Liver Motion Research Articles

Reproducibility of liver ADC measurements using first moment optimized diffusion imaging.

Cardiac-induced liver motion can bias liver ADC measurements and compromise reproducibility. The purpose of this work was to enable motion-robust DWI on multiple MR scanners and assess reproducibility of the resulting liver ADC measurements. First moment-optimized diffusion imaging (MODI) was implemented on three MR scanners with various gradient performances and field strengths. MODI-DWI and conventional Stejskal-Tanner monopolar (MONO) DWI were acquired in eight (N = 8) healthy volunteers on each scanner, and DWI repetitions were combined using three different averaging methods. For each combination of scanner, acquisition, and averaging method, ADC measurements from each liver segment were collected. Systematic differences in ADC values between scanners and methods were assessed with linear mixed effects modeling, and reproducibility was quantified via reproducibility coefficients. MODI reduced left-right liver lobe ADC bias from 0.43 × 10-3 mm2/s (MONO) to 0.19 × 10-3 mm2/s (MODI) when simple (unweighted) repetition averaging was used. The bias was reduced from 0.23 × 10-3 mm2/s to 0.06 × 10-3 mm2/s using weighted averaging, and 0.14 × 10-3 mm2/s to 0.01 × 10-3 mm2/s using squared weighted averaging. There was no significant difference in ADC measurements between field strengths or scanner gradient performance. MODI improved reproducibility coefficients compared to MONO: 0.84 × 10-3 mm2/s vs. 0.63 × 10-3 mm2/s (MODI vs. MONO) for simple averaging, 0.66 × 10-3 mm2/s vs. 0.50 × 10-3 mm2/s for weighted averaging, and 0.61 × 10-3 mm2/s vs. 0.47 × 10-3 mm2/s for squared weighted averaging. The feasibility of motion-robust liver DWI using MODI was demonstrated on multiple MR scanners. MODI improved interlobar agreement and reproducibility of ADC measurements in a healthy cohort.

Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging.

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data. Our findings indicate that the ultrasound sensor can track motion in deep-seated organs (bladder) as well as respiratory-related motion. The Time-of-Flight camera offers ease of interpretation and performs well in detecting surface motion (respiration). The Pilot-Tone demonstrates efficacy in tracking bulk respiratory motion and motion of major organs (liver). Simultaneous use of all three sensors could provide complementary motion information outside the MRI bore, providing potential value for motion tracking during position-sensitive treatments such as radiation therapy.

Intraoperative liver deformation and organ motion caused by ventilation, laparotomy, and pneumoperitoneum in a porcine model for image-guided liver surgery

BackgroundImage-guidance promises to make complex situations in liver interventions safer. Clinical success is limited by intraoperative organ motion due to ventilation and surgical manipulation. The aim was to assess influence of different ventilatory and operative states on liver motion in an experimental model.MethodsLiver motion due to ventilation (expiration, middle, and full inspiration) and operative state (native, laparotomy, and pneumoperitoneum) was assessed in a live porcine model (n = 10). Computed tomography (CT)-scans were taken for each pig for each possible combination of factors. Liver motion was measured by the vectors between predefined landmarks along the hepatic vein tree between CT scans after image segmentation.ResultsLiver position changed significantly with ventilation. Peripheral regions of the liver showed significantly higher motion (maximal Euclidean motion 17.9 ± 2.7 mm) than central regions (maximal Euclidean motion 12.6 ± 2.1 mm, p < 0.001) across all operative states. The total average motion measured 11.6 ± 0.7 mm (p < 0.001). Between the operative states, the position of the liver changed the most from native state to pneumoperitoneum (14.6 ± 0.9 mm, p < 0.001). From native state to laparotomy comparatively, the displacement averaged 9.8 ± 1.2 mm (p < 0.001). With pneumoperitoneum, the breath-dependent liver motion was significantly reduced when compared to other modalities. Liver motion due to ventilation was 7.7 ± 0.6 mm during pneumoperitoneum, 13.9 ± 1.1 mm with laparotomy, and 13.5 ± 1.4 mm in the native state (p < 0.001 in all cases).ConclusionsVentilation and application of pneumoperitoneum caused significant changes in liver position. Liver motion was reduced but clearly measurable during pneumoperitoneum. Intraoperative guidance/navigation systems should therefore account for ventilation and intraoperative changes of liver position and peripheral deformation.

NUMERICAL ANALYSIS OF LIVER BIOMECHANICAL RESPONSE IN BLUNT IMPACTS TO UPPER ABDOMEN

Hepatic injury induced by blunt abdominal impact is a major cause of death in vehicle crashes. However, few works have been done effectively in cadaver experiments to clarify the liver’s specific dynamic behavior and mechanical characteristics. This paper described the dynamic behavior and mechanical characteristics of the liver under blunt impacts to the upper abdomen with the finite element model (FEM) of the Chinese human body — the 50 percentile-sized male (CHUBM-M50). The simulation matrix, three directions (frontal, oblique, lateral), and four speeds included in each group were designed with a 23.4[Formula: see text]kg, rigid cylindrical impactor aligning the T11 level. The liver deformation contours displayed compression against the spine and rotation in the horizontal plane, which were the two main features in liver motion. Pressure distribution in the liver capsule and parenchyma was discussed to elucidate the biomechanical characteristics related to impact direction. Generally, the stress distribution in the capsule was 10 times higher than that in the parenchyma. A discussion of the injury mechanism of the liver capsule and parenchyma observed in the simulations was given upon the pressure distribution. It demonstrated that the capsule could protect liver parenchyma at low-speed impacts and should not be neglected for understanding liver injury mechanisms.

Radiation dose is associated with improved local control for large, but not small, hepatocellular carcinomas

BackgroundWith advances in understanding liver tolerance, conformal techniques, image guidance, and motion management, dose-escalated radiotherapy has become a potential treatment for inoperable hepatocellular carcinoma (HCC). We aimed to evaluate the possible impact of biologically effective dose (BED) on local control and toxicity among patients with HCC.Methods and materialsPatients treated at our institution from 2009 to 2018 were included in this retrospective analysis if they received definitive-intent radiotherapy with a nominal BED of at least 60 Gy. Patients were stratified into small and large tumors using a cutoff of 5 cm, based on our clinical practice. Toxicity was assessed using ALBI scores and rates of clinical liver function deterioration.ResultsOne hundred and twenty-eight patients were included, with a mean follow-up of 16 months. The majority of patients (90.5%) had a good performance status (ECOG 0–1), with Child-Pugh A (66.4%) and ALBI Grade 2 liver function at baseline (55.4%). Twenty (15.6%) patients had a local recurrence in the irradiated field during the follow-up period. Univariate and multivariate Cox proportional hazard analyses showed that only BED significantly predicted local tumor recurrence. Higher BED was associated with improved local control in tumors with equivalent diameters over 5 cm but not in smaller tumors. There was no difference in liver toxicity between the low and high-dose groups.ConclusionsHigher radiotherapy dose is associated with improved local control in large tumors but not in tumors smaller than 5 cm in diameter. High-dose radiotherapy was not associated with increased liver toxicity.

Performing Quality Control on Magnetic Resonance Imaging Liver Fat/Iron Quantification Studies: A Critical Requirement.

Nonalcoholic fatty liver and iron overload can lead to cirrhosis requiring early detection. Magnetic resonance (MR) imaging utilizing chemical shift-encoded sequences and multi-Time of Echo single-voxel spectroscopy (SVS) are frequently used for assessment. The purpose of this study was to assess various quality factors of technical acceptability and any deficiencies in technologist performance in these fat/iron MR quantification studies. Institutional review board waived retrospective quality improvement review of 87 fat/iron MR studies performed over a 6-month period was evaluated. Technical acceptability/unacceptability for chemical shift-encoded sequences (q-Dixon and IDEAL-IQ) included data handling errors (missing maps), liver field coverage, fat/water swap, motion, or other artifacts. Similarly, data handling (missing table/spectroscopy), curve-fit, fat- and water-peak separation, and water-peak sharpness were evaluated for SVS technical acceptability. Data handling errors were found in 11% (10/87) of studies with missing maps or entire sequence (SVS or q-Dixon). Twenty-seven percent (23/86) of the q-Dixon/IDEAL-IQ were technically unacceptable (incomplete liver-field [39%], other artifacts [35%], significant/severe motion [18%], global fat/water swap [4%], and multiple reasons [4%]). Twenty-eight percent (21/75) of SVS sequences were unacceptable (water-peak broadness [67%], poor curve-fit [19%] overlapping fat and water peaks [5%], and multiple reasons [9%]). A high rate of preventable errors in fat/iron MR quantification studies indicates the need for routine quality control and evaluation of technologist performance and technical deficiencies that may exist within a radiology practice. Potential solutions such as instituting a checklist for technologists during each acquisition procedure and routine auditing may be required.

Abstract No. 49 Motion Compensation in 3D MRI-US Fusion Using Fast Deformable Registration: A Feasibility Study for Real-Time Intervention

The heterogeneity of hepatocellular carcinoma may be better seen with magnetic resonance imaging (MRI) compared with CT due to its high soft tissue contrast, providing precise tumor targets during therapy, while ultrasound (US) remains as the imaging modality for real-time guidance. Image fusion methods existing in clinical workflows involve rigid registration only and fail to compensate for liver motion in US. In this work, we present a hybrid deformable fusion method to align pre-interventional 3D MRI and interventional 3D US in real-time. Multimodal pre-interventional MRI (pMRI) and US (pUS) volumes were obtained from 3 human volunteers using a simultaneous MRI-US acquisition system, with an MR-compatible, hands-free US probe. pMRI and pUS volumes were aligned using conventional deformable registration, as it is not time critical. Deep learning (DL)–based registration was used for real-time fusion of pUS to iUS, and consecutive iUS volumes in near real time. The predicted DL deformation fields were used to deform the pMRI to match each US volume. US data with respiration were collected at a temporal resolution of 4.2 volumes/sec. 1600 US volumes from one volunteer was used for DL training. The hybrid deformable registration method was evaluated for pMRI and 20 US volume alignments for each volunteer. Mean Euclidean distance error between expert placed landmarks and predicted positions of landmarks after image alignment were computed. Table 1 shows the mean landmark error (LE) and computation times for the hybrid (HDR) and conventional deformable registration (CDR) method aligning pMRI and 20 US volumes. Feasibility of a multimodal hybrid deformable registration method with clinically acceptable registration accuracy and low latency was shown. The method for motion compensation may improve tumor targeting in interventional procedures including liver ablation.

A hybrid deformable registration method to generate motion-compensated 3D virtual MRI for fusion with interventional real-time 3D ultrasound.

Ultrasound is often the preferred modality for image-guided therapy or treatment in organs such as liver due to real-time imaging capabilities. However, the reduced conspicuity of tumors in ultrasound images adversely impacts the precision and accuracy of treatment delivery. This problem is compounded by deformable motion due to breathing and other physiological activity. This creates the need for a fusion method to align interventional US with pre-interventional modalities that provide superior soft-tissue contrast (e.g., MRI) to accurately target a structure-of-interest and compensate for liver motion. In this work, we developed a hybrid deformable fusion method to align 3D pre-interventional MRI and 3D interventional US volumes to target the structures-of-interest in liver accurately in real-time. The deformable multimodal fusion method involved an offline alignment of a pre-intervention MRI with a pre-intervention US volume using a traditional registration method, followed by real-time prediction of deformation using a trained deep-learning model between interventional US volumes across different respiratory states. This framework enables motion-compensated MRI-US image fusion in real-time for image-guided treatment. The proposed hybrid deformable registration method was evaluated on three healthy volunteers across the pre-intervention MRI and 20 US volume pairs in the free-breathing respiratory cycle. The mean Euclidean landmark distance of three homologous targets in all three volunteers was less than 3 mm for percutaneous liver procedures. Preliminary results show that clinically acceptable registration accuracies for near real-time, deformable MRI-US fusion can be achieved by our proposed hybrid approach. The proposed combination of traditional and deep-learning deformable registration techniques is thus a promising approach for motion-compensated MRI-US fusion to improve targeting in image-guided liver interventions.

Design of a Patient-Specific Respiratory-Motion-Simulating Platform for In Vitro 4D Flow MRI.

Four-dimensional (4D) flow magnetic resonance imaging (MRI) is a leading-edge imaging technique and has numerous medicinal applications. In vitro 4D flow MRI can offer some advantages over in vivo ones, especially in accurately controlling flow rate (gold standard), removing patient and user-specific variations, and minimizing animal testing. Here, a complete testing method and a respiratory-motion-simulating platform are proposed for in vitro validation of 4D flow MRI. A silicon phantom based on the hepatic arteries of a living pig is made. Under the free-breathing, a human volunteer's liver motion (inferior-superior direction) is tracked using a pencil-beam MRI navigator and is extracted and converted into velocity-distance pairs to program the respiratory-motion-simulating platform. With the magnitude displacement of about 1.3cm, the difference between the motions obtained from the volunteer and our platform is ≤ 1mm which is within the positioning error of the MRI navigator. The influence of the platform on the MRI signal-to-noise ratio can be eliminated even if the actuator is placed in the MRI room. The 4D flow measurement errors are respectively 0.4% (stationary phantom), 9.4% (gating window = 3mm), 27.3% (gating window = 4mm) and 33.1% (gating window = 7mm). The vessel resolutions decreased with the increase of the gating window. The low-cost simulation system, assembled from commercially available components, is easy to be duplicated.

Image-based motion artifact reduction on liver dynamic contrast enhanced MRI

Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.

Interfractional diaphragm and abdominal organ motion in 189 children during radiotherapy: a multicenter study

<h3>Objectives</h3> For adequate radiotherapy in thoracic-abdominal tumors, organ motion needs to be considered in treatment planning. Although margin definitions for children have been reported, comprehensive analyses on applicable margins for treatment planning remain scarce. We aimed to quantify interfractional organ motion in a large multicenter patient cohort, and to investigate correlations with age and with general anesthesia (GA). <h3>Methods</h3> Interfractional motion of both diaphragm domes, spleen, liver and kidneys relative to bony anatomy was quantified in 189 children (mean age 8.2; range 0.4–17.9 years), using 271 reference CT and 2052 cone beam CT scans. We calculated the distributions of systematic and random errors (standard deviations Σ and σ, respectively) in cranio-caudal (CC), left-right and anterior-posterior directions. Besides correlations between organ motion and age we investigated motion differences in a subcohort of patients <7 years treated with and without GA. <h3>Results</h3> For all organs (all directions) interfractional group mean motion was not significantly different compared to the CT (Bonferroni's adjusted p=0.008). Calculated Σ and σ were larger for diaphragm domes, liver and spleen compared to values for the kidneys (Table). We found no correlations between organ motion and age (r<0.1; p>0.05). Motion of both diaphragm domes, liver and spleen in CC direction for patients (<7 years) treated with GA was significantly smaller compared to those treated without GA (p<0.008), resulting in smaller Σ and σ for both diaphragm domes and three organs (Table). <h3>Conclusion</h3> Motion quantification in a large cohort resulted in good estimations of interfractional Σ and σ values. There were no correlations between motion and age, but the use of GA in young patients showed significantly less motion, with subsequently small differences in Σ and σ values between groups.

Real-Time Motion Monitoring Using Orthogonal Cine MRI during MR-Guided Radiation Therapy for Liver Cancer

<h3>Purpose/Objective(s)</h3> This work investigates the feasibility of real-time motion monitoring (MM) using orthogonal cine MRI during MR guided Adaptative Radiation Therapy (MRgART) for liver tumors that are normally invisible on cine MRI with no contrast agent. <h3>Materials/Methods</h3> An investigational package, Motion Monitoring Research Package (MMRP), was developed to measure real-time target motion between orthogonal cine MRI and the daily baseline 3D MRI. The MMRP package was evaluated on 8 liver cancer patients treated on a 1.5T MR-Linac. For all patients, a 3D mid-position (MidP) image derived from a daily 4D MRI was used to delineate a sub-liver volume (SLV) encompassing the tumor. During the same session, real-time 2D T2/T1-weighted bFFE cine MRI were collected with free breathing during MRgART. The cine images were acquired with a temporal resolution of 200 ms interleaved between coronal and sagittal orientations. MMRP workflow included two steps: (1) creating a 2D template in each orientation using cine data acquired over the first 12 s, followed by a rigid registration between the combined templates and the MidP image over the SLV region; (2) measuring the SLV motion by registering the rest of the 2D cine series with the corresponding template. To evaluate the MM accuracy from MMRP, the ground-truth contours on cine MRI were created manually. Common visible vessels and segments of liver boundaries in proximity to the tumor were used as anatomical landmarks for reproducible delineations on both the 3D and cine images. MM accuracy was measured using the standard deviation of the error (SDE) to compare the center of mass position of the ground-truth and the monitored motion. Maximum of tumor motion was assessed on 4D MRI. <h3>Results</h3> In 6 of the 8 cases, the assessed mean (range) centroid motions were 8.8 (4.6-13.8), 1.5 (0.7-2.8) and 3.2 (1.5-5) mm with mean SDE (range) values of 1.2 (0.7-1.6), 0.94 (0.4-1.7), and 1 (0.4-1.4) mm in superior inferior (SI), left right, and anterior posterior (AP) directions, respectively. The mean (range) of the maximum tumor motion from the 4D MRI was 8 (5-11) mm in SI direction, smaller than the centroid MM, indicating the importance of motion capture using the MM in real-time. For the remaining two cases, quantitative MM was challenging due to target deformation and large through-plane motion in the AP direction. <h3>Conclusion</h3> We have demonstrated the feasibility of accurate real-time MM of a liver tumor using a sub-liver volume as a tumor surrogate, based on real-time orthogonal cine MRI during MRgART. The approach overcomes difficulty due to the invisibility of liver tumor on the T2/T1-w 2D cine without using a contrast agent and the need of implanting radio-opaque clips or the approximation of the whole liver motion as the tumor motion. With further investigation with more patient data, the real-time MM technique may be implemented in MRgART to manage intrafraction liver motion.

An integrated ultrasound imaging and abdominal compression device for respiratory motion management in radiation therapy.

Radiotherapy to tumors in the abdomen is challenging because of the significant organ movement and tissue deformation caused by respiration. A motion management strategy that integrated ultrasound (US) imaging with abdominal compression was developed and evaluated, where US was used to real-time monitor organ motion after abdominal compression. A device that combined a US imaging system and an abdominal compression plate (ACP) was developed. Twenty-one healthy volunteers were involved to evaluate the motion management efficacy. Each volunteer was immobilized on a flat bench by the device. Abdominal US data were successively collected with and without ACP compression, and experiments were repeated three times to verify the imaging reproducibility. A template matching algorithm based on normalized cross-correlation was implemented to track the targets (vessels in the liver, pancreas, and stomach) automatically. The matching algorithm was validated by comparing with the manual references. Automatic tracking was judged as failed if the center-of-mass difference from manual tracking was beyond a failure threshold. Based on the locations obtained through the template matching algorithm, the motion correlation between liver and pancreas/stomach was investigated using the Pearson correlation test. Paired Student's t-test was used to analyze the difference between the results without and with ACP compression. The liver motion amplitude over all 21 volunteers was significantly (p<0.001) reduced from 14.9±5.5/3.4±1.8mm in superior-inferior (SI)/anterior-posterior (AP) direction before ACP compression to 7.3±1.5/1.6±0.7mm after ACP compression. The mean liver motion standard deviation before compression was on average 2.8/1.4mm in SI/AP direction and was significantly (p<0.001) reduced to 0.9/0.4mm after compression. The failure rates of automatic tracking for liver, pancreas, and stomach were reduced for failure thresholds of 1-5mm after applying ACP. The Pearson correlation coefficients between liver and pancreas/stomach were 0.98/0.97 without ACP and 0.96/0.94 with ACP in the SI direction and were 0.68/0.68 and 0.43/0.42 in the AP direction. The motion prediction errors for pancreas/stomach with ACP have significantly (p<0.001) reduced to 0.45±0.36/0.52±0.43mm from 0.69±0.56/0.71±0.66mm without ACP in the SI direction, and to 0.38±0.33/0.39±0.27mm from 0.44±0.35/0.61±0.59mm in the AP direction. The proposed strategy that combines real-time US imaging and abdominal compression has the potential to reduce the abdominal organ motion while improving both target tracking reliability and motion reproducibility. Furthermore, the observed correlation between liver and pancreas/stomach motion indicates the possibility of indirect pancreas/stomach tracking using liver markers as tracking surrogates. The strategy is expected to provide an alternative for respiratory motion management in the radiation treatment of abdominal tumors.

Ultrasound-based navigation for open liver surgery using active liver tracking.

Despite extensive preoperative imaging, intraoperative localization of liver lesions after systemic treatment can be challenging. Therefore, an image-guided navigation setup is explored that links preoperative diagnostic scans and 3D models to intraoperative ultrasound (US), enabling overlay of detailed diagnostic images on intraoperative US. Aim of this study is to assess the workflow and accuracy of such a navigation system which compensates for liver motion. Electromagnetic (EM) tracking was used for organ tracking and movement of the transducer. After laparotomy, a sensor was attached to the liver surface while the EM-tracked US transducer enabled image acquisition and landmark digitization. Landmarks surrounding the lesion were selected during patient-specific preoperative 3D planning and identified for registration during surgery. Endpoints were accuracy and additional times of the investigative steps. Accuracy was computed at the center of the target lesion. In total, 22 navigated procedures were performed. Navigation provided useful visualization of preoperative 3D models and their overlay on US imaging. Landmark-based registration resulted in a mean fiducial registration error of 10.3 ± 4.3mm, and a mean target registration error of 8.5 ± 4.2mm. Navigation was available after an average of 12.7min. We developed a navigation method combining ultrasound with active liver tracking for organ motion compensation, with an accuracy below 10mm. Fixation of the liver sensor near the target lesion compensates for local movement and contributes to improved reliability during navigation. This represents an important step forward in providing surgical navigation throughout the procedure. This study is registered in the Netherlands Trial Register (number NL7951).

Automatic scan range for dose-reduced multiphase CT imaging of the liver utilizing CNNs and Gaussian models.

Multiphase CT scanning of the liver is performed for several clinical applications; however, radiation exposure from CT scanning poses a nontrivial cancer risk to the patients. The radiation dose may be reduced by determining the scan range of the subsequent scans by the location of the target of interest in the first scan phase. The purpose of this study is to present and assess an automatic method for determining the scan range for multiphase CT scans. Our strategy is to first apply a CNN-based method for detecting the liver in 2D slices, and to use a liver range search algorithm for detecting the liver range in the scout volume. The target liver scan range for subsequent scans can be obtained by adding safety margins achieved from Gaussian liver motion models to the scan range determined from the scout. Experiments were performed on 657 multiphase CT volumes obtained from multiple hospitals. The experiment shows that the proposed liver detection method can detect the liver in 223 out of a total of 224 3D volumes on average within one second, with mean intersection of union, wall distance and centroid distance of 85.5%, 5.7mm and 9.7mm, respectively. In addition, the performance of the proposed liver detection method is comparable to the best of the state-of-the-art 3D liver detectors in the liver detection accuracy while it requires less processing time. Furthermore, we apply the liver scan range generation method on the liver CT images acquired from radiofrequency ablation and Y-90 transarterial radioembolization (selective internal radiation therapy) interventions of 46 patients from two hospitals. The result shows that the automatic scan range generation can significantly reduce the effective radiation dose by an average of 14.5% (2.56 mSv) compared to manual performance by the radiographer from Y-90 transarterial radioembolization, while no statistically significant difference in performance was found with the CT images from intra RFA intervention (p = 0.81). Finally, three radiologists assess both the original and the range-reduced images for evaluating the effect of the range reduction method on their clinical decisions. We conclude that the automatic liver scan range generation method is able to reduce excess radiation compared to the manual performance with a high accuracy and without penalizing the clinical decision.

Towards free breathing 3D ASL imaging of the human liver using prospective motion correction.

Measurements of liver perfusion yield valuable information about certain diseases like carcinomas and cirrhosis. To assess perfusion, noninvasive arterial spin labeling (ASL) MRI has the potential to become an important alternative to contrast agent-based methods. Unfortunately, ASL perfusion-weighted images are highly susceptible to breathing motion. This work presents a prospective motion-compensation technique, tackling breathing motion during ASL experiments of the human liver. Feasibility of 3D pseudo-continuous ASL imaging under free breathing is investigated by using a prospective motion-compensation technique with fat signal in additional 2D-EPI navigators. The proposed technique is compatible with strong signal suppression, occurring in background-suppressed pseudo-continuous ASL imaging. An additional retrospective 2D elastic registration algorithm is proposed to correct for residual elastic deformations. The technique is validated in 8 healthy volunteers. The prospective technique allowed a significant reduction of the cranial-caudal liver shifts during free breathing pseudo-continuous ASL experiments. Additional 2D elastic image registration allowed reduction of cranial-caudal liver motion to levels of timed breathing protocols. Artifacts in perfusion-weighted images could be reduced when compared with free-breathing ASL images without motion correction. The proposed technique is suitable for prospective compensation of liver motion in background-suppressed 3D pseudo-continuous ASL imaging. Future work focuses on further improving the quality of perfusion-weighted images.

An abdominal phantom with anthropomorphic organ motion and multimodal imaging contrast for MR-guided radiotherapy

Purpose. Improvements in image-guided radiotherapy (IGRT) enable accurate and precise treatment of moving tumors in the abdomen while simultaneously sparing healthy tissue. However, the lack of validation tools for newly developed MR-guided radiotherapy hybrid devices such as the MR-Linac is an open issue. This study presents a custom developed abdominal phantom with respiratory organ motion and multimodal imaging contrast to perform end-to-end tests for IGRT treatment planning scenarios. Methods. The abdominal phantom contains deformable and anatomically shaped liver and kidney models made of Ni-DTPA and KCl-doped agarose mixtures that can be reproducibly positioned within the phantom. Organ models are wrapped in foil to avoid ion exchange with the surrounding agarose and to provide stable T1 and T2 relaxation times as well as HU numbers. Breathing motion is realized by a diaphragm connected to an actuator that is hydraulically controlled via a programmable logic controller. With this system, artificial and patient-specific breathing patterns can be carried out. In 1.5 T magnetic resonance imaging (MRI), diaphragm, liver and kidney motion was measured and compared to the breathing motion of a healthy male volunteer for different breathing amplitudes including shallow, normal and deep breathing. Results. The constructed abdominal phantom demonstrated organ-equivalent intensity values in CT as well as in MRI. T1-weighted (T1w) and T2-weighted (T2w) relaxation times for 1.5 T and CT numbers were 552.9 ms, 48.2 ms and 48.8 HU (liver) as well as 950.42 ms, 79 ms and 28.2 HU (kidney), respectively. These values were stable for more than six months. Extracted breathing motion from a healthy volunteer revealed a liver to diaphragm motion ratio (LDMR) of 64.4% and a kidney to diaphragm motion ratio (KDMR) of 30.7%. Well-comparable values were obtained for the phantom (LDMR: 65.5%, KDMR: 27.5%). Conclusions. The abdominal phantom demonstrated anthropomorphic T1 and T2 relaxation times as well as HU numbers and physiological motion pattern in MRI and CT. This allows for wide use in the validation of IGRT including MRgRT.

3D dosimetric validation of ultrasound-guided radiotherapy with a dynamically deformable abdominal phantom.

The purpose of this study was to dosimetrically benchmark gel dosimetry measurements in a dynamically deformable abdominal phantom for intrafraction image guidance through a multi-dosimeter comparison. Once benchmarked, the study aimed to perform a proof-of-principle study for validation measurements of an ultrasound image-guided radiotherapy delivery system. The phantom was dosimetrically benchmarked by delivering a liver VMAT plan and measuring the 3D dose distribution with DEFGEL dosimeters. Measured doses were compared to the treatment planning system and measurements acquired with radiochromic film and an ion chamber. The ultrasound image guidance validation was performed for a hands-free ultrasound transducer for the tracking of liver motion during treatment. Gel dosimeters were compared to the TPS and film measurements, showing good qualitative dose distribution matches, low γ values through most of the high dose region, and average 3%/5 mm γ-analysis pass rates of 99.2%(0.8%) and 90.1%(0.8%), respectively. Gel dosimeter measurements matched ion chamber measurements within 3%. The image guidance validation study showed the measurement of the treatment delivery improvements due to the inclusion of the ultrasound image guidance system. Good qualitative matching of dose distributions and improvements of the γ-analysis results were observed for the ultrasound-gated dosimeter compared to the ungated dosimeter. DEFGEL dosimeters in phantom showed good agreement with the planned dose and other dosimeters for dosimetric benchmarking. Ultrasound image guidance validation measurements showed good proof-of-principle of the utility of the phantom system as a method of validating ultrasound-based image guidance systems and potentially other image guidance methods.

Real-time liver tracking algorithm based on LSTM and SVR networks for use in surface-guided radiation therapy

BackgroundSurface-guided radiation therapy can be used to continuously monitor a patient’s surface motions during radiotherapy by a non-irradiating, noninvasive optical surface imaging technique. In this study, machine learning methods were applied to predict external respiratory motion signals and predict internal liver motion in this therapeutic context.MethodsSeven groups of interrelated external/internal respiratory liver motion samples lasting from 5 to 6 min collected simultaneously were used as a dataset, Dv. Long short-term memory (LSTM) and support vector regression (SVR) networks were then used to establish external respiratory signal prediction models (LSTMpred/SVRpred) and external/internal respiratory motion correlation models (LSTMcorr/SVRcorr). These external prediction and external/internal correlation models were then combined into an integrated model. Finally, the LSTMcorr model was used to perform five groups of model updating experiments to confirm the necessity of continuously updating the external/internal correlation model. The root-mean-square error (RMSE), mean absolute error (MAE), and maximum absolute error (MAX_AE) were used to evaluate the performance of each model.ResultsThe models established using the LSTM neural network performed better than those established using the SVR network in the tasks of predicting external respiratory signals for latency-compensation (RMSE < 0.5 mm at a latency of 450 ms) and predicting internal liver motion using external signals (RMSE < 0.6 mm). The prediction errors of the integrated model (RMSE ≤ 1.0 mm) were slightly higher than those of the external prediction and external/internal correlation models. The RMSE/MAE of the fifth model update was approximately ten times smaller than that of the first model update.ConclusionsThe LSTM networks outperform SVR networks at predicting external respiratory signals and internal liver motion because of LSTM’s strong ability to deal with time-dependencies. The LSTM-based integrated model performs well at predicting liver motion from external respiratory signals with system latencies of up to 450 ms. It is necessary to update the external/internal correlation model continuously.

Retrospective four-dimensional magnetic resonance imaging of liver: Method development.

Purpose of our research was to develop a four‐dimensional (4D) magnetic resonance imaging (MRI) method of liver. Requirements of the method were to create a clinical procedure with acceptable imaging time and sufficient temporal and spatial accuracy. The method should produce useful planning image sets for stereotactic body radiation therapy delivery both during breath‐hold and in free breathing. The purpose of the method was to improve the localization of liver metastasis. The method was validated with phantom tests. Imaging parameters were optimized to create a 4D dataset compressed to one respiratory cycle of the whole liver with clinically reasonable level of image contrast and artifacts. Five healthy volunteers were imaged with T2‐weighted SSFSE research sequence. The respiratory surrogate signal was observed by the linear navigator interleaved with the anatomical liver images. The navigator was set on head‐feet — direction on the superior surface of the liver to detect the edge of diaphragm. The navigator signal and 2D liver image data were retrospectively processed with a self‐developed MATLAB algorithm. A deformable phantom for 4D imaging tests was constructed by combining deformable tissue‐equivalent material and a commercial programmable motor unit of the 4D phantom with a clinically relevant range of deformation patterns. 4D Computed Tomography images were used as reference to validate the MRI protocol. The best compromise of reasonable accuracy and imaging time was found with 2D T2‐weighted SSFSE imaging sequence using parameters: TR = 500–550 ms, images/slices = 20, slice thickness = 3 mm. Then, image processing with number of respiratory phases = 8 constructed accurate 4D images of liver. We have developed the 4D‐MRI method visualizing liver motions three‐dimensionally in one representative respiratory cycle. From phantom tests it was found that the spatial agreement to 4D‐CT is within 2 mm that is considered sufficient for clinical applications.