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Cumulative Effective Dose and Cancer Risk of Pediatric Population in Repetitive Whole-Body Scan Using Dual-Energy X-Ray Absorptiometry.

This study aims to quantitatively evaluate the cumulative effective dose and associated cancer risk of pediatric patients of US and Hong Kong population undergoing repetitive whole-body scans with dual-energy X-ray absorptiometry (DXA) during their diagnosis and follow-up periods. Organ-absorbed doses of pediatric patients undergoing DXA whole-body scan have been computer simulated using patient imaging parameters input to the Monte Carlo software PCXMC. Gender- and age-specific effective doses have been calculated with the simulated organ-absorbed doses using the ICRP-103 approach. The associated radiation-induced cancer risk, expressed as lifetime attributable cancer risk (LAR), has been estimated according to the method introduced in the Biological Effects of Ionizing Radiation VII report. Mathematical fitting for effective dose and for LAR, as a function of age at exposure, has been analytically obtained to quantitatively estimate the cumulated effective dose and LAR for pediatric patients of US and Hong Kong population with repetitive DXA whole-body scan during their follow-up period. The effective dose of a single DXA whole-body scan for patients exposed at the age between 5 and 18 years was calculated as 8.47-17.68 µSv. The corresponding LAR for US and Hong Kong population was between the range of 4.57 × 10-7 and 7.14 × 10-7. The cumulative effective dose of DXA whole-body scan for patients exposed annually at age between 5 and 18 years was calculated as 180 µSv for girls and 168 µSv for boys. The corresponding cumulative LAR for US and Hong Kong population was calculated as 3.77 × 10-6 to 5.48 × 10-6. Girls would be at a statistically significant higher cumulated cancer risk than boys under the same whole-body DXA protocol (p = 0.03). The probability of cumulative LAR for pediatric populations undergoing annual DXA whole-body scan is regarded as minimal. We demonstrate the use of computer simulation and analytic formulation to quantitatively obtain the cumulated effective dose and cancer risk at any age of exposure, which are useful information for medical personnel to track patient radiation dose and to alleviate patients' parents concern about radiation safety in repetitive whole-body scan using DXA.

Development of a predictive energy equation for maintenance hemodialysis patients: a pilot study.

OBJECTIVE: The study objectives were to explore the predictors of measured resting energy expenditure (mREE) among a sample of maintenance hemodialysis (MHD) patients, to generate a predictive energy equation (MHDE), and to compare such models to another commonly used predictive energy equation in nutritional care, the Mifflin-St. Jeor equation (MSJE). DESIGN AND METHODS: The study was a retrospective, cross-sectional cohort design conducted at the Vanderbilt University Medical Center. Study subjects were adult MHD patients (N = 67). Data collected from several clinical trials were analyzed using Pearson's correlation and multivariate linear regression procedures. Demographic, anthropometric, clinical, and laboratory data were examined as potential predictors of mREE. Limits of agreement between the MHDE and the MSJE were evaluated using Bland-Altman plots. The a priori α was set at P < .05. The main outcome measure was mREE. RESULTS: The mean age of the sample was 47 ± 13 years. Fifty participants (75.6%) were African American, 7.5% were Hispanic, and 73.1% were males. Fat-free mass (FFM), serum albumin (ALB), age, weight, serum creatinine (CR), height, body mass index, sex, high-sensitivity C-reactive protein (CRP), and fat mass (FM) were all significantly (P < .05) correlated with mREE. After screening for multi-collinearity, the best predictive model (MHDE-lean body mass [LBM]) of mREE included (R(2) = 0.489) FFM, ALB, age, and CRP. Two additional models (MHDE-CRP and MHDE-CR) with acceptable predictability (R(2) = 0.460 and R(2) = 0.451) were derived to improve the clinical utility of the developed energy equation (MHDE-LBM). Using Bland-Altman plots, the MHDE over- and underpredicted mREE less often than the MSJE. CONCLUSIONS: Predictive models (MHDE) including selective demographic, clinical, and anthropometric data explained less than 50% variance of mREE but had better precision in determining energy requirements for MHD patients when compared with MSJE. Further research is necessary to improve predictive models of mREE in the MHD population and to test its validity and clinical application.

A super-voxel-based method for generating surrogate lung ventilation images from CT.

Purpose: This study aimed to develop and evaluate CTVISVD , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

Volumetric multiphase ventilation imaging based on four-dimensional computed tomography for functional lung avoidance radiotherapy.

PURPOSE: Current computed tomography (CT)-based lung ventilation imaging (CTVI) techniques derive a static ventilation image without temporal information. This research aims to develop a four-dimensional CT (4DCT)-based multiphase dynamic ventilation imaging framework capable of recovering the entire ventilation process throughout the breathing cycle for functional lung avoidance radiotherapy (FLART). METHODS: A total of 15 free-breathing thoracic 4DCT scans of lung or esophageal cancer patients were collected from the public datasets. The lung region of each phase image was first delineated, and then the mask-free isotropic total variation image registration algorithm was used to derive the deformation vector fields between the end-expiration (EE) phase and other phases. As a surrogate of ventilation, the voxel-wise local expansion ratio of each phase relative to the EE phase was estimated using the parameterized Integrated Jacobian Formulation method in the EE phase coordinate. Lastly, the dynamic ventilation images were generated by warping these phase-specific local expansion distributions with a same geometry into their respective breathing phases. Quantitative analysis, including interphase Spearman correlation coefficients, voxel-wise, and regional-wise expansion/contraction tracking, were performed to indirectly validate the proposed method. RESULTS: The proposed method maintains the physiological meaning of ventilation on each phase and enables to recover the dynamic lung ventilation process. The mean interphase Spearman correlations ranged between 0.23 ± 0.20 and 0.93 ± 0.04 and decreased near the EE phase. Only 26.2% (2.59E + 6 out of 9.89E + 6) of lung voxels exhibited the same expansion/contraction pattern as the global lung. Qualitative and quantitative evaluations of the interphase ventilation distribution difference show that ventilation spatiotemporal heterogeneities generally exist during respiration. CONCLUSIONS: In contrast to conventional CTVI metrics, our method enables to extract additional phase-resolved respiration-correlated information and reflects the generally existed ventilation spatiotemporal heterogeneities. Subsequent studies with quantitative phase-by-phase cross-modality evaluations will further explore its potential to deepen our understanding of lung function and respiration mechanics and also to facilitate more accurate implementation of FLART.

Corrigendum: Investigation of a novel deep learning-based computed tomography perfusion mapping framework for functional lung avoidance radiotherapy.

[This corrects the article DOI: 10.3389/fonc.2021.644703.].

A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients.

Purpose: Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods: SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman's correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results: The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion: For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.

Investigation of a Novel Deep Learning-Based Computed Tomography Perfusion Mapping Framework for Functional Lung Avoidance Radiotherapy.

Functional lung avoidance radiation therapy aims to minimize dose delivery to the normal lung tissue while favoring dose deposition in the defective lung tissue based on the regional function information. However, the clinical acquisition of pulmonary functional images is resource-demanding, inconvenient, and technically challenging. This study aims to investigate the deep learning-based lung functional image synthesis from the CT domain. Forty-two pulmonary macro-aggregated albumin SPECT/CT perfusion scans were retrospectively collected from the hospital. A deep learning-based framework (including image preparation, image processing, and proposed convolutional neural network) was adopted to extract features from 3D CT images and synthesize perfusion as estimations of regional lung function. Ablation experiments were performed to assess the effects of each framework component by removing each element of the framework and analyzing the testing performances. Major results showed that the removal of the CT contrast enhancement component in the image processing resulted in the largest drop in framework performance, compared to the optimal performance (~12%). In the CNN part, all the three components (residual module, ROI attention, and skip attention) were approximately equally important to the framework performance; removing one of them resulted in a 3-5% decline in performance. The proposed CNN improved ~4% overall performance and ~350% computational efficiency, compared to the U-Net model. The deep convolutional neural network, in conjunction with image processing for feature enhancement, is capable of feature extraction from CT images for pulmonary perfusion synthesis. In the proposed framework, image processing, especially CT contrast enhancement, plays a crucial role in the perfusion synthesis. This CTPM framework provides insights for relevant research studies in the future and enables other researchers to leverage for the development of optimized CNN models for functional lung avoidance radiation therapy.

Deep Learning-Based Computed Tomography Perfusion Mapping (DL-CTPM) for Pulmonary CT-to-Perfusion Translation.

PURPOSE: Our purpose was to develop a deep learning-based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images. METHODS AND MATERIALS: This paper presents a retrospective analysis of the pulmonary technetium-99m-labeled macroaggregated albumin single-photon emission CT (SPECT)/CT scans obtained from 73 patients at Queen Mary Hospital in Hong Kong in 2019. The left and right lung scans were separated to double the size of the data set to 146. A 3-dimensional attention residual neural network was constructed to extract textural features from the CT images and reconstruct corresponding functional images. Eighty-four samples were randomly selected for training and cross-validation, and the remaining 62 were used for model testing in terms of voxel-wise agreement and function-wise concordance. To assess the voxel-wise agreement, the Spearman's correlation coefficient (R) and structural similarity index measure between the images predicted by the DL-CTPM and the corresponding SPECT perfusion images were computed to assess the statistical and perceptual image similarities, respectively. To assess the function-wise concordance, the Dice similarity coefficient (DSC) was computed to determine the similarity of the low/high functional lung volumes. RESULTS: The evaluation of the voxel-wise agreement showed a moderate-to-high voxel value correlation (0.6733 ± 0.1728) and high structural similarity (0.7635 ± 0.0697) between the SPECT and DL-CTPM predicted perfusions. The evaluation of the function-wise concordance obtained an average DSC value of 0.8183 ± 0.0752 for high-functional lungs (range, 0.5819-0.9255) and 0.6501 ± 0.1061 for low-functional lungs (range, 0.2405-0.8212). Ninety-four percent of the test cases demonstrated high concordance (DSC >0.7) between the high-functional volumes contoured from the predicted and ground-truth perfusions. CONCLUSIONS: We developed a novel DL-CTPM method for estimating perfusion-based lung functional images from the CT domain using a 3-dimensional attention residual neural network, which yielded moderate-to-high voxel-wise approximations of lung perfusion. To further contextualize these results toward future clinical application, a multi-institutional large-cohort study is warranted.

Upgrading pacemaker patients with right ventricular apical pacing to right ventricular septal pacing improves left ventricular performance and functional capacity.

BACKGROUND: Right ventricular (RV) apical pacing results in abnormal left ventricular (LV) electrical and mechanical activation and is associated with an increased risk of developing heart failure. Chronic RV septal pacing has been shown to be superior to RV apical pacing in newly implanted patients. However, whether RV septal pacing can reverse deleterious effects of RV apical pacing remain unclear. METHODS: We evaluated the effects of RV septal pacing on LV performance and functional capacity before and at 18 months after device replacement in 12 patients with previously permanent RV apical pacing and in 12 control patients that continued RV apical pacing. All patients underwent radionuclide ventriculography and 6-minute hallwalk (6-MHW) test before replacement (baseline) and at 18 months afterward to determine changes in LV performance and functional capacity, respectively. RESULTS: After RV septal upgraded, there was a significant decrease in paced QRS duration (171.2 +/- 3.9 ms to 160.4 +/- 3.5 ms, P = 0.0016), increase in LV ejection fraction (55.2 +/- 2.6% vs 60.4 +/- 2.9%, P = 0.0002), the peak ventricular filling rate (2.60 +/- 0.13 s(-1) vs 3.01 +/- 0.14 s(-1), P = 0.046), and 6-MHW (308.2 +/- 31.6 m vs 355.5 +/- 34.2 m, P = 0.015) at 18 months compared with baseline. No changes in these parameters were observed in the control group (P > 0.05). CONCLUSION: RV septal pacing upgraded improves LV systolic and diastolic function and functional capacity in patients with previously permanent RV apical pacing. These findings suggest that RV septal pacing can reverse the deleterious effects of RV apical pacing in patients who required permanent ventricular pacing.

Impacts of ventricular rate regularization pacing at right ventricular apical vs. septal sites on left ventricular function and exercise capacity in patients with permanent atrial fibrillation.

AIMS: The deleterious effects of right ventricular apex (RVA) pacing may offset the potential benefit of ventricular rate (VR) regularization during atrial fibrillation (AF). Recent studies suggested that right ventricular septal (RVS) pacing may prevent the potential deleterious effects of RVA pacing and enhance the VR regularization (VRR) with ventricular pacing due to closer proximity of the pacing site to the retrograde atrioventricular conduction. METHODS AND RESULTS: We randomized 24 patients with permanent AF and symptomatic bradycardia to undergo RVA (n = 12) or RVS (n = 12) pacing. A VRR algorithm was programmed for all patients at 6-month after implantation. All patients underwent 6 min hall walk (6MHW) to assess exercise capacity at 6, 12, and 24 months, and radionuclide ventriculography to determine left ventricular ejection fraction (LVEF) at 6 and 24 months. Baseline characteristics were comparable in both groups except pacing QRS duration was significantly shorter during RVS pacing than RVA pacing (132 +/- 4 vs. 151 +/- 6 ms, P = 0.012). In both groups, VRR significantly increased the percentage of ventricular pacing and reduced VR variability (P < 0.05) without increasing mean VR (P > 0.05). At 6 months, 6MHW and LVEF were comparable in patients with RVA and RVS pacing (P > 0.05). At 24 months, patients with RVA pacing had significant decreases in LVEF and 6MHW after VRR pacing (P < 0.05), whereas RVS pacing with VRR preserved LVEF and improved 6MHW (P < 0.05). CONCLUSION: In patients with permanent AF, VRR pacing at RVS, but not at RVA, preserves LVEF and provides incremental benefit for exercise capacity.

Opportunistic screening for osteoporosis in abdominal computed tomography for Chinese population.

This study assessed the possibility of diagnosing and excluding osteoporosis with routine abdominal CT scans in a Chinese population who underwent both DXA and CT for unrelated reasons. Statistical correlation was made between the HU measured of the spine on CT and various parameters on DXA. Diagnostic cutoff points in terms of HU were established for the diagnosis (≤ 136 HU) and exclusion (≥ 175 HU) of osteoporosis on sagittal reformatted images. There was excellent positive and negative predictive value for the DXA-defined diagnostic subgroups and were also comparable with previous studies in Caucasian populations. The authors exhort radiologists to report these incidental findings to facilitate early detection and treatment of osteoporosis in unsuspecting patients to prevent fractures and related complications. PURPOSE: The suspicion for osteoporosis can be raised in diagnostic computed tomography of the abdomen performed for other indications. We derived cutoff thresholds for the attenuation value of the lumbar spinal vertebrae (L1-5) in Hounsfield units (HU) in a Chinese patient population to facilitate implementation of opportunistic screening in radiologists. METHODS: We included 109 Chinese patients who concomitantly underwent abdominal CT and dual X-ray absorptiometry (DXA) within 6 months between July 2014 and July 2017 at a university hospital in Hong Kong. Images were retrospectively reviewed on sagittal reformats, and region-of-interest (ROI) markers were placed on the anterior portion of each of the L1-L5 vertebra to measure the HU. The mean values of CT HU were then compared with the bone mineral density (BMD) and T-score obtained by DXA. Receiver operator characteristic (ROC) curves were generated to determine diagnostic cutoff thresholds and their sensitivity and specificity values. RESULTS: The mean CT HU differed significantly (p < 0.01) for the three DXA-defined BMD categories of osteoporosis (97 HU), of osteopenia (135 HU), and of normal individuals (230 HU). There was good correlation between the mean CT HU and BMD and T-score (Pearson coefficient of 0.62 and 0.61, respectively, p < 0.001). The optimal cutoff point for exclusion of osteoporosis or osteopenia was HU ≥ 175 with negative predictive value as 98.9% and with area under curve (AUC) of ROC curve as 0.97. The optimal cutoff point for diagnosis of osteoporosis was HU ≤ 136 with positive predictive value as 81.2% and with AUC of ROC curve as 0.86. CONCLUSION: This is the first study on osteoporosis diagnosis with routine CT abdominal scans in Chinese population. The cutoff values were comparable with previous studies in Caucasian populations suggesting generalizability. Radiologists should consider routinely reporting these opportunistic findings to facilitate early detection and treatment of osteoporosis to prevent fractures and related complications.

Functional abnormalities in patients with permanent right ventricular pacing: the effect of sites of electrical stimulation.

OBJECTIVES: We sought to evaluate the long-term effects of alternative right ventricular pacing sites on myocardial function and perfusion. BACKGROUND: Previous studies have demonstrated that asynchronous ventricular activation due to right ventricular apical (RVA) pacing alters regional myocardial perfusion and functions. METHODS: We randomized 24 patients with complete atrioventricular block to undergo permanent ventricular stimulation either at the RVA (n = 12) or right ventricular outflow (RVOT) (n = 12). All patients underwent dipyridamole thallium myocardial scintigraphy and radionuclide ventriculography at 6 and 18 months after pacemaker implantation. RESULTS: After pacing, the mean QRS duration was significantly longer during RVA pacing than during RVOT pacing (151 +/- 6 vs. 134 +/- 4 ms, p = 0.03). At six months, the incidence of myocardial perfusion defects (50% vs. 25%) and regional wall motion abnormalities (42% vs. 25%) and the left ventricular ejection fraction (LVEF) (55 +/- 3% vs. 55 +/- 1%) were similar during RVA pacing and RVOT pacing (p > 0.05). However, at 18 months, the incidence of myocardial perfusion defects (83% vs. 33%) and regional wall motion abnormalities (75% vs. 33%) were higher and LVEF (47 +/- 3 vs. 56 +/- 1%) was lower during RVA pacing than during RVOT pacing (all p < 0.05). Patients with RVA pacing had a significant increase in the incidence of myocardial perfusion defects (p < 0.05) and a decrease in LVEF (p < 0.01) between 6 and 18 months, but patients with RVOT pacing did not (p > 0.05). CONCLUSIONS: This study demonstrates that preserved synchronous ventricular activation with RVOT pacing prevents the long-term deleterious effects of RVA pacing on myocardial perfusion and function in patients implanted with a permanent pacemaker.

Breast sentinel lymph node biopsy using radioisotope injection: is one-day better than two-day protocol?

Sentinel lymph node (SLN) biopsy is an evolving treatment approach for patients with operable breast cancer. There have been a number of variations in the biopsy technique. One is the timing of radioisotope injection, which might have a significant influence on the radiation exposure of the personnel in the operating room. The present study aims to compare the one-day with the two-day protocol to see which one is associated with a lower radiation hazard while giving similar results. There were 60 patients recruited; half of them had the SLN biopsy 4 hours after the injection and the other half 24 hours later. Patient characteristics were comparable in both groups. The mean numbers of SLNs found per patient were 1.46 and 1.96 respectively. There was a statistically significant difference in the dosage of radioactivity present in the resected specimen between both groups of patients. However, there were still a number of confounding factors, so the proposed hypothesis of getting less radiation exposure to the medical personnel by using a two-day approach should be further investigated.

Characteristic uptake pattern of bone scintigraphy in patients with hepatocellular carcinoma following treatment with high-intensity focused ultrasound.

PURPOSE: This study retrospectively reviews the characteristic bone scintigraphic findings in 18 patients with hepatocellular carcinoma (HCC) following treatment with high-intensity focused ultrasound (HIFU). A potential complication of HIFU is damage to the tissues along the path of the ultrasound beam and structures superficial to the lesion of interest. METHODS: Patients with hepatocellular carcinoma who underwent a bone scan between 1st December 2005 and 31st December 2011 were considered for this study. Among these patients, only those who had bone scans after the HIFU treatment were included. The time between HIFU treatment and bone scans, HIFU energy, HCC sites, tumour sizes and related radiological findings were evaluated. RESULTS: In total, 20 bone scans of 18 patients were reviewed. Of these scans, two patients were normal; three patients showed decreased uptake, four patients showed increased uptake and nine patients showed mixed uptakes of the bony tracer in their rib cages. The defects were located in the anterior, lateral, anterolateral or posterolateral aspects of the rib cage. The majority of those cold defects were in the right anterior rib cages. SPECT/CT was used to localise the decreased uptake in ribs. The magnetic resonance imaging in individual patients invariably showed ill-defined rim enhancement along the right chest wall, signifying chest wall injury. CONCLUSIONS: The results showed that tissue ablation using HIFU caused tissue injury along the pathway of high-intensity ultrasound beams. The harm to tissues is presented as photopenic area on the rib cages due to necrosis or hot spots due to rib fractures in the bone scan. Since these cold defects are subtle, they are easily overlooked or mistaken as aggressive bony metastasis.

Evaluation of patient effective dose from sentinel lymph node lymphoscintigraphy in breast cancer: a phantom study with SPECT/CT and ICRP-103 recommendations.

OBJECTIVE: To evaluate the effective dose delivered to patients undergoing sentinel lymph node (SLN) lymphoscintigraphy by taking into account both the transmission dose using the CT component of a SPECT/CT system and the (99m)Tc internal emission dose. MATERIALS AND METHODS: An adult female humanoid phantom and a set of thermoluminescent dosimeters were used in dose measurement from the CT transmission irradiation. The choice of measurement organs in the humanoid was guided by the recommendations described in the International Commission on Radiological Protection report number 103 (ICRP-103). The effective doses due to (99m)Tc internal emission source were re-calculated from measurement data reported in our previous study on the same subject with the use of tissue weighting factors of ICRP-103. RESULTS: CT transmission dose is the main contribution to the patient total effective dose for both 1-day and 2-day lymphoscintigraphy protocols and for different surgical procedures. Patients undergoing SLN lymphoscintigraphy receive about the same amount of total effective dose of about 3mSv for both 1-day and 2-day protocol, regardless of whether the tissues containing radioactivity would be excised at surgery or not. CONCLUSION: Although the total effective dose from using SPECT/CT is equivalent to the annual natural background radiation of about 3mSv, nuclear medicine physicians should be aware of the increase in effective dose for SLN lymphoscintigraphy using hybrid imaging technique of SPECT/CT when compared to conventional planar (57)Co flood source for transmission scan. Results from the current study provide update information in radiation exposure to patients undergoing SLN lymphoscintigraphy with the use of SPECT/CT.