemented by PET/MRI vendors and achieving a consensus on PET/MRI QC procedures across imaging centers. Users of PET/MRI systems at partner sites involved in the HYBRID consortium were surveyed about local frequencies of QC procedures for PET/MRI. Although all sites indicated that they perform vendor-specific daily QC procedures, significant variations across the centers were observed for other QC tests and testing frequencies. Likewise, variations in available recommendations and guidelines and the QC procedures implemented by vendors were found. Based on the available information and our clinical expertise within this consortium, we were able to propose a minimum set of PET/MRI QC recommendations including the daily QC, cross-calibration tests, and an image quality (IQ) assessment for PET and coil checks and MR image quality tests for MRI. Together with regular checks of the PET–MRI alignment, proper PET/MRI performance can be ensured.

In this work, we propose and analyze a new concept of gamma ray imaging that corresponds to a gamma camera with a mobile collimator, which can be used in vivo, during surgical interventions for oncological patients for localizing regions of interest such as tumors or ganglia. The benefits are a much higher sensitivity, better image quality and, consequently, a dose reduction for the patient and medical staff. This novel approach is a practical solution to the overlapping problem which is inherent to multi-pinhole gamma camera imaging and single photon emission computed tomography and which translates into artifacts and/or image truncation in the final reconstructed image. The key concept consists in introducing a relative motion between the collimator and the detector. Moreover, this design could also be incorporated into most commercially available gamma camera devices, without any excessive additional requirements. We use Monte Carlo simulations to assess the feasibility of such a device, analyze three possible designs and compare their sensitivity, resolution and uniformity. We propose a final design of a gamma camera with a high sensitivity ranging from 0.001 to 0.006 cps/Bq, and a high resolution of 0.5–1.0 cm (FWHM), for source-to-detector distances of 4–10 cm. Additionally, this planar gamma camera provides information about the depth of source (with approximate resolution of 1.5 cm) and excellent image uniformity.

There are drawbacks with using a Positron Emission Tomography (PET) scanner design employing the traditional arrangement of multiple detectors in an array format. Typically PET systems are constructed with many regular gaps between the detector modules in a ring or box configuration, with additional axial gaps between the rings. Although this has been significantly reduced with the use of the compact high granularity SiPM photodetector technology, such a scanner design leads to a decrease in the number of annihilation photons that are detected causing lower scanner sensitivity. Moreover, the ability to precisely determine the line of response (LOR) along which the positron annihilated is diminished closer to the detector edges because the spatial resolution there is degraded due to edge effects. This happens for both monolithic based designs, caused by the truncation of the scintillation light distribution, but also for detector blocks that use crystal arrays with a number of elements that are larger than the number of photosensors and, therefore, make use of the light sharing principle. In this report we present a design for a small-animal PET scanner based on a single monolithic annulus-like scintillator that can be used as a PET insert in high-field Magnetic Resonance systems. We provide real data showing the performance improvement when edge-less modules are used. We also describe the specific proposed design for a rodent scanner that employs facetted outside faces in a single LYSO tube. In a further step, in order to support and prove the proposed edgeless geometry, simulations of that scanner have been performed and lately reconstructed showing the advantages of the design.