Detection Of Small Lesions Research Articles

GravityNet for end-to-end small lesion detection

This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be “attracted” by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.

Current progress and future perspectives in total‐body positron emission tomography/computed tomography. Part II: Clinical applications

AbstractTotal‐body positron emission tomography (TB‐PET) has significantly advanced from initial conception to global commercial availability. The high sensitivity of TB‐PET has led to superior lesion detection, thereby expanding the range of clinical applications. TB‐PET technology offers several advantages: (a) It enables the detection of small lesions, facilitating precise cancer staging and targeted cancer formulation. (b) The technology shortens the acquisition time while maintaining the quality of diagnostic images. (c) TB‐PET allows for a reduction in the amount of administered radiotracer, which minimizes image noise, reduces the effective radiation dose to patients, and enhances staff safety. (d) The scanner supports the development of new tracers and the dynamic imaging of these tracers throughout the entire body. (e) TB‐PET accommodates delayed scanning, which has been shown to improve the detection of small and previously undetected malignant lesions by enhancing the clearance in areas of significant background activity. (f) Owing to its high‐quality images, TB‐PET is suitable for parametric imaging, which offers several advantages over conventional standardized uptake value imaging. However, TB‐PET still faces several challenges. There is a lack of consensus on the optimal dose and scan duration for clinical diagnosis using TB‐PET. Additionally, unified standards for parametric imaging via TB‐PET are yet to be established, and the full clinical significance of this technology remains under‐explored. The accompanying review (Part 1) covers TB‐PET data manipulation and analysis.

Phantom study and clinical application of total-body 18F-FDG PET/CT imaging: How to use small voxel imaging better?

BackgroundConventional PET/CT imaging reconstruction is typically performed using voxel size of 3.0–4.0 mm in three axes. It is hypothesized that a smaller voxel sizes could improve the accuracy of small lesion detection. This study aims to explore the advantages and conditions of small voxel imaging on clinical application.MethodsBoth NEMA IQ phantom and 30 patients with an injected dose of 3.7 MBq/kg were scanned using a total-body PET/CT (uEXPLORER). Images were reconstructed using matrices of 192 × 192, 512 × 512, and 1024 × 1024 with scanning duration of 3 min, 5 min, 8 min, and 10 min, respectively.ResultsIn the phantom study, the contrast recovery coefficient reached the maximum in matrix group of 512 × 512, and background variability increased as voxel size decreased. In the clinical study, SUVmax, SD, and TLR increased, while SNR decreased as the voxel size decreased. When the scanning duration increased, SNR increased, while SUVmax, SD, and TLR decreased. The SUVmean was more reluctant to the changes in imaging matrix and scanning duration. The mean subjective scores for all 512 × 512 groups and 1024 × 1024 groups (scanning duration ≥ 8 min) were over three points. One false-positive lesion was found in groups of 512 × 512 with scanning duration of 3 min, 1024 × 1024 with 3 min and 5 min, respectively. Meanwhile, the false-negative lesions found in group of 192 × 192 with duration of 3 min and 5 min, 512 × 512 with 3 min and 1024 × 1024 with 3 min and 5 min were 5, 4, 1, 4, and 1, respectively. The reconstruction time and storage space occupation were significantly increased as the imaging matrix increased.ConclusionsPET/CT imaging with smaller voxel can improve SUVmax and TLR of lesions, which is advantageous for the diagnosis of small or hypometabolic lesions if with sufficient counts. With an 18F-FDG injection dose of 3.7 MBq/kg, uEXPLORER PET/CT imaging using matrix of 512 × 512 with 5 min or 1024 × 1024 with 8 min can meet the image requirements for clinical use.

Grating-based x-ray dark-field CT for lung cancer diagnosis in mice

BackgroundThe low absorption of x-rays in lung tissue and the poor resolution of conventional computed tomography (CT) limits its use to detect lung disease. However, x-ray dark-field imaging can sense the scattered x-rays deflected by the structures being imaged. This technique can facilitate the detection of small alveolar lesions that would be difficult to detect with conventional CT. Therefore, it may provide an alternative imaging modality to diagnose lung disease at an early stage.MethodsEight mice were inoculated with lung cancers simultaneously. Each time two mice were scanned using a grating-based dark-field CT on days 4, 8, 12, and 16 after the introduction of the cancer cells. The detectability index was calculated between nodules and healthy parenchyma for both attenuation and dark-field modalities. High-resolution micro-CT and pathological examinations were used to crosscheck and validate our results. Paired t-test was used for comparing the ability of dark-field and attenuation modalities in pulmonary nodule detection.ResultsThe nodules were shown as a signal decrease in the dark-field modality and a signal increase in the attenuation modality. The number of nodules increased from day 8 to day 16, indicating disease progression. The detectability indices of dark-field modality were higher than those of attenuation modality (p = 0.025).ConclusionsCompared with the standard attenuation CT, the dark-field CT improved the detection of lung nodules.Relevance statementDark-field CT has a higher detectability index than conventional attenuation CT in lung nodule detection. This technique could improve the early diagnosis of lung cancer.Key points• Lung cancer progression was observed using x-ray dark-field CT.• Dark-field modality complements with attenuation modality in lung nodule detection.• Dark-field modality showed a detectability index higher than that attenuation in nodule detection.Graphical

MSPA-DLA++: A Multi-Scale Phase Attention Deep Layer Aggregation for Lesion Detection in Multi-Phase CT Images.

Object detection using convolutional neural networks (CNNs) has achieved high performance and achieved state-of-the-art results with natural images. Compared to natural images, medical images present several challenges for lesion detection. First, the sizes of lesions vary tremendously, from several millimeters to several centimeters. Scale variations significantly affect lesion detection accuracy, especially for the detection of small lesions. Moreover, the effective extraction of temporal and spatial features from multi-phase CT images is also an important issue. In this paper, we propose a group-based deep layer aggregation method with multiphase attention for liver lesion detection in multi-phase CT images. The method, which is called MSPA-DLA++, is a backbone feature extraction network for anchor-free liver lesion detection in multi-phase CT images that addresses scale variations and extracts hidden features from such images. The effectiveness of the proposed method is demonstrated on public datasets (LiTS2017) and our private multiphase dataset. The results of the experiments show that MSPA-DLA++ can improve upon the performance of state-of-the-art networks by approximately 3.7%.

Detection of Small Lesions on Grape Leaves Based on Improved YOLOv7

The precise detection of small lesions on grape leaves is beneficial for early detection of diseases. In response to the high missed detection rate of small target diseases on grape leaves, this paper adds a new prediction branch and combines an improved channel attention mechanism and an improved E-ELAN (Extended-Efficient Long-range Attention Network) to propose an improved algorithm for the YOLOv7 (You Only Look Once version 7) model. Firstly, to address the issue of low resolution for small targets, a new detection head is added to detect smaller targets. Secondly, in order to increase the feature extraction ability of E-ELAN components in YOLOv7 for small targets, the asymmetric convolution is introduced into E-ELAN to replace the original 3 × 3 convolution in E-ELAN network to achieve multi-scale feature extraction. Then, to address the issue of insufficient extraction of information from small targets in YOLOv7, a channel attention mechanism was introduced and improved to enhance the network’s sensitivity to small-scale targets. Finally, the CIoU (Complete Intersection over Union) in the original YOLOv7 network model was replaced with SIoU (Structured Intersection over Union) to optimize the loss function and enhance the network’s localization ability. In order to verify the effectiveness of the improved YOLOv7 algorithm, three common grape leaf diseases were selected as detection objects to create a dataset for experiments. The results show that the average accuracy of the algorithm proposed in this paper is 2.7% higher than the original YOLOv7 algorithm, reaching 93.5%.

Automatic detection of small bowel lesions with different bleeding risks based on deep learning models.

Deep learning provides an efficient automatic image recognition method for small bowel (SB) capsule endoscopy (CE) that can assist physicians in diagnosis. However, the existing deep learning models present some unresolved challenges. To propose a novel and effective classification and detection model to automatically identify various SB lesions and their bleeding risks, and label the lesions accurately so as to enhance the diagnostic efficiency of physicians and the ability to identify high-risk bleeding groups. The proposed model represents a two-stage method that combined image classification with object detection. First, we utilized the improved ResNet-50 classification model to classify endoscopic images into SB lesion images, normal SB mucosa images, and invalid images. Then, the improved YOLO-V5 detection model was utilized to detect the type of lesion and its risk of bleeding, and the location of the lesion was marked. We constructed training and testing sets and compared model-assisted reading with physician reading. The accuracy of the model constructed in this study reached 98.96%, which was higher than the accuracy of other systems using only a single module. The sensitivity, specificity, and accuracy of the model-assisted reading detection of all images were 99.17%, 99.92%, and 99.86%, which were significantly higher than those of the endoscopists' diagnoses. The image processing time of the model was 48 ms/image, and the image processing time of the physicians was 0.40 ± 0.24 s/image (P < 0.001). The deep learning model of image classification combined with object detection exhibits a satisfactory diagnostic effect on a variety of SB lesions and their bleeding risks in CE images, which enhances the diagnostic efficiency of physicians and improves the ability of physicians to identify high-risk bleeding groups.

Imaging of Multiple Myeloma: Present and Future.

Multiple myeloma (MM) is the second most common adult hematologic malignancy, and early intervention increases survival in asymptomatic high-risk patients. Imaging is crucial for the diagnosis and follow-up of MM, as the detection of bone and bone marrow lesions often dictates the decision to start treatment. Low-dose whole-body computed tomography (CT) is the modality of choice for the initial assessment, and dual-energy CT is a developing technique with the potential for detecting non-lytic marrow infiltration and evaluating the response to treatment. Magnetic resonance imaging (MRI) is more sensitive and specific than 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for the detection of small focal lesions and diffuse marrow infiltration. However, FDG-PET/CT is recommended as the modality of choice for follow-up. Recently, diffusion-weighted MRI has become a new technique for the quantitative assessment of disease burden and therapy response. Although not widespread, we address current proposals for structured reporting to promote standardization and diminish variations. This review provides an up-to-date overview of MM imaging, indications, advantages, limitations, and recommended reporting of each technique. We also cover the main differential diagnosis and pitfalls and discuss the ongoing controversies and future directions, such as PET-MRI and artificial intelligence.

A newly developed deep learning-based system for automatic detection and classification of small bowel lesions during double-balloon enteroscopy examination

BackgroundDouble-balloon enteroscopy (DBE) is a standard method for diagnosing and treating small bowel disease. However, DBE may yield false-negative results due to oversight or inexperience. We aim to develop a computer-aided diagnostic (CAD) system for the automatic detection and classification of small bowel abnormalities in DBE.Design and methodsA total of 5201 images were collected from Renmin Hospital of Wuhan University to construct a detection model for localizing lesions during DBE, and 3021 images were collected to construct a classification model for classifying lesions into four classes, protruding lesion, diverticulum, erosion & ulcer and angioectasia. The performance of the two models was evaluated using 1318 normal images and 915 abnormal images and 65 videos from independent patients and then compared with that of 8 endoscopists. The standard answer was the expert consensus.ResultsFor the image test set, the detection model achieved a sensitivity of 92% (843/915) and an area under the curve (AUC) of 0.947, and the classification model achieved an accuracy of 86%. For the video test set, the accuracy of the system was significantly better than that of the endoscopists (85% vs. 77 ± 6%, p < 0.01). For the video test set, the proposed system was superior to novices and comparable to experts.ConclusionsWe established a real-time CAD system for detecting and classifying small bowel lesions in DBE with favourable performance. ENDOANGEL-DBE has the potential to help endoscopists, especially novices, in clinical practice and may reduce the miss rate of small bowel lesions.

COMET: Cross-space Optimization-based Mutual learning network for super-resolution of CEST-MRI.

Chemical Exchange Saturation Transfer Magn-etic Resonance Imaging (CEST-MRI) is a promising approach for detecting tissue metabolic changes. However, due to the constraints of scan time and contrast-noise-ratio, CEST-MRI always exhibits low spatial resolution, hindering the clinical applications especially for detection of small lesions. Many super-resolution (SR) methods have shown good performance in medical images. However, when applied to CEST-MRI, these methods have two shortcomings that may limit their performance. Firstly, CEST-MRI has an additional frequency dimension, but the information along this dimension is not fully utilized. The second is that these SR methods mainly focus on improving the quality of the CEST-weighted images, while the accuracy of the quantitative maps is the most concerned aspect for CEST-MRI. To address these shortcomings, we propose a Cross-space Optimization-based Mutual learning nETwork (COMET) for SR of CEST-MRI. COMET incorporates novel spatio-frequency extraction modules and a mutual learning module to leverage and combine information from both spatial and frequency spaces, thereby enhancing the SR performance. Furthermore, we propose a novel CEST-based normalization loss to address the normalization-induced distribution problem and preserve the sharpness of quantitative maps, enabling more accurate CEST-MRI quantification. COMET is evaluated on an ischemia rat brain dataset and a human brain dataset. The results demonstrate COMET achieves 8-fold SR, providing accurate quantitative maps. Moreover, COMET outperforms all other state-of-the-art SR methods. Additionally, COMET exhibits its potential in prospective study.

Automatic segmentation of white matter hyperintensities in T2-FLAIR with AQUA: A comparative validation study against conventional methods

White matter hyperintensities (WMHs) are lesions in the white matter of the brain that are associated with cognitive decline and an increased risk of dementia. The manual segmentation of WMHs is highly time-consuming and prone to intra- and inter-variability. Therefore, automatic segmentation approaches are gaining attention as a more efficient and objective means to detect and monitor WMHs. In this study, we propose AQUA, a deep learning model designed for fully automatic segmentation of WMHs from T2-FLAIR scans, which improves upon our previous study for small lesion detection and incorporating a multicenter approach. AQUA implements a two-dimensional U-Net architecture and uses patch-based training. Additionally, the network was modified to include Bottleneck Attention Module on each convolutional block of both the encoder and decoder to enhance performance for small-sized WMH. We evaluated the performance and robustness of AQUA by comparing it with five well-known supervised and unsupervised methods for automatic segmentation of WMHs (LGA, LPA, SLS, UBO, and BIANCA). To accomplish this, we tested these six methods on the MICCAI 2017 WMH Segmentation Challenge dataset, which contains MRI images from 170 elderly participants with WMHs of presumed vascular origin, and assessed their robustness across multiple sites and scanner types. The results showed that AQUA achieved superior performance in terms of spatial (Dice = 0.72) and volumetric (logAVD = 0.10) agreement with the manual segmentation compared to the other methods. While the recall and F1-score were moderate at 0.49 and 0.59, respectively, they improved to 0.75 and 0.82 when excluding small lesions (≤ 6 voxels). Remarkably, despite being trained on a different dataset with different ethnic backgrounds, lesion loads, and scanners, AQUA's results were comparable to the top 10 ranked methods of the MICCAI challenge. The findings suggest that AQUA is effective and practical for automatic segmentation of WMHs from T2-FLAIR scans, which could help identify individuals at risk of cognitive decline and dementia and allow for early intervention and management.

Quantitative analysis evaluation of image reconstruction algorithms between digital and analog PET-CT

Positron emission tomography – computed tomography (PET-CT) is a non-invasive diagnostic tool that is widely used in oncology imaging. High quality diagnostic images and quantitative accuracy are often restricted by image noise, adequate spatial resolution and contrast ratio.Ordered Subset Expectation Maximisation (OSEM) is a widely used statistical iterative reconstruction algorithm in PET-CT due to its dependability, reconstruction quality and adequate signal-to-noise ratio. However, OSEM requires a large number of iterations to achieve high quantitative accuracy which results in increasing image noise. A novel algorithm, HYPER DPR (developed by United Imaging Healthcare) is an artificial intelligence-based reconstruction method that aims to provide increased sensitivity, higher spatial resolution and less noise.This study evaluates the accuracy and sensitivity of HYPER DPR against OSEM using reconstructed images from analog and digital PET-CT. Results demonstrate that both OSEM and HYPER DPR reconstruction algorithms in digital PET-CT has greater spatial resolution, increased detection sensitivity and less image noise when compared to analog PET-CT. Digital PET-CT and HYPER DPR enables better small lesion detection and increased resolution, thus resulting in better disease detection and improved patient management. Increased sensitivity of digital PET-CT results in low dose scans from reduced radiotracer injections, therefore having higher patient output.

Spatio-Temporal Positron Emission Tomography Reconstruction with Attenuation and Motion Correction.

The detection of cancer lesions of a comparable size to that of the typical system resolution of modern scanners is a long-standing problem in Positron Emission Tomography. In this paper, the effect of composing an image-registering convolutional neural network with the modeling of the static data acquisition (i.e., the forward model) is investigated. Two algorithms for Positron Emission Tomography reconstruction with motion and attenuation correction are proposed and their performance is evaluated in the detectability of small pulmonary lesions. The evaluation is performed on synthetic data with respect to chosen figures of merit, visual inspection, and an ideal observer. The commonly used figures of merit-Peak Signal-to-Noise Ratio, Recovery Coefficient, and Signal Difference-to-Noise Ration-give inconclusive responses, whereas visual inspection and the Channelised Hotelling Observer suggest that the proposed algorithms outperform current clinical practice.

CPSNet: a cyclic pyramid-based small lesion detection network

CPSNet: a cyclic pyramid-based small lesion detection network

Contrast-enhanced ultrasound demonstrates greater adjuvant potential: past, current status, and future applications in Hepatocellular carcinoma early diagnosis.

Hepatocellular carcinoma (HCC) has an atypical onset of clinical symptoms and a rapid tumor progression. The majority of patients with HCC are already in the late stages of the disease when they are diagnosed, limiting them to the best available treatments. Contrast-enhanced ultrasound (CEUS) has achieved significant advances in the diagnosis of HCC, including the detection of small lesions, the investigation of more beneficial contrast agents, and the use of CEUS-based radiomics. The purpose of this review is to discuss the relevant research and future challenges of CEUS in the early detection of HCC, to advise more accurate therapy.

Amplifying the Effects of Contrast Agents on Magnetic Resonance Images Using a Deep Learning Method Trained on Synthetic Data.

Artificial intelligence (AI) methods can be applied to enhance contrast in diagnostic images beyond that attainable with the standard doses of contrast agents (CAs) normally used in the clinic, thus potentially increasing diagnostic power and sensitivity. Deep learning-based AI relies on training data sets, which should be sufficiently large and diverse to effectively adjust network parameters, avoid biases, and enable generalization of the outcome. However, large sets of diagnostic images acquired at doses of CA outside the standard-of-care are not commonly available. Here, we propose a method to generate synthetic data sets to train an "AI agent" designed to amplify the effects of CAs in magnetic resonance (MR) images. The method was fine-tuned and validated in a preclinical study in a murine model of brain glioma, and extended to a large, retrospective clinical human data set. A physical model was applied to simulate different levels of MR contrast from a gadolinium-based CA. The simulated data were used to train a neural network that predicts image contrast at higher doses. A preclinical MR study at multiple CA doses in a rat model of glioma was performed to tune model parameters and to assess fidelity of the virtual contrast images against ground-truth MR and histological data. Two different scanners (3 T and 7 T, respectively) were used to assess the effects of field strength. The approach was then applied to a retrospective clinical study comprising 1990 examinations in patients affected by a variety of brain diseases, including glioma, multiple sclerosis, and metastatic cancer. Images were evaluated in terms of contrast-to-noise ratio and lesion-to-brain ratio, and qualitative scores. In the preclinical study, virtual double-dose images showed high degrees of similarity to experimental double-dose images for both peak signal-to-noise ratio and structural similarity index (29.49 dB and 0.914 dB at 7 T, respectively, and 31.32 dB and 0.942 dB at 3 T) and significant improvement over standard contrast dose (ie, 0.1 mmol Gd/kg) images at both field strengths. In the clinical study, contrast-to-noise ratio and lesion-to-brain ratio increased by an average 155% and 34% in virtual contrast images compared with standard-dose images. Blind scoring of AI-enhanced images by 2 neuroradiologists showed significantly better sensitivity to small brain lesions compared with standard-dose images (4.46/5 vs 3.51/5). Synthetic data generated by a physical model of contrast enhancement provided effective training for a deep learning model for contrast amplification. Contrast above that attainable at standard doses of gadolinium-based CA can be generated through this approach, with significant advantages in the detection of small low-enhancing brain lesions.

Tumor microenvironment and fibroblast activation protein inhibitor (FAPI) PET: developments toward brain imaging.

Fibroblast activation protein (FAP) is a type-II membrane bound glycoprotein specifically expressed by activated fibroblasts almost exclusively in pathological conditions including arthritis, fibrosis and cancer. FAP is overexpressed in cancer-associated fibroblasts (CAFs) located in tumor stroma, and is known to be involved in a variety of tumor-promoting activities such as angiogenesis, proliferation, resistance to chemotherapy, extracellular matrix remodeling and immunosuppression. In most cancer types, higher FAP expression is associated with worse clinical outcomes, leading to the hypothesis that FAP activity is involved in cancer development, cancer cell migration, and cancer spread. Recently, various high selectivity FAP inhibitors (FAPIs) have been developed and subsequently used for positron emission tomography (PET) imaging of different pathologies. Considering the paucity of widely available and especially mainstream reliable radioligands in brain cancer PET imaging, and the poor survival rates of patients with certain types of brain cancer such as glioblastoma, FAPI-PET represents a major development in enabling the detection of small primary or metastatic lesions in the brain due to its biological characteristics and low background accumulation. In this work, we aim to summarize the potential avenues for use of FAPI-PET, from the basic biological processes to oncologic imaging and with a main focus on brain imaging.

A virtual-pinhole PET device for improving contrast recovery and enhancing lesion detectability of a one-meter-long PET scanner: a simulation study

This paper presents a simulation study to demonstrate that the contrast recovery coefficients (CRC) and detectability of small lesions of a one-meter-long positron emission tomography (PET) scanner can be further enhanced by the integration of high resolution virtual-pinhole (VP) PET devices. The scanner under investigation is a Siemens Biograph Vision Quadra which has an axial field-of-view (FOV) of 106 cm. The VP-PET devices contain two high-resolution flat panel detectors, each composed of 2 × 8 detector modules each of which consists of 32 × 64 lutetium-oxyorthosilicate crystals (1.0 × 1.0 × 10.0 mm3 each). Two configurations for the VP-PET device placement were evaluated: (1) place the two flat-panel detectors at the center of the scanner’s axial FOV below the patient bed; (2) place one flat-panel detector at the center of the first and the last quarter of the scanner’s axial FOV below the patient bed. Sensitivity profiles were measured by moving a point 22Na source stepwise across the scanner’s FOV axially at different locations. To assess the improvement in CRC and lesion detectability by the VP-PET devices, an elliptical torso phantom (31.6 × 22.8 × 106 cm3) was first imaged by the native scanner then subsequently by the two VP-PET geometry configurations. Spherical lesions (4 mm in diameter) having 5:1 lesion-to-background radioactivity concentration ratio were grouped and placed at nine regions in the phantom to analyze the dependence of the improvement in plane. Average CRCs and their standard deviations of the 7 tumors in each group were computed and the receiver operating characteristic (ROC) curves were drawn to evaluate the improvement in lesion detectability by the VP-PET device over the native long axial PET scanner. The fraction of coincidence events between the inserts and the scanner detectors was 13%–16% (out of the total number of coincidences) for VP-PET configuration 1 and 2, respectively. The VP-PET systems provide higher CRCs for lesions in all regions in the torso, with more significant enhancement at regions closer to the inserts, than the native scanner does. For any given false positive fraction, the VP-PET systems offer higher true positive fraction compared to the native scanner. This work provides a potential solution to further enhance the image resolution of a long axial FOV PET scanner to maximize its lesion detectability afforded by its super high effective sensitivity.

Diagnosis and management of indeterminate testicular lesions.

The sophistication and accessibility of modern-day imaging result in frequent detection of small or equivocal lesions of the testes. Traditionally, diagnosis of a testicular lesion with any possibility of malignancy would usually prompt radical orchidectomy. However, awareness is growing that a substantial proportion of these lesions might be benign and that universal application of radical orchidectomy risks frequent overtreatment. Given the potentially profound effects of radical orchidectomy on fertility, endocrine function and psychosexual well-being, particularly in scenarios of an abnormal contralateral testis or bilateral lesions, organ-preserving strategies for equivocal lesions should be considered. Image-based active surveillance can be applied for indeterminate lesions measuring ≤15 mm with a low conversion rate to surgical treatment. However, these outcomes are early and from relatively small, selected cohorts, and concerns prevail regarding the metastatic potential of even small undiagnosed germ cell tumours. No consensus exists on optimal surveillance (short interval (<3 months) ultrasonography is generally adopted); histological sampling is a widespread alternative, involving inguinal delivery of the testis and excisional biopsy of the lesion, with preoperative marking or intraoperative ultrasonographic localization when necessary. Frozen section analysis in this context demonstrates excellent diagnostic accuracy. Histological results support thatapproximately two-thirds of marker-negative indeterminate solitary testicular lesions measuring ≤25 mm overall are benign. In summary, modern imaging detects many small indeterminate testicular lesions, of which the majority are benign. Awareness is growing of surveillance and organ-sparing diagnostic and treatment strategies with the aim of minimizing rates of overtreatment with radical orchidectomy.

Monte Carlo simulation of the system performance of a long axial field-of-view PET based on monolithic LYSO detectors

BackgroundIn light of the milestones achieved in PET design so far, further sensitivity improvements aim to optimise factors such as the dose, throughput, and detection of small lesions. While several longer axial field-of-view (aFOV) PET systems based on pixelated detectors have been installed, continuous monolithic scintillation detectors recently gained increased attention due to their depth of interaction capability and superior intrinsic resolution. As a result, the aim of this work is to present and evaluate the performance of two long aFOV, monolithic LYSO-based PET scanner designs.MethodsGeant4 Application for Tomographic Emission (GATE) v9.1 was used to perform the simulations. Scanner designs A and B have an aFOV of 36.2 cm (7 rings) and 72.6 cm (14 rings), respectively, with 40 detector modules per ring each and a bore diameter of 70 cm. Each module is a 50 × 50 × 16 mm3 monolithic LYSO crystal. Sensitivity, noise equivalent count rate (NECR), scatter fraction, spatial resolution, and image quality tests were performed based on NEMA NU-2018 standards.ResultsThe sensitivity of design A was calculated to be 29.2 kcps/MBq at the centre and 27 kcps/MBq at 10 cm radial offset; similarly, the sensitivity of design B was found to be 106.8 kcps/MBq and 98.3 kcps/MBq at 10 cm radial offset. NECR peaks were reached at activity concentrations beyond the range of activities used for clinical studies. In terms of spatial resolution, the values for the point sources were below 2 mm for the radial, tangential, and axial full width half maximum. The contrast recovery coefficient ranged from 53% for design B and 4:1 contrast ratio to 90% for design A and 8:1 ratio, with a reasonably low background variability.ConclusionsLonger aFOV PET designs using monolithic LYSO have superior spatial resolution compared to current pixelated total-body PET (TB-PET) scanners. These systems combine high sensitivity with improved contrast recovery.