In vivo diagnosis of skin cancer with a portable Raman spectroscopic device.

In this study, we performed in vivo diagnosis of skin cancer based on implementation of a portable low-cost spectroscopy setup combining analysis of Raman and autofluorescence spectra in the near-infrared region (800-915 nm). We studied 617 cases of skin neoplasms (615 patients, 70 melanomas, 122 basal cell carcinomas, 12 squamous cell carcinomas and 413 benign tumors) in vivo with a portable setup. The studies considered the patients examined by GPs in local clinics and directed to a specialized Oncology Dispensary with suspected skin cancer. Each sample was histologically examined after excisional biopsy. The spectra were classified with a projection on latent structures and discriminant analysis. To check the classification models stability, a 10-fold cross-validation was performed. We obtained ROC AUCs of 0.75 (0.71-0.79; 95% CI), 0.69 (0.63-0.76; 95% CI) and 0.81 (0.74-0.87; 95% CI) for classification of a) malignant and benign tumors, b) melanomas and pigmented tumors and c) melanomas and seborrhoeic keratosis, respectively. The positive and negative predictive values ranged from 20% to 52% and from 73% to 99%, respectively. The biopsy ratio varied from 0.92:1 to 4.08:1 (at sensitivity levels from 90% to 99%). The accuracy of automatic analysis with the proposed system is higher than the accuracy of GPs and trainees, and is comparable or less to the accuracy of trained dermatologists. The proposed approach may be combined with other optical techniques of skin lesion analysis, such as dermoscopy- and spectroscopy-based computer-assisted diagnosis systems to increase accuracy of neoplasms classification.

Feasibility of non-contact cardiorespiratory monitoring using impulse-radio ultra-wideband radar in the neonatal intensive care unit.

BACKGROUND: Current cardiorespiratory monitoring equipment can cause injuries and infections in neonates with fragile skin. Impulse-radio ultra-wideband (IR-UWB) radar was recently demonstrated to be an effective contactless vital sign monitor in adults. The purpose of this study was to assess heart rates (HRs) and respiratory rates (RRs) in the neonatal intensive care unit (NICU) using IR-UWB radar and to evaluate its accuracy and reliability compared to conventional electrocardiography (ECG)/impedance pneumography (IPG). METHODS: The HR and RR were recorded in 34 neonates between 3 and 72 days of age during minimal movement (51 measurements in total) using IR-UWB radar (HRRd, RRRd) and ECG/IPG (HRECG, RRIPG) simultaneously. The radar signals were processed in real time using algorithms for neonates. Radar and ECG/IPG measurements were compared using concordance correlation coefficients (CCCs) and Bland-Altman plots. RESULTS: From the 34 neonates, 12,530 HR samples and 3,504 RR samples were measured. Both the HR and RR measured using the two methods were highly concordant when the neonates had minimal movements (CCC = 0.95 between the RRRd and RRIPG, CCC = 0.97 between the HRRd and HRECG). In the Bland-Altman plot, the mean biases were 0.17 breaths/min (95% limit of agreement [LOA] -7.0-7.3) between the RRRd and RRIPG and -0.23 bpm (95% LOA -5.3-4.8) between the HRRd and HRECG. Moreover, the agreement for the HR and RR measurements between the two modalities was consistently high regardless of neonate weight. CONCLUSIONS: A cardiorespiratory monitor using IR-UWB radar may provide accurate non-contact HR and RR estimates without wires and electrodes for neonates in the NICU.

A Millimeter-Scale Crystal-Less MICS Transceiver for Insertable Smart Pills.

This paper presents a millimeter-scale crystal-less wireless transceiver for volume-constrained insertable pills. Operating in the 402-405 MHz medical implant communication service (MICS) band, the phase-tracking receiver-based over-the-air carrier recovery has a ±160 ppm coverage. A fully integrated adaptive antenna impedance matching solution is proposed to calibrate the antenna impedance variation inside the body. A tunable matching network (TMN) with single inductor performs impedance matching for both transmitter (TX) and receiver (RX) and TX/RX mode switching. To dynamically calibrate the antenna impedance variation over different locations and diet conditions, a loop-back power detector using self-mixing is adopted, which expands the power contour up to 4.8 VSWR. The transceiver is implemented in a 40-nm CMOS technology, occupying 2 mm2 die area. The transceiver chip and a miniature antenna are integrated in a 3.5 × 15 mm2 area prototype wireless module. It has a receiver sensitivity of -90 dBm at 200 kbps data rate and delivers up to - 25 dBm EIRP in the wireless measurement with a liquid phantom.

Biological Living Cell in-Flow Detection Based on Microfluidic Chip and Compact Signal Processing Circuit.

Detection and counting of biological living cells in continuous fluidic flows play an essential role in many applications for early diagnosis and treatment of diseases. In this regard, this study highlighted the proposal of a biochip system for detecting and enumerating human lung carcinoma cell flow in the microfluidic channel. The principle of detection was based on the change of impedance between sensing electrodes integrated in the fluidic channel, due to the presence of a biological cell in the sensing region. A compact electronic module was built to sense the unbalanced impedance between the sensing microelectrodes. It consisted of an instrumentation amplifier stage to obtain the difference between the acquired signals, and a lock-in amplifier stage to demodulate the signals at the stimulating frequency as well as to reject noise at other frequencies. The performance of the proposed system was validated through experiments of A549 cells detection as they passed over the microfluidic channel. The experimental results indicated the occurrence of large spikes (up to approximately 180 mV) over the background signal according to the passage of a single A549 cell in the continuous flow. The proposed device is simple-to-operate, inexpensive, portable, and exhibits high sensitivity, which are suitable considerations for developing point-of-care applications.

A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End.

In this article, we present a real-time electroencephalogram (EEG) based depth of anesthesia (DoA) monitoring system in conjunction with a deep learning framework, AnesNET. An EEG analog front-end (AFE) that can compensate ±380-mV electrode DC offset using a coarse digital DC servo loop is implemented in the proposed system. The EEG-based MAC, EEGMAC, is introduced as a novel index to accurately predict the DoA, which is designed for applying to patients anesthetized by both volatile and intravenous agents. The proposed deep learning protocol consists of four layers of convolutional neural network and two dense layers. In addition, we optimize the complexity of the deep neural network (DNN) to operate on a microcomputer such as the Raspberry Pi 3, realizing a cost-effective small-size DoA monitoring system. Fabricated in 110-nm CMOS, the prototype AFE consumes 4.33 µW per channel and has the input-referred noise of 0.29 µVrms from 0.5 to 100 Hz with the noise efficiency factor of 2.2. The proposed DNN was evaluated with pre-recorded EEG data from 374 subjects administrated by inhalational anesthetics under surgery, achieving an average squared and absolute errors of 0.048 and 0.05, respectively. The EEGMAC with subjects anesthetized by an intravenous agent also showed a good agreement with the bispectral index value, confirming the proposed DoA index is applicable to both anesthetics. The implemented monitoring system with the Raspberry Pi 3 estimates the EEGMAC within 20 ms, which is about thousand-fold faster than the BIS estimation in literature.

Applied current thermoacoustic imaging for biological tissues.

BACKGROUND: The large differences of electrical characteristics can be used to reflect the physiological and pathological changes about biological tissues, and it can provide evidence for the early diagnosis and treatment of cancer in potential applications. OBJECTIVE: This paper describes a method called Applied Current Thermoacoustic Imaging (ACTAI) and explores the theory and demonstrates a low conductivity numerical simulation and fresh pork experimental studies. METHODS: In this paper, firstly, the principle of ACTAI is studied. In ACTAI, a target is applied with a microsecond width Gaussian pulse current. Then the target absorbs Joule heat and expands instantaneously, sending out thermoacoustic waves. The waves contain the conductivity information of the target. The waves received by sound transducers are processed by the time inversion method to reconstruct the sound source distribution of the target to illustrate the conductivity information of the target. Secondly, a square model with low conductivity was used as a target to conduct numerical simulation of ACTAI. Lastly, a fresh pork experiment study was conducted. RESULTS: The presented experimental results suggest that ACTAI can identify the conductivity changes information of the target with perfect imagery contrast and deep penetration. CONCLUSION: The ACTAI modality would benefit from the noncontact measurement and can be convenient for clinical application.

Quantitative Assessment of the Effects of Compression on Deep Learning in Digital Pathology Image Analysis.

PURPOSE: Deep learning (DL), a class of approaches involving self-learned discriminative features, is increasingly being applied to digital pathology (DP) images for tasks such as disease identification and segmentation of tissue primitives (eg, nuclei, glands, lymphocytes). One application of DP is in telepathology, which involves digitally transmitting DP slides over the Internet for secondary diagnosis by an expert at a remote location. Unfortunately, the places benefiting most from telepathology often have poor Internet quality, resulting in prohibitive transmission times of DP images. Image compression may help, but the degree to which image compression affects performance of DL algorithms has been largely unexplored. METHODS: We investigated the effects of image compression on the performance of DL strategies in the context of 3 representative use cases involving segmentation of nuclei (n = 137), segmentation of lymph node metastasis (n = 380), and lymphocyte detection (n = 100). For each use case, test images at various levels of compression (JPEG compression quality score ranging from 1-100 and JPEG2000 compression peak signal-to-noise ratio ranging from 18-100 dB) were evaluated by a DL classifier. Performance metrics including F1 score and area under the receiver operating characteristic curve were computed at the various compression levels. RESULTS: Our results suggest that DP images can be compressed by 85% while still maintaining the performance of the DL algorithms at 95% of what is achievable without any compression. Interestingly, the maximum compression level sustainable by DL algorithms is similar to where pathologists also reported difficulties in providing accurate interpretations. CONCLUSION: Our findings seem to suggest that in low-resource settings, DP images can be significantly compressed before transmission for DL-based telepathology applications.

Efficient Denoising Framework for Mammogram Images with a New Impulse Detector and Non-Local Means.

OBJECTIVE: The survival rates of breast cancer are increasing as screening and diagnosis improve. The removal of noise is revealed to be a significant step for automatic - computer aided detection (CAD) of microcalcification in digital mammography. METHODS: In this paper, a combined approach for eradicating impulse noise from digital mammograms is proposed. The process is achieved in two stages, detection of noise followed by filtering of noise. The detection of noise is carried out by using Modified Robust Outlyingness Ratio (mROR) trailed by an extended NL (Non-Local)-means filter for filtering mechanism. RESULTS: According to the value of mROR, all pixels in mammogram images are divided into four distinct groups. In each cluster, many decision rules are then applied for detecting the impulse noise. Filtering is done with NL-means filter by providing a reference mammogram image. CONCLUSION: The comparative analysis and evaluated results are compared with some existing filters which indicate that the proposed structure outperforms the analysed result of others.

Electrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable.

Surgery is a particularly potent stressor and the detrimental effects of stress on people undergoing any surgery is indisputable. When left unchecked, the pre-surgery stress adversely impacts people's physical and psychological well-being, and may even evolve into severe pathological states. Therefore, it is essential to identify levels of preoperative stress in surgical patients. This paper focuses on developing an automatic pre-surgery stress detection scheme based on electrodermal activity (EDA). The measurement set up involves a wrist wearable that monitors EDA of a subject continuously in the most non-invasive and unobtrusive manner. Data were collected from 41 subjects [17 females and 24 males, age: 54.8 ± 16.8 years (mean ± SD)], who subsequently underwent different surgical procedures at the Sri Ramakrishna Hospital, Coimbatore, India. A supervised machine learning algorithm that detects motion artifacts in the recorded EDA data was developed. It yielded an accuracy of 97.83% on a new user dataset. The clean EDA data were further analyzed to determine low, moderate, and high levels of stress. A novel localized supervised learning scheme based on the adaptive partitioning of the dataset was adopted for stress detection. Consequently, the interindividual variability in the EDA due to person-specific factors such as the sweat gland density and skin thickness, which may lead to erroneous classification, could be eliminated. The scheme yielded a classification accuracy of 85.06% on a new user dataset and proved to be more effective than the general supervised classification model.

Harmonic Motion Imaging of Pancreatic Tumor Stiffness Indicates Disease State and Treatment Response.

PURPOSE: Pancreatic ductal adenocarcinoma (PDA) is a common, deadly cancer that is challenging both to diagnose and to manage. Its hallmark is an expansive, desmoplastic stroma characterized by high mechanical stiffness. In this study, we sought to leverage this feature of PDA for two purposes: differential diagnosis and monitoring of response to treatment. EXPERIMENTAL DESIGN: Harmonic motion imaging (HMI) is a functional ultrasound technique that yields a quantitative relative measurement of stiffness suitable for comparisons between individuals and over time. We used HMI to quantify pancreatic stiffness in mouse models of pancreatitis and PDA as well as in a series of freshly resected human pancreatic cancer specimens. RESULTS: In mice, we learned that stiffness increased during progression from preneoplasia to adenocarcinoma and also effectively distinguished PDA from several forms of pancreatitis. In human specimens, the distinction of tumors versus adjacent pancreatitis or normal pancreas tissue was even more stark. Moreover, in both mice and humans, stiffness increased in proportion to tumor size, indicating that tuning of mechanical stiffness is an ongoing process during tumor progression. Finally, using a brca2-mutant mouse model of PDA that is sensitive to cisplatin, we found that tissue stiffness decreases when tumors respond successfully to chemotherapy. Consistent with this observation, we found that tumor tissues from patients who had undergone neoadjuvant therapy were less stiff than those of untreated patients. CONCLUSIONS: These findings support further development of HMI for clinical applications in disease staging and treatment response assessment in PDA.

Development of Weak Signal Recognition and an Extraction Algorithm for Raman Imaging.

Improving time resolution of Raman imaging is essential for the observation of dynamic processes involved in interfacial catalysis and biological systems. The crucial step is how to recognize and extract weak Raman signals overwhelmed in the strong noise under the low signal-to-noise ratio (SNR) condition. Here, by exploring the relationship between the SNR of a single Raman spectrum and the structural similarity (SSIM; the key parameter evaluating the image quality) of the whole image, we determined a semiempirical threshold with SNR = 0 dB for clear imaging for the first time. Therefore, we proposed one signal processing algorithm for fast Raman imaging by reconstructing the Raman spectrum with the aid of weak signal processing: extracting the reliable Raman signal of the target under the low SNR and then determining the suitable scanning time to obtain the Raman image with a trustworthy image quality. In the first step, fast Fourier transform (FFT), least squares, and 2-D median filter are sequentially applied to improve the SNR of each raw Raman spectrum. In the second step, a local SNR evaluation strategy is developed to predict image quality as well as the determination of clear imaging. The proposed method was successfully applied to the fast imaging of the cell under the low SNR condition.

A Wireless Radio Frequency Triggered Acquisition Device (WRAD) for Self-Synchronised Measurements of the Rate of Change of the MRI Gradient Vector Field for Motion Tracking.

In this paper, we present a device that is capable of wireless synchronization to the MRI pulse sequence time frame with sub-microsecond precision. This is achieved by detecting radio frequency pulses in the parent pulse sequence using a small resonant circuit. The device incorporates a 3-axis pickup coil, constructed using conventional printed circuit board (PCB) manufacturing techniques, to measure the rate of change of the gradient waveforms with respect to time. Using Maxwell's equations, assuming negligible rates of change of curl and divergence, a model of the expected gradient derivative (slew) vector field is presented. A 3-axis Hall effect magnetometer allows for the measurement of the direction of the static magnetic field in the device co-ordinate frame. By combining the magnetometer measurement with the pickup coil voltages and slew vector field model, the orientation and position can be determined to within a precision of 0.1 degrees and 0.1 mm, respectively, using a pulse series lasting 880 µs . The gradient pulses are designed to be sinusoidal, enabling the detection of a phase shift between the time frame of the pickup coil digitization circuit and the gradient amplifiers. The signal processing is performed by a low power micro-controller on the device and the results are transmitted out of the scanner bore using a low latency 2.4 GHz radio link. The device identified an unexpected 40 kHz oscillation relating to the pulse width modulation frequency of the gradient amplifiers that is predominantly in the direction of the static magnetic field. The proposed wireless radio frequency triggered acquisition device enables users to probe the scanner gradient slew vector field with minimal hardware set-up and shows promise for the future developments in the prospective motion correction.

Development of a Non-invasive Device for Swallow Screening in Patients at Risk of Oropharyngeal Dysphagia: Results from a Prospective Exploratory Study.

Oropharyngeal dysphagia is prevalent in several at-risk populations, including post-stroke patients, patients in intensive care and the elderly. Dysphagia contributes to longer hospital stays and poor outcomes, including pneumonia. Early identification of dysphagia is recommended as part of the evaluation of at-risk patients, but available bedside screening tools perform inconsistently. In this study, we developed algorithms to detect swallowing impairment using a novel accelerometer-based dysphagia detection system (DDS). A sample of 344 individuals was enrolled across seven sites in the United States. Dual-axis accelerometry signals were collected prospectively with simultaneous videofluoroscopy (VFSS) during swallows of liquid barium stimuli in thin, mildly, moderately and extremely thick consistencies. Signal processing classifiers were trained using linear discriminant analysis and 10,000 random training-test data splits. The primary objective was to develop an algorithm to detect impaired swallowing safety with thin liquids with an area under receiver operating characteristic curve (AUC) > 80% compared to the VFSS reference standard. Impaired swallowing safety was identified in 7.2% of the thin liquid boluses collected. At least one unsafe thin liquid bolus was found in 19.7% of participants, but participants did not exhibit impaired safety consistently. The DDS classifier algorithms identified participants with impaired thin liquid swallowing safety with a mean AUC of 81.5%, (sensitivity 90.4%, specificity 60.0%). Thicker consistencies were effective for reducing the frequency of penetration-aspiration. This DDS reached targeted performance goals in detecting impaired swallowing safety with thin liquids. Simultaneous measures by DDS and VFSS, as performed here, will be used for future validation studies.

A Coupled Magnetoelastic Strain Sensor Array for Guiding and Monitoring Hernia Repairs.

OBJECTIVE: Ventral hernia repairs using mesh prosthetics suffer from high recurrence rates, with 10%-20% of repairs failing within three years. Uneven distribution of stress within the implanted mesh prosthetic is thought to contribute to the high recurrence rate. We propose a method for providing quantitative guidance and monitoring of hernia repairs using an array of magnetoelastic strain sensors. METHODS: The magnetoelastic strain sensors presented here are based on a coupled design to achieve measurements with higher signal-to-noise ratio (SNR). A first magnetoelastic element (the transducer) is bonded to the mesh prosthetic and is characterized by a strain-dependent magnetic field. The resonance frequency of a second magnetoelastic element (the resonator) encased in a rigid casing is biased by the transducer element's magneticity and can be measured noninvasively using an external interrogation coil. The coupled magnetoelastic strain sensors are assembled using a combination of photochemical machining, patterning, and heat sealing. RESULTS: The dynamic range of the coupled sensors can be tuned by altering the transducer geometry. Additional spring elements are integrated onto the transducer element to achieve high dynamic range measurements saturating at 74 millistrains. CONCLUSION: A coupled magnetoelastic strain sensor combines a transducer with an encased resonator element to measure strain with high SNR on an implantable flexible hernia mesh substrate. SIGNIFICANCE: This study provides surgeons and researchers with a clinically relevant tool to quantify the strain distributions within implanted mesh prosthetics, with the ultimate goal of reducing the recurrence rate of ventral hernia repairs.

Using Brain Activation to Evaluate Arrangements Aiding Hand-Eye Coordination in Surgical Robot Systems.

GOAL: To realize intuitive, minimally invasive surgery, surgical robots are often controlled using master-slave systems. However, the surgical robot's structure often differs from that of the human body, so the arrangement between the monitor and master must reflect this physical difference. In this study, we validate the feasibility of an embodiment evaluation method that determines the arrangement between the monitor and master. In our constructed cognitive model, the brain's intraparietal sulcus activates significantly when somatic and visual feedback match. Using this model, we validate a cognitively appropriate arrangement between the monitor and master. METHODS: In experiments, we measure participants' brain activation using an imaging device as they control the virtual surgical simulator. Two experiments are carried out that vary the monitor and hand positions. CONCLUSION: There are two common arrangements of the monitor and master at the brain activation's peak: One is placing the monitor behind the master, so the user feels that the system is an extension of his arms into the monitor; the other arranges the monitor in front of the master, so the user feels the correspondence between his own arm and the virtual arm in the monitor. SIGNIFICANCE: From these results, we conclude that the arrangement between the monitor and master impacts embodiment, enabling the participant to feel apparent posture matches in master-slave surgical robot systems.

Suppression Technique of HeLa Cell Proliferation Using Ultrasonic Power Amplifiers Integrated with a Series-Diode Linearizer.

A series-diode linearizer scheme is developed, which can possibly generate higher voltage signals. To verify our proposed concept, ultrasonic power amplifiers with and without the linearizer were tested for HeLa cells proliferation in vitro. In general, ultrasonic stimulus initiates the process of cavitation which can cause cell lysis and disruption of cell attachment. The cavitation can also induce formation of free radicals so that a rigid membrane of malignant cancer cells have increased sensitivity to ultrasonic stimulus. The cell density of the control group increased up to almost 100% on Day 3. However, cell densities of the experimental group when using an isolated ultrasonic power amplifier, and ultrasonic power amplifiers integrated with the linearizer at 1 V and 5 V DC (direct current) bias could be suppressed more than that when using an ultrasonic power amplifier (90.7 ± 1.2%, 75.8 ± 3.5%, and 68.1 ± 1.1%, respectively). Additionally, the proliferation suppressing ratios of each experimental group confirmed that the cell density decrements of the experimental groups exhibited statistical significance compared to the control group (ultrasonic power amplifier = 8.87%, ultrasonic power amplifier with 1 V biased linearizer = 23.87%, and ultrasonic power amplifier with 5 V biased linearizer = 31.56%).

Selective retina therapy: toward an optically controlled automatic dosing.

Selective retina therapy (SRT) targets the retinal pigment epithelium (RPE) with pulsed laser irradiation by inducing microbubble formation (MBF) at the intracellular melanin granula, which leads to selective cell disruption. The following wound healing process rejuvenates the chorio-retinal junction. Pulse energy thresholds for selective RPE effects vary intra- and interindividually. We present the evaluation of an algorithm that processes backscattered treatment light to detect MBF as an indicator of RPE cell damage since these RPE lesions are invisible during treatment. Eleven patients with central serous chorioretinopathy and four with diabetic macula edema were treated with a SRT system, which uses a wavelength of 527 nm, a repetition rate of 100 Hz, and a pulse duration of 1.7 µs. Fifteen laser pulses with stepwise increasing pulse energy were applied per treatment spot. Overall, 4626 pulses were used for algorithm parameter optimization and testing. Sensitivity and specificity were the metrics maximized through an automatic optimization process. Data were verified by fluorescein angiography. A sensitivity of 1 and a specificity of 0.93 were achieved. The method introduced in this paper can be used for guidance or automatization of microbubble-related treatments like SRT or selective laser trabeculoplasty.

Magnetic Levitation Haptic Augmentation for Virtual Tissue Stiffness Perception.

Haptic-based tissue stiffness perception is essential for palpation training system, which can provide the surgeon haptic cues for improving the diagnostic abilities. However, current haptic devices, such as Geomagic Touch, fail to provide immersive and natural haptic interaction in virtual surgery due to the inherent mechanical friction, inertia, limited workspace and flawed haptic feedback. To tackle this issue, we design a novel magnetic levitation haptic device based on electromagnetic principles to augment the tissue stiffness perception in virtual environment. Users can naturally interact with the virtual tissue by tracking the motion of magnetic stylus using stereoscopic vision so that they can accurately sense the stiffness by the magnetic stylus, which moves in the magnetic field generated by our device. We propose the idea that the effective magnetic field (EMF) is closely related to the coil attitude for the first time. To fully harness the magnetic field and flexibly generate the specific magnetic field for obtaining required haptic perception, we adopt probability clouds to describe the requirement of interactive applications and put forward an algorithm to calculate the best coil attitude. Moreover, we design a control interface circuit and present a self-adaptive fuzzy proportion integration differentiation (PID) algorithm to precisely control the coil current. We evaluate our haptic device via a series of quantitative experiments which show the high consistency of the experimental and simulated magnetic flux density, the high accuracy (0.28 mm) of real-time 3D positioning and tracking of the magnetic stylus, the low power consumption of the adjustable coil configuration, and the tissue stiffness perception accuracy improvement by 2.38 percent with the self-adaptive fuzzy PID algorithm. We conduct a user study with 22 participants, and the results suggest most of the users can clearly and immersively perceive different tissue stiffness and easily detect the tissue abnormality. Experimental results demonstrate that our magnetic levitation haptic device can provide accurate tissue stiffness perception augmentation with natural and immersive haptic interaction.

Improvement of dose calculation in radiation therapy due to metal artifact correction using the augmented likelihood image reconstruction.

BACKGROUND: Metal artifacts caused by high-density implants lead to incorrectly reconstructed Hounsfield units in computed tomography images. This can result in a loss of accuracy in dose calculation in radiation therapy. This study investigates the potential of the metal artifact reduction algorithms, Augmented Likelihood Image Reconstruction and linear interpolation, in improving dose calculation in the presence of metal artifacts. MATERIALS AND METHODS: In order to simulate a pelvis with a double-sided total endoprosthesis, a polymethylmethacrylate phantom was equipped with two steel bars. Artifacts were reduced by applying the Augmented Likelihood Image Reconstruction, a linear interpolation, and a manual correction approach. Using the treatment planning system Eclipse™, identical planning target volumes for an idealized prostate as well as structures for bladder and rectum were defined in corrected and noncorrected images. Volumetric modulated arc therapy plans have been created with double arc rotations with and without avoidance sectors that mask out the prosthesis. The irradiation plans were analyzed for variations in the dose distribution and their homogeneity. Dosimetric measurements were performed using isocentric positioned ionization chambers. RESULTS: Irradiation plans based on images containing artifacts lead to a dose error in the isocenter of up to 8.4%. Corrections with the Augmented Likelihood Image Reconstruction reduce this dose error to 2.7%, corrections with linear interpolation to 3.2%, and manual artifact correction to 4.1%. When applying artifact correction, the dose homogeneity was slightly improved for all investigated methods. Furthermore, the calculated mean doses are higher for rectum and bladder if avoidance sectors are applied. CONCLUSION: Streaking artifacts cause an imprecise dose calculation within irradiation plans. Using a metal artifact correction algorithm, the planning accuracy can be significantly improved. Best results were accomplished using the Augmented Likelihood Image Reconstruction algorithm.