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We establish an open framework for developing plan optimization models for knowledge-based planning (KBP) in radiotherapy. Our framework includes reference plans for 100 patients with head-and-neck cancer and high-quality dose predictions from 19 KBP models that were developed by different research groups during the OpenKBP Grand Challenge. The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans. The predictions and plans were compared to the reference plans via: dose score, which is the average mean absolute voxel-by-voxel difference in dose a model achieved; the deviation in dose-volume histogram (DVH) criterion; and the frequency of clinical planning criteria satisfaction. We also performed a theoretical investigation to justify our dose mimicking models. The range in rank order correlation of the dose score between predictions and their KBP pipelines was 0.50 to 0.62, which indicates that the quality of the predictions is generally positively correlated with the quality of the plans. Additionally, compared to the input predictions, the KBP-generated plans performed significantly better (P, 19 pages, 7 tables, 6 figures

Background Adjuvant radiation therapy improves the overall survival and loco-regional control in patients with breast cancer. However, radiation-induced heart disease, which occurs after treatment from incidental radiation exposure to the cardiac organ, is an emerging challenge. This study aimed to generate synthetic contrast-enhanced computed tomography (SCECT) from non-contrast CT (NCT) using deep learning (DL) and investigate its role in contouring cardiac substructures. We also aimed to determine its applicability for a retrospective study on the substructure volume-dose relationship for predicting radiation-induced heart disease. Methods We prepared NCT-CECT cardiac scan pairs of 59 patients. Of these, 35, 4, and 20 pairs were used for training, validation, and testing, respectively. We adopted conditional generative adversarial network as a framework to generate SCECT. SCECT was validated in the following three stages: (1) The similarity between SCECT and CECT was evaluated; (2) Manual contouring was performed on SCECT and CECT with sufficient intervals and based on this, the geometric similarity of cardiac substructures was measured between them; (3) The treatment plan was quantitatively analyzed based on the contours of SCECT and CECT. Results While the mean values (± standard deviation) of the mean absolute error, peak signal-to-noise ratio, and structural similarity index measure between SCECT and CECT were 20.66 ± 5.29, 21.57 ± 1.85, and 0.77 ± 0.06, those were 23.95 ± 6.98, 20.67 ± 2.34, and 0.76 ± 0.07 between NCT and CECT, respectively. The Dice similarity coefficients and mean surface distance between the contours of SCECT and CECT were 0.81 ± 0.06 and 2.44 ± 0.72, respectively. The dosimetry analysis displayed error rates of 0.13 ± 0.27 Gy and 0.71 ± 1.34% for the mean heart dose and V5Gy, respectively. Conclusion Our findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.

PURPOSE The purpose of this study was to present real-time three-dimensional (3D) magnetic resonance imaging (MRI) in the presence of motion for MRI-guided radiotherapy (MRgRT) using dynamic keyhole imaging for high-temporal acquisition and super-resolution generative (SRG) model for high-spatial reconstruction. METHOD We propose a unique real-time 3D MRI technique by combining a data sharing technique (3D dynamic keyhole imaging) with a SRG model using cascaded deep learning technique. 3D dynamic keyhole imaging utilizes the data sharing mechanism by combining keyhole central k-space data acquired in real-time with high-spatial, high-temporal resolution prior peripheral k-space data at various motion positions prepared by the SRG model. The efficacy of the 3D dynamic keyhole imaging with super-resolution (SR_dKeyhole) was compared to the ground-truth super-resolution images with the original full k-space data. It was also compared with the zero-filling reconstruction (zero-filling), conventional keyhole reconstruction with low-spatial high-temporal prior data (LR_cKeyhole), and conventional keyhole reconstruction with super-resolution prior data (SR_cKeyhole). RESULTS High-spatial, high-temporal resolution 3D MRI datasets (1.5 × 1.5 × 6 mm3 ) were generated from low-spatial, high-temporal resolution 3D MRI datasets (6 × 6 × 6 mm3 ) using the cascaded deep learning SRG framework (

Purpose The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the popularization of MRI-guided radiation therapy (MR-IGRT), especially in recent years with the advent of first and second generation MRI-based therapy delivery systems for MR-IGRT. The expanding use of these systems is driving interest in MRI-only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient's MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI-to-CT image-to-image translation task and compare its performance to the well established U-Net architecture in a generative adversarial network (GAN) framework. Methods Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi-scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large-scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method. Results Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7 ± 4.3 HU, 0.9995 ± 0.0003, and 71.7 ± 2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2 mm criterion. Conclusions The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image-to-image translation task of sCT generation. Adopting the method is a first step toward an MRI-only RT workflow that enables widespread clinical applications for MR-IGRT including online adaptive therapy.

MR-guided radiotherapy (MRgRT) systems provide excellent soft tissue imaging immediately prior to and in real time during radiation delivery for cancer treatment. However, 2D cine MRI often has limited spatial resolution due to high temporal resolution. This work applies a super resolution machine learning framework to 3.5 mm pixel edge length, low resolution (LR), sagittal 2D cine MRI images acquired on a MRgRT system to generate 0.9 mm pixel edge length, super resolution (SR), images originally acquired at 4 frames per second (FPS). LR images were collected from 50 pancreatic cancer patients treated on a ViewRay MR-LINAC. SR images were evaluated using three methods. 1) The first method utilized intrinsic image quality metrics for evaluation. 2) The second used relative metrics including edge detection and structural similarity index (SSIM). 3) Finally, automatically generated tumor contours were created on both low resolution and super resolution images to evaluate target delineation and compared with DICE and SSIM. Intrinsic image quality metrics all had statistically significant improvements for SR images versus LR images, with mean (±1 SD) BRISQUE scores of 29.65 ± 2.98 and 42.48 ± 0.98 for SR and LR, respectively. SR images showed good agreement with LR images in SSIM evaluation, indicating there was not significant distortion of the images. Comparison of LR and SR images with paired high resolution (HR) 3D images showed that SR images had a mean (±1 SD) SSIM value of 0.633 ± 0.063 and LR a value of 0.587 ± 0.067 (p ≪ 0.05). Contours generated on SR images were also more robust to noise addition than those generated on LR images. This study shows that super resolution with a machine learning framework can generate high spatial resolution images from 4fps low spatial resolution cine MRI acquired on the ViewRay MR-LINAC while maintaining tumor contour quality and without significant acquisition or post processing delay.

PURPOSE Adjuvant radiotherapy (RT) has been performed to reduce locoregional failure (LRF) following radical cystectomy for locally advanced bladder cancer; however, its efficacy has not been well established. We analyzed the locoregional recurrence patterns of post-radical cystectomy to identify patients who could benefit from adjuvant RT and determine the optimal target volume. MATERIALS AND METHODS We retrospectively reviewed 160 patients with stage ≥ pT3 bladder cancer who were treated with radical cystectomy between January 2006 and December 2015. The impact of pathologic findings, including the stage, lympho-vascular invasion, perineural invasion, margin status, nodal involvement, and the number of nodes removed on failure patterns, was assessed. RESULTS Median follow-up period was 27.7 months. LRF was observed in 55 patients (34.3%), 12 of whom presented with synchronous local and regional failures as the first failure. The most common failure pattern was distant metastasis (40%). Among LRFs, the most common recurrence site was the cystectomy bed (15.6%). Patients with positive resection margins had a significantly higher recurrence rate compared to those without (28% vs. 10%, p=0.004). The pelvic nodal recurrence rate was < 5% in pN0 patients; the rate of recurrence in the external and common iliac nodes was 12.5% in pN+ patients. The rate of recurrence in the common iliac nodes was significantly higher in pN2-3 patients than in pN0-1 patients (15.2% vs. 4.4%, p=0.04). CONCLUSION Pelvic RT could be beneficial especially for those with positive resection margins or nodal involvement after radical cystectomy. Radiation fields should be optimized based on the patient-specific risk factors.

Additional file 1. Inter-observer variability and contouring time of three radiation oncologists for manual contours of organs-at-risk and target volumes.

Objective. Owing to the superior soft tissue contrast of MRI, MRI-guided adaptive radiotherapy (ART) is well-suited to managing interfractional changes in anatomy. An MRI-only workflow is desirable, but producing synthetic CT (sCT) data through paired data-driven deep learning (DL) for abdominal dose calculations remains a challenge due to the highly variable presence of intestinal gas. We present the preliminary dosimetric evaluation of our novel approach to sCT reconstruction that is well suited to handling intestinal gas in abdominal MRI-only ART.Approach. We utilize a paired data DL approach enabled by the intensity projection prior, in which well-matching training pairs are created by propagating air from MRI to corresponding CT scans. Evaluations focus on two classes: patients with (1) little involvement of intestinal gas, and (2) notable differences in intestinal gas presence between corresponding scans. Comparisons between sCT-based plans and CT-based clinical plans for both classes are made at the first treatment fraction to highlight the dosimetric impact of the variable presence of intestinal gas.Main results. Class 1 patients (n= 13) demonstrate differences in prescribed dose coverage of the PTV of 1.3 ± 2.1% between clinical plans and sCT-based plans. Mean DVH differences in all structures for Class 1 patients are found to be statistically insignificant. In Class 2 (n= 20), target coverage is 13.3 ± 11.0% higher in the clinical plans and mean DVH differences are found to be statistically significant.Significance. Significant deviations in calculated doses arising from the variable presence of intestinal gas in corresponding CT and MRI scans result in uncertainty in high-dose regions that may limit the effectiveness of adaptive dose escalation efforts. We have proposed a paired data-driven DL approach to sCT reconstruction for accurate dose calculations in abdominal ART enabled by the creation of a clinically unavailable training data set with well-matching representations of intestinal gas.

Objective. Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patients’ anatomy and motion during treatments and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or few x-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the surrounding normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real-time from a single onboard x-ray projection. Approach. Liver tumor tracking is achieved in two steps. First, a deep learning network is developed to predict the liver surface deformation using image features learned from the x-ray projection. Second, the intra-liver deformation is estimated through biomechanical modeling, using the liver surface deformation as the boundary condition to solve tumor motion by finite element analysis. The accuracy of the proposed framework was evaluated using a dataset of 10 patients with liver cancer. Main results. The results show accurate liver surface registration from the graph neural network-based deep learning model, which translates into accurate, fiducial-less liver tumor localization after biomechanical modeling (Significance. The method demonstrates its potentiality towards intra-treatment and real-time 3D liver tumor monitoring and localization. It could be applied to facilitate 4D dose accumulation, multi-leaf collimator tracking and real-time plan adaptation. The method can be adapted to other anatomical sites as well.

Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of deep learning-based auto-segmentation (DLBAS) in comparison to commercially available atlas-based segmentation solutions (ABAS) for breast cancer radiation therapy. This study used contrast-enhanced planning computed tomography scans from 62 patients with breast cancer who underwent breast-conservation surgery. Contours of target volumes (CTVs), organs, and heart substructures were generated using two commercial ABAS solutions and DLBAS using fully convolutional DenseNet. The accuracy of the segmentation was assessed using 14 test patients using the Dice Similarity Coefficient and Hausdorff Distance referencing the expert contours. A sensitivity analysis was performed using non-contrast planning CT from 14 additional patients. Compared to ABAS, the proposed DLBAS model yielded more consistent results and the highest average Dice Similarity Coefficient values and lowest Hausdorff Distances, especially for CTVs and the substructures of the heart. ABAS showed limited performance in soft-tissue-based regions, such as the esophagus, cardiac arteries, and smaller CTVs. The results of sensitivity analysis between contrast and non-contrast CT test sets showed little difference in the performance of DLBAS and conversely, a large discrepancy for ABAS. The proposed DLBAS algorithm was more consistent and robust in its performance than ABAS across the majority of structures when examining both CTVs and normal organs. DLBAS has great potential to aid a key process in the radiation therapy workflow, helping optimise and reduce the clinical workload.

PURPOSE/OBJECTIVE(S) Although radiation-induced cardiac toxicity is an important issue in breast radiation therapy (RT), the dose relationship between cardiac structures and its toxicity has not been fully elucidated, partially because many centers do not routinely administer intravenous (IV) contrast for breast RT, which preclude substructure delineation and detection of potential correlations. In this study, we attempted to generate the synthetic contrast-enhanced CT (CECTsyn) from the non-contrast CT (NCTreal) using deep convolutional neural network (DCNN) and to investigate whether CECTsyn can take a supportive role in case the studies for the cardiac toxicity induced by radiation is needed for the patient whose CECTreal cannot be obtained. MATERIALS/METHODS For this study, 22 NCTreal-CECTreal cardiac scan-pairs have been prepared of which the volume size was ∼512 × 512 × 400 and the resolution was ∼0.75 × 0.75 × 1 mm3 on average. Of the 22 datasets, 13/2/7 were used for training, validation, and testing, respectively. After matching the structure of paired scans using the deformable image registration (DIR), the area near the heart was cropped and used for training the deep learning model. We adopted the modified 2D fully convolution DenseNet (FC-DenseNet) as our backbone and trained it in the conditional generative adversarial network (cGAN) framework. The similarity between CECTsyn and CECTreal was evaluated first, and all NCTreal,CECTsyn, and CECTreal were applied to pre-trained cardiac auto-segmentation models to obtain the substructures of the heart, which is used for dose assessments in each of them. Finally, dose evaluations were conducted on CECTsyn and CECTreal with the dose distributions which are from clinical treatment plans and the manually modified contours of cardiac substructures. RESULTS Firstly, the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) between CECTsyn and CECTreal were 23.96 and 0.772, whereas those of NCTreal and CECTreal were 22.25and 0.748, respectively. Secondly, the results of applying the pre-trained cardiac auto-segmentation model were obtained, and the dice similarity coefficient (DSC) of CECTsyn was 0.762 on average which is superior to 0.267 of NCTreal and comparable to 0.843 of CECTreal. We could distinguish the L/R ventricles, L/R atriums, left anterior descending artery (LAD), and right coronary artery (RCA) clearly on CECTsyn. Finally, the dose differences between CECTsyn and CECTreal were on average 0.4/2.0 Gy in Dmean and Dmax, and 2.1/1.3/0.6/0.4/0.3/0.1/0.0% in V5 Gy, V10 Gy, V20 Gy, V30 Gy, V40 Gy, V50 Gy, and V60 Gy, respectively. CONCLUSION DCNN model proposed here showed the feasibility of CECTsyn generation from NCTreal and potential for cardiac substructure delineation such as ventricles, atriums, LAD, and RCA on NCTreal for breast RT. Furthermore, it is shown that CECTsyn can play a supportive role when the studies for the radiation-induced cardiac toxicity is needed.

PURPOSE/OBJECTIVE(S) Brain metastases (BM) are the most common intracranial tumors in adults. Benefiting from efficient local control, stereotactic ablative radiotherapy (SABR) is becoming popular in the treatment of BM. Detection and manual delineation of BM are labor-intense processes, and BM are occasionally mistaken for normal brain, especially if volumes are very small. Recently, several efforts have been made to develop automatic detection and segmentation for BM using advanced deep learning (DL) algorithms. In this study, we investigated the efficacy and accuracy of a DL model for the detection and segmentation of BM. MATERIALS/METHODS To train and validate the DL model, magnetic resonance imaging (MRI) for 65 patients and 603 BM were collected and analyzed. Manual contouring for BM was done by a radiation oncologist. We utilized 2.5-dimensional training using a two-dimensional U-Net as the DL architecture to avoid inefficiencies of the three-dimensional architecture. Because of MRI characteristic, we applied N4 bias field correction and gamma correction as pre-processing techniques. Also, we utilized the overlapping patch technique and under-sampling technique to extract small-volume BM from the normal brain that occupies most of the slice. During the training, data augmentations (horizontal flip, vertical flip, random rotation, random blur) were applied on the fly randomly. The detection performance was measured with sensitivity and average false positive rate and the segmentation performance with the Dice coefficient with dilation (DWD) with dilation factor set to a radius of 4 pixels. BMs with volumes 0.04 cc and 49.7% in BM with volumes < 0.04 cc. For all BM, the DWD value was 63%. CONCLUSION Our DL model can detect and segment BM with good performance; it is especially effective even for small-volume BM, suggesting its considerable benefit for gross tumor volume detection and segmentation for stereotactic radiosurgery or SABR.

Purpose/Objective(s) A large inter-physician variation can be seen when delineating organ-at-risks (OARs) for breast radiotherapy. This study aimed to externally validate the performance of deep learning-based auto-contouring system (ACS) for breast radiotherapy. Materials/Methods Eleven experts from two institutions were asked to delineate 9 OARs of 10 cases of breast radiotherapy. Then, auto-contours were provided to the experts for correction. To compare the performance of auto-, corrected-auto-, and experts’ manual contours, Dice similarity coefficient (DSC) and Hausdorff distance (HD) between each contour and the best manual contour were used, where higher DSC and lower HD means better geometric overlap. Results Total mean time for 9 OARs was 37 ± 20 min for manual and 6 ± 5 min for corrected-auto-contours. Among the DSC of experts’ manual contours and an auto-contour, DSC of an auto-contour ranked the second place and HD ranked the first place. Better performance was shown in corrected-auto-contours than in manual contours (median DSC: 0.90 vs. 0.88; median HD: 4.5 vs. 6.5 mm). The inter-physician variations among experts were reduced in corrected-auto-contours, compared with manual contours (DSC range: 0.86–0.90 vs. 0.89–0.90; HD range: 5.1–9.1 mm vs. 4.3–5.7 mm). Among manual OARs, breast contours had the largest variations, which were most significantly improved with an aid of ACS. Conclusion ACS showed at least similar performance in OARs compared with experts’ manual contouring, which anticipates further applications of ACS to target volumes. ACS can be a valuable tool for improving the quality of breast radiotherapy and reducing inter-physician variability in clinical practice.

PURPOSE Deep learning (DL)-based super-resolution (SR) reconstruction for magnetic resonance imaging (MRI) has recently been receiving attention due to the significant improvement in spatial resolution compared to conventional SR techniques. Challenges hindering the widespread implementation of these approaches remain, however. Low-resolution (LR) MRIs captured in the clinic exhibit complex tissue structures obfuscated by noise that are difficult for a simple DL framework to handle. Moreover, training a robust network for a SR task requires abundant, perfectly matched pairs of LR and high-resolution (HR) images that are often unavailable or difficult to collect. The purpose of this study is to develop a novel SR technique for MRI based on the concept of cascaded DL that allows for the reconstruction of high-quality SR images in the presence of insufficient training data, an unknown translation model, and noise. METHODS The proposed framework, based on the concept named cascaded deep learning, consists of three components: (a) a denoising autoencoder (DAE) trained using clinical LR noisy MRI scans that have been processed with a nonlocal means filter that generates denoised LR data; (b) a down-sampling network (DSN) trained with a small amount of paired LR/HR data from volunteers that allows for the generation of perfectly paired LR/HR data for the training of a generative model; and (c) the proposed SR generative model (p-SRG) trained with data generated by the DSN that maps from LR inputs to HR outputs. After training, LR clinical images may be fed through the DAE and p-SRG to yield SR reconstructions of the LR input. The application of this framework was explored in two settings: 3D breath-hold MRI axial SR reconstruction from LR axial scans (

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline–based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.

Diketopyrrolopyrrole (DPP)-based conjugated polymer PDTDPPQT was synthesized and was used to perform epitaxial polymer crystal growth on removable 1,3,5-trichlorobenzene crystallite templates. A thin-film transistor (TFT) was successfully fabricated in well-grown large spherulites of PDTDPPQT. The charge carrier mobility along the radial direction of the spherulites was measured to be 5.46-12.04 cm(2) V(-1) s(-1), which is significantly higher than that in the direction perpendicular to the radial direction. The dynamic response of charge transport was also investigated by applying a pulsed bias to TFTs loaded with a resistor (∼20 MΩ). The charge-transport behaviors along the radial direction and perpendicular to the radial direction were investigated by static and dynamic experiments through a resistor-loaded (RL) inverter. The RL inverter made of PDTDPPQT-based TFT operates well, maintaining a fairly high switching voltage ratio (Vout(ON)/Vout(OFF)) at a relatively high frequency when the source-drain electrodes are aligned parallel to the radial direction.

Purpose In the past decade, demands for organ specific (target oriented) single photon emission computed tomography (SPECT) is increasing, and several groups have conducted studies on developing clinical dedicated SPECT with pinhole collimator in order to improve the spatial resolution. However acceptance angle of the collimator cannot be adjusted to fit the different ROIs of target objects because the shape of pinhole could not be changed, and the magnifying factor cannot be maximized as the collimator-to-detector distance are fixed. Furthermore, those dedicated pinhole-SPECTs are typically made for a single purpose, and therefore possess a drawback in that it cannot be utilized for any other purpose. In this study we propose a novel SPECT system using variable pinhole collimator (VP SPECT) whose parameters are flexible. Methods The proposed variable pinhole collimator is modeled on conventional pinhole by piling several tungsten layers of different apertures. Depending on the combination of the holes in each layer, a variety of hole diameters and acceptance angles of the pinhole can be made. In addition, VP SPECT system allows attaching the collimator to the object as close as possible to maximize the sensitivity, and adjust the distance of the pinhole from the scintillation detector to optimize the system resolution for each rotation angle, automatically. For quantitative measurement, we compared the sensitivity and spatial resolution of VP SPECT with those of conventional pinhole-SPECT. To determine the possibility of the clinical and preclinical use of proposed system, a digital mouse whole body (MOBY) phantom is used for simulating the live mouse model. Results The result of simulation using ultra-micro hot spot phantom shows that the sensitivity of the proposed VP SPECT system is about 297% of that of the conventional system. While hot rods of diameter 0.6 mm can be distinguished in the image with the proposed VP SPECT system, 1.2 mm hot rods are barely discernible in the conventional pinhole-SPECT image. According to the result of MOBY phantom simulation, heart walls separated by 3 mm were not distinguished in conventional pinhole-SPECT images, but were clearly discerned in VP SPECT images. Conclusions In this study, we designed a novel pinhole collimator for SPECT and presented preliminary results of target oriented imaging with a simulation study. Currently, we are pursuing strategies to realize the proposed system, with the goal to apply the technology into a high sensitivity and high resolution preclinical SPECT. Should VP SPECT be applied to the clinical setting, we anticipate a high-sensitivity, high-resolution system for applications such as heart dedicated SPECT or related fields. This article is protected by copyright. All rights reserved.