Automatic scan prescription for brain MRI

Automated Placement of Scan and Pre-Scan Volumes for Breast MRI Using a Convolutional Neural Network

Graphically prescribed patient-specific imaging volumes and local pre-scan volumes are routinely placed by MRI technologists to optimize image quality. However, manual placement of these volumes by MR technologists is time-consuming, tedious, and subject to intra- and inter-operator variability. Resolving these bottlenecks is critical with the rise in abbreviated breast MRI exams for screening purposes. This work proposes an automated approach for the placement of scan and pre-scan volumes for breast MRI. Anatomic 3-plane scout image series and associated scan volumes were retrospectively collected from 333 clinical breast exams acquired on 10 individual MRI scanners. Bilateral pre-scan volumes were also generated and reviewed in consensus by three MR physicists. A deep convolutional neural network was trained to predict both the scan and pre-scan volumes from the 3-plane scout images. The agreement between the network-predicted volumes and the clinical scan volumes or physicist-placed pre-scan volumes was evaluated using the intersection over union, the absolute distance between volume centers, and the difference in volume sizes. The scan volume model achieved a median 3D intersection over union of 0.69. The median error in scan volume location was 2.7 cm and the median size error was 2%. The median 3D intersection over union for the pre-scan placement was 0.68 with no significant difference in mean value between the left and right pre-scan volumes. The median error in the pre-scan volume location was 1.3 cm and the median size error was -2%. The average estimated uncertainty in positioning or volume size for both models ranged from 0.2 to 3.4 cm. Overall, this work demonstrates the feasibility of an automated approach for the placement of scan and pre-scan volumes based on a neural network model.

Automated MRI Field of View Prescription from Region of Interest Prediction by Intra-Stack Attention Neural Network

Manual prescription of the field of view (FOV) by MRI technologists is variable and prolongs the scanning process. Often, the FOV is too large or crops critical anatomy. We propose a deep learning framework, trained by radiologists' supervision, for automating FOV prescription. An intra-stack shared feature extraction network and an attention network are used to process a stack of 2D image inputs to generate scalars defining the location of a rectangular region of interest (ROI). The attention mechanism is used to make the model focus on a small number of informative slices in a stack. Then, the smallest FOV that makes the neural network predicted ROI free of aliasing is calculated by an algebraic operation derived from MR sampling theory. The framework's performance is examined quantitatively with intersection over union (IoU) and pixel error on position and qualitatively with a reader study. The proposed model achieves an average IoU of 0.867 and an average ROI position error of 9.06 out of 512 pixels on 80 test cases, significantly better than two baseline models and not significantly different from a radiologist. Finally, the FOV given by the proposed framework achieves an acceptance rate of 92% from an experienced radiologist.

Automated MR Image Prescription of the Liver Using Deep Learning: Development, Evaluation, and Prospective Implementation

BACKGROUND

There is an unmet need for fully automated image prescription of the liver to enable efficient, reproducible MRI.

PURPOSE

To develop and evaluate artificial intelligence (AI)-based liver image prescription.

STUDY TYPE

Prospective.

POPULATION

A total of 570 female/469 male patients (age: 56 ± 17 years) with 72%/8%/20% assigned randomly for training/validation/testing; two female/four male healthy volunteers (age: 31 ± 6 years).

FIELD STRENGTH/SEQUENCE

1.5 T, 3.0 T; spin echo, gradient echo, bSSFP.

ASSESSMENT

A total of 1039 three-plane localizer acquisitions (26,929 slices) from consecutive clinical liver MRI examinations were retrieved retrospectively and annotated by six radiologists. The localizer images and manual annotations were used to train an object-detection convolutional neural network (YOLOv3) to detect multiple object classes (liver, torso, and arms) across localizer image orientations and to output corresponding 2D bounding boxes. Whole-liver image prescription in standard orientations was obtained based on these bounding boxes. 2D detection performance was evaluated on test datasets by calculating intersection over union (IoU) between manual and automated labeling. 3D prescription accuracy was calculated by measuring the boundary mismatch in each dimension and percentage of manual volume covered by AI prescription. The automated prescription was implemented on a 3 T MR system and evaluated prospectively on healthy volunteers.

STATISTICAL TESTS

Paired t-tests (threshold = 0.05) were conducted to evaluate significance of performance difference between trained networks.

RESULTS

In 208 testing datasets, the proposed method with full network had excellent agreement with manual annotations, with median IoU > 0.91 (interquartile range < 0.09) across all seven classes. The automated 3D prescription was accurate, with shifts <2.3 cm in superior/inferior dimension for 3D axial prescription for 99.5% of test datasets, comparable to radiologists' interreader reproducibility. The full network had significantly superior performance than the tiny network for 3D axial prescription in patients. Automated prescription performed well across single-shot fast spin-echo, gradient-echo, and balanced steady-state free-precession sequences in the prospective study.

DATA CONCLUSION

AI-based automated liver image prescription demonstrated promising performance across the patients, pathologies, and field strengths studied.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 1.

Rapid head-pose detection for automated slice prescription of fetal-brain MRI

In fetal-brain MRI, head-pose changes between prescription and acquisition present a challenge to obtaining the standard sagittal, coronal and axial views essential to clinical assessment. As motion limits acquisitions to thick slices that preclude retrospective resampling, technologists repeat ~55-second stack-of-slices scans (HASTE) with incrementally reoriented field of view numerous times, deducing the head pose from previous stacks. To address this inefficient workflow, we propose a robust head-pose detection algorithm using full-uterus scout scans (EPI) which take ~5 seconds to acquire. Our ~2-second procedure automatically locates the fetal brain and eyes, which we derive from maximally stable extremal regions (MSERs). The success rate of the method exceeds 94% in the third trimester, outperforming a trained technologist by up to 20%. The pipeline may be used to automatically orient the anatomical sequence, removing the need to estimate the head pose from 2D views and reducing delays during which motion can occur.

Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment

OBJECTIVE

Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology.

CONCLUSION

Given the rapid pace of development in machine learning over the past several years, a basic proficiency of the key tenets and use cases in the field is critical to assessing potential opportunities and challenges of this exciting new technology.

Automated Voxel Placement: A Linux-based Suite of Tools for Accurate and Reliable Single Voxel Coregistration

Background

Single-voxel proton magnetic resonance spectroscopy (¹H MRS) is a powerful technique for studying in vivo neurochemistry, but has an often-overlooked source of error variance: inconsistent voxel placement between scans. We developed and evaluated an Automated Voxel Placement (AVP) procedure for accurate and reliable ¹H MRS voxel prescription. AVP is a suite of Linux-based programs that facilitate automated template-driven single-voxel coregistration.

Methods

Three studies were conducted to evaluate AVP for prescription of one voxel: left dorsolateral prefrontal cortex. First, we evaluated how robust AVP was to ‘extreme’ subject head positions/angulations within the scanner head coil. Second, subjects (N = 13) were recruited and underwent MR scans. Manual voxel prescription (n = 5) was contrasted with AVP (n = 8). A subset of AVP subjects (n = 4) completed a second scan. Third, ongoing data collection (n = 16; recruited for a separate study) helped evaluate AVP. Voxel placement accuracy was quantified as 3D geometric voxel overlap percentage between each subject’s voxel and the template voxel. Reliability was quantified as 3D geometric voxel overlap percentage across subjects at each time point and within subjects who completed two scans.

Results

Results demonstrated that AVP was robust to ‘extreme’ head positions (97.5% - 97.9% overlap with the template voxel). AVP was significantly more accurate (baseline and follow-up: 96.2% ± 3.0% and 97.6% ± 1.4% overlap) than manual voxel placement (67.7% ± 22.8% overlap; ps<.05). AVP was reliable within- (97.9%) and between-subjects (94.2% and 97.2% overlap; baseline and follow-up; respectively). Finally, ongoing data collection indicates AVP is accurate (96.0%).

Conclusion

These pilot studies demonstrated that AVP was feasible, accurate, and reliable method for automated single voxel coregistration.

AutoVOI: real-time automatic prescription of volume-of-interest for single voxel spectroscopy

PURPOSE

To develop a fast and automated volume-of-interest (VOI) prescription pipeline (AutoVOI) for single-voxel MRS that removes the need for manual VOI placement, allows flexible VOI planning in any brain region, and enables high inter- and intra-subject consistency of VOI prescription.

METHODS

AutoVOI was designed to transfer pre-defined VOIs from an atlas to the 3D anatomical data of the subject during the scan. The AutoVOI pipeline was optimized for consistency in VOI placement (precision), enhanced coverage of the targeted tissue (accuracy), and fast computation speed. The tool was evaluated against manual VOI placement using existing T₁ -weighted data sets and corresponding VOI prescriptions. Finally, it was implemented on 2 scanner platforms to acquire MRS data from clinically relevant VOIs that span the cerebrum, cerebellum, and the brainstem.

RESULTS

The AutoVOI pipeline includes skull stripping, non-linear registration of the atlas to the subject's brain, and computation of the VOI coordinates and angulations using a minimum oriented bounding box algorithm. When compared against manual prescription, AutoVOI showed higher intra- and inter-subject spatial consistency, as quantified by generalized Dice coefficients (GDC), lower intra- and inter-subject variability in tissue composition (gray matter, white matter, and cerebrospinal fluid) and higher or equal accuracy, as quantified by GDC of prescribed VOI with targeted tissues. High quality spectra were obtained on Siemens and Philips 3T systems from 6 automatically prescribed VOIs by the tool.

CONCLUSION

Robust automatic VOI prescription is feasible and can help facilitate clinical adoption of MRS by avoiding operator dependence of manual selection.

Automatic voxel positioning for MRS at 7 T

OBJECT

The purpose of this study was to test, for the first time, whether spectroscopy voxels could be positioned automatically with high accuracy and reproducibility in ultrahigh-field longitudinal magnetic resonance spectroscopy (MRS) studies.

MATERIALS AND METHODS

MRS voxels were automatically positioned in two cingulate subregions of 12 healthy subjects using a vendor-provided automatic voxel positioning (AutoAlign) technique, and were manually placed in the same regions of 10 healthy subjects by an experienced technician in three 7 T MRS scan sessions. Different coils were used for manual (24-channel coil) and automatic (32-channel coil) voxel placement, and the effects of signal-to-noise-ratio differences on the spectra were considered.

RESULTS

Over three scan sessions and two regions scanned for each subject, a mean voxel geometric overlap ratio of 0.91 for automatic positioning reflected accurate voxel alignment, while the geometric overlap ratio was only 0.70 for voxels placed manually. Comparable voxel positions among the three scan sessions (p > 0.05) indicated high reproducibility of automatic voxel alignment. In comparison, significant voxel displacement among scan sessions (p < 0.05) was found using manual voxel positioning.

CONCLUSIONS

In view of the highly accurate and reproducible voxel alignment with automatic voxel positioning, we propose the application of automatic rather than manual voxel positioning in future ultrahigh-field longitudinal MRS studies.

Reproducibility of scan prescription in follow-up brain MRI: manual versus automatic determination

In follow-up brain magnetic resonance imaging (MRI), precise reproducibility of the scan prescription is important so that over- or underestimating changes in volumes of clinical interest is prevented. (The scan prescription is defined as the location and orientation of the head with respect to the scan planes of the three-dimensional MRI matrix.) In this study, the misregistration between the original and a second scan was calculated in the case of both manual positioning and automatic positioning. These calculations were carried out both for a healthy volunteer scanned repeatedly and, in a retrospective study, for 225 patients who had an original and at least one follow-up scan. The effects of the scan operator being the same for both scans or being different were also examined. A commercially available 1.5 Tesla MRI system and a six-element head-array coil were employed in all of the imaging. The reproducibility of the scan prescription was determined by the registration of the original scan image to the follow-up scan image by use of the Fourier phase correlation method. Our results showed that (1) the reproducibility by automatic positioning was superior to that by manual positioning (p < 0.05), and (2) there was no significant difference in the results between when the operator was the same or different (p > 0.05). We conclude that, in follow-up brain MRI, automatic positioning should be used, because manual positioning decreases the reproducibility of the scan prescription even if the same operator performs the second scan.

Automated scan prescription for MR imaging of deformed and normal livers

PURPOSE

We propose an automated scan prescription to assess normal and deformed livers and demonstrate its efficacy in normal volunteers and in simulated deformed livers.

METHODS

Our automated scan prescription can be used to identify the upper and lower edges of the liver enables in commonly used axial slice positioning. The liver's upper edge is detected by template matching and finally identified by applying an active shape model to a sagittal projection image. The lower edge is detected using a maximum a posteriori (MAP) probability estimate that utilizes statistical information from a region of interest (ROI) placed in the liver. This places no restraints on liver shape and is therefore effective in assessing a deformed liver. Following institutional review and approval, we tested our method in 45 healthy volunteers. We also used clinical information to simulate deformed livers and tested our method with those datasets offline.

RESULTS

We could detect the upper edges within an error range of -3 to 6 mm, even without intensity correction for normal volunteers. Similar detection of the lower edges with maximum 21-mm and 7.84-mm standard deviation for normal volunteers confirmed the superior efficacy of our modified approach for deformed livers to that using our previous method. Clinical use required approximately 10 s' computational time on a Core i5 laptop with 2-GB memory.

CONCLUSION

We propose a method for automated scan prescription in magnetic resonance (MR) imaging of the liver and demonstrate the efficacy of our algorithm for evaluating deformed livers within a practical computation time. Detection of liver edges of various shapes by applying the MAP estimate combined with statistical information from the ROI demonstrated the potential clinical utility of this technique.

Reproducibility of rapid short echo time CSI at 3 tesla for clinical applications

PURPOSE

To validate the reproducibility of a chemical shift imaging (CSI) acquisition protocol with parallel imaging, using automated repositioning software.

MATERIALS AND METHODS

Ten volunteers were imaged three times on two different 3 Tesla (T) MRI scanners, receiving anatomical imaging and two identical CSI measurements, using automated repositioning software for consistent repositioning of the CSI grid. Offcenter parameters of the CSI plane were analyzed. Coefficients of variation (CoV), Cramér-Rao lower bounds (CRLB), intraclass correlation coefficients (ICC), and coefficients of repeatability (CoR) for immediate repetition and between scanners were calculated for N-acetylaspartate, total choline, creatine, myo-inositol (Myo) and glutamine+glutamate (Glx). Proportions of variance reflecting the effect of voxel location, volunteer, repetition, time instance and scanner were calculated from an analysis of variance analysis.

RESULTS

The offcenter vector and angulations of the CSI grid differed less than 1 mm and 2° between all measurements. The mean CoV and CRLB were less than 30% for all metabolites, except for Myo. The variance due to voxel location in the volume of interest and the error represent the largest contributions in variability. The ICC is the lowest for Myo and Glx. CoR for immediate repetition and between scanners display values between 22 and 83%.

CONCLUSION

We propose a CSI protocol with acceptable reproducibility, applicable in clinical routine.

Automated prescription of oblique brain 3D magnetic resonance spectroscopic imaging

Two major difficulties encountered in implementing Magnetic Resonance Spectroscopic Imaging (MRSI) in a clinical setting are limited coverage and difficulty in prescription. The goal of this project was to automate completely the process of 3D PRESS MRSI prescription, including placement of the selection box, saturation bands and shim volume, while maximizing the coverage of the brain. The automated prescription technique included acquisition of an anatomical MRI image, optimization of the oblique selection box parameters, optimization of the placement of outer-volume suppression saturation bands, and loading of the calculated parameters into a customized 3D MRSI pulse sequence. To validate the technique and compare its performance with existing protocols, 3D MRSI data were acquired from six exams from three healthy volunteers. To assess the performance of the automated 3D MRSI prescription for patients with brain tumors, the data were collected from 16 exams from 8 subjects with gliomas. This technique demonstrated robust coverage of the tumor, high consistency of prescription and very good data quality within the T2 lesion.

Automatic identification of landmarks for standard slice positioning in brain MRI

PURPOSE

To demonstrate a novel automatic slice-positioning technique based on three new anatomical landmarks and to standardize prospective scans by lowering rotational and translational variances.

MATERIALS AND METHODS

After defining the interpeduncular fossa corner and the eyeball centers as landmarks, they are manually labeled on 25 different T1 MRI scans. New scans are produced according to the Eyeball centers-Mesencephalon (EM) plane. The comparison of angular deviations at EM and original scans is based on the comparison of rotational angles according to manually labeled Talairach points on both scans. The same variability comparison is also done with automatically captured landmarks to see the effects of segmentation errors.

RESULTS

Analysis of variances proved significant lowering of intersubject variability for pitch and yaw angles (P(pitch) < 0.005, P(yaw) < 0.001), which are the two basic causes of misalignments. Automatic segmentation accuracy is proved by paired t-test and significance tests.

CONCLUSION

A new field of view and slice orientation proposed by the EM technique will have fixed the follow-up scans by significantly lowering the rotational and translational variances. The EM technique will precisely match the intrasubject scans and produce better standardized intersubject scans. The distinguishing features of landmarks are sufficient for robust automatic capture.

Prospective image registration for automated scan prescription of follow-up knee images in quantitative studies

Consistent scan prescription for MRI of the knee is very important for accurate comparison of images in a longitudinal study. However, consistent scan region selection is difficult due to the complexity of the knee joint. We propose a novel method for registering knee images using a mutual information registration algorithm to align images in a baseline and follow-up exam. The output of the registration algorithm, three translations and three Euler angles, is then used to redefine the region to be imaged and acquire an identical oblique imaging volume in the follow-up exam as in the baseline. This algorithm is robust to articulation of the knee and anatomical abnormalities due to disease (e.g., osteophytes). The registration method is performed only on the distal femur and is not affected by the proximal tibia or soft tissues. We have incorporated this approach in a clinical MR system and have demonstrated its utility in automatically obtaining consistent scan regions between baseline and follow-up examinations, thus improving the precision of quantitative evaluation of cartilage. Results show an improvement with prospective registration in the coefficient of variation for cartilage thickness, cartilage volume and T2 relaxation measurements.

Pre- and postoperative MR brain imaging with automatic planning and scanning software in tumor patients: an intraindividual comparative study at 3 Tesla

PURPOSE

To evaluate the feasibility of automatic planning and scanning of brain MR imaging (MRI) protocols on a clinical 3 Tesla system in tumor patients before and after neurosurgical intervention.

MATERIALS AND METHODS

Twenty-nine patients with intra-axial lesions were examined with automated planscan software pre- and postoperatively. MR section geometries were determined using intensity-based three-dimensional registration and an extraction of landmarks. The technique involved an active shape model to match the boundaries of anatomical structures and typical shape variations. Insufficient geometries were corrected manually by a trained operator.

RESULTS

In 29/29 of the preoperative and 47/58 MRI sessions in total, no manual interaction was necessary. Predominantly minor corrections were necessary in 11/29 postoperative sessions, with critical corrections (> or = 3-mm offcenter change or > or = 5 degrees in alignment of the stacks) in 3/58 sessions. Mean offcenter correction was 1.41 mm (range, 0-7.33 mm), mean angle change toward the midline or commissural line was 1.43 degrees (range, 0-8.05 degrees ).

CONCLUSION

Automatic planning and scanning before and after brain surgery yields robust results in most of the patients with substantial shape deviations. The dimensions of necessary geometry corrections are predominantly small. These results are promising to minimize interscan variability in longitudinal studies.

The QUASAR reproducibility study, Part II: Results from a multi-center Arterial Spin Labeling test-retest study

Arterial Spin Labeling (ASL) is a method to measure perfusion using magnetically labeled blood water as an endogenous tracer. Being fully non-invasive, this technique is attractive for longitudinal studies of cerebral blood flow in healthy and diseased individuals, or as a surrogate marker of metabolism. So far, ASL has been restricted mostly to specialist centers due to a generally low SNR of the method and potential issues with user-dependent analysis needed to obtain quantitative measurement of cerebral blood flow (CBF). Here, we evaluated a particular implementation of ASL (called Quantitative STAR labeling of Arterial Regions or QUASAR), a method providing user independent quantification of CBF in a large test-retest study across sites from around the world, dubbed "The QUASAR reproducibility study". Altogether, 28 sites located in Asia, Europe and North America participated and a total of 284 healthy volunteers were scanned. Minimal operator dependence was assured by using an automatic planning tool and its accuracy and potential usefulness in multi-center trials was evaluated as well. Accurate repositioning between sessions was achieved with the automatic planning tool showing mean displacements of 1.87+/-0.95 mm and rotations of 1.56+/-0.66 degrees . Mean gray matter CBF was 47.4+/-7.5 [ml/100 g/min] with a between-subject standard variation SD(b)=5.5 [ml/100 g/min] and a within-subject standard deviation SD(w)=4.7 [ml/100 g/min]. The corresponding repeatability was 13.0 [ml/100 g/min] and was found to be within the range of previous studies.

Clinical evaluation of automated scan prescription of knee MR images

PURPOSE

To compare an automated scan planning method to manual scan positioning in routine knee magnetic resonance imaging (MRI) studies.

MATERIALS AND METHODS

The automated scan planning method uses anatomical landmarks in a 3D survey of the knee. The method is trained by example plannings, consisting of manual slice positioning by an experienced technologist in 15 MRI studies. Automated knee MR examinations obtained in three geometries in 50 consecutive patients were compared to those obtained in 50 consecutive control patients, where imaging planes were planned manually. Anatomical coverage and slice angulation were scored for each geometry on a 4-grade scale by an experienced radiologist blinded to the way of planning; groups were compared using a Mann-Whitney U-test.

RESULTS

In 150 automated sequences the technologist adapted slice positioning in four cases (addition of slices to adapt to the size of the knee), representing the only automated sequences that received a poor rating. Thirteen sequences with manual planning received a poor rating. No difference in quality was found (P > 0.05) between automated and manual plannings for coronal coverage, sagittal coverage and angulation, and transverse angulation. Rating of automated planning was higher for transverse coverage, but lower than manual planning for coronal angulation.

CONCLUSION

Automated sequence prescription for knee MRI is feasible in clinical practice, with similar quality as manual positioning.

Fast prospective registration of in vivo MR images of trabecular bone microstructure in longitudinal studies

In micro-MRI studies of trabecular bone designed to evaluate structural changes in response to intervention, follow-up scan volumes do not typically align exactly with the baseline scan volumes due to the orientation and placement of the anatomic location, here the distal tibia, relative to the scanner coordinates. Failure of accurate registration of the follow-up to the baseline images introduces errors due to the inherent anisotropy in the trabecular network and anisotropic voxel size. In this work it is shown that these limitations can be overcome by incorporating on-line prospective registration into the data acquisition protocol. The technique is based on a short 3D localizer scan of 1 mm isotropic resolution prior to acquiring the high-resolution images. During the follow-up exam, localizer images are registered on-site with an algorithm relying on a fast Fourier transform for maximizing the correlation between baseline and follow-up localizers. Transformation parameters obtained in this manner are then fed into the scanner software so that the imaging slab for the high-resolution follow-up images is automatically positioned consistent with that of the baseline scan. Based on phantom and human subject studies it is shown that prospective registration yields very close matching between baseline and follow-up imaging volumes.

Automatic prospective registration of high-resolution trabecular bone images of the tibia

Magnetic Resonance Imaging (MRI) longitudinal studies conducted to assess changes in tibia bone quality impose strict requirements on the reproducibility of the prescribed region acquired. Registration, the process of aligning two images, is commonly performed on the images after acquisition. However, techniques to improve image registration precision by adjusting scanning parameters prospectively, prior to image acquisition, would be preferred. We have adapted an automatic prospective mutual information based registration algorithm to a MRI longitudinal study of trabecular bone of the tibia and compared it to a post-scan manual registration. Qualitatively, image alignment due to the prospective registration is shown in 2D subtraction images and 3D surface renderings. Quantitatively, the registration performance is demonstrated by calculating the sum of the squares of the subtraction images. Results show that the sum of the squares is lower for the follow up images with prospective registration by an average of 19.37% +/- 0.07 compared to follow up images with post-scan manual registration. Our study found no significant difference between the trabecular bone structure parameters calculated from the post-scan manual registration and the prospective registration images (p > 0.05). All coefficient of variation values for all trabecular bone structure parameters were within a 2-4.5% range which are within values previously reported in the literature. Results suggest that this algorithm is robust enough to be used in different musculoskeletal imaging applications including the hip as well as the tibia.

Autoalignment of intervertebral disks

PURPOSE

To develop an automated planning method to obtain double oblique slices for clinically relevant diagnoses of spine-related pathologies.

MATERIALS AND METHODS

Thirty healthy volunteers, 18 patients with cervical spine (c-spine) pathologies, and 15 patients with lumbar spine (l-spine) pathologies were included in this study. The planning method requires no interaction and is calculated online on the MR scanner after two sagittal acquisitions (a MR myelogram and standard T2-weighted (T2W) acquisition). The planning of the subsequent double oblique slice packets is calculated automatically. The results from the volunteers were evaluated visually by an operating technician. The results obtained in a clinical trial from interactive planning by an operating technician and from automated planning were compared.

RESULTS

Visual assessment of the planned slices in the T2W images of all subjects confirmed the accuracy and robustness of the method for both applications. The differences in positions and orientations between interactively and automatically planned transverse series were within the range of interindividual variability.

CONCLUSION

he new approach can be used to automatically plan double oblique MR images for examination of spine-related pathologies with high reliability and robustness. The major advantage is that simultaneous planning in the transversal-coronal and transversal-sagittal orientations can be performed without any additional measurement. Another advantage is that standardized localization of the nerve roots in the center of the image can be obtained.

Comparison of manual and automatic section positioning of brain MR images

The study protocol was approved by the institutional review board and was in full compliance with HIPAA guidelines. Informed consent was obtained from all patients. The purpose of this study was to prospectively compare intra- and intersubject variability of manual versus automatic magnetic resonance (MR) imaging section prescription. In two examinations, T2-weighted series were acquired with both methods. All intrasubject and three of six intersubject section prescription variances were significantly higher for manual prescription (P < .01). Root mean square errors confirmed better coregistration of the automated approach (P < .001). Automatic section prescription leads to improved reproducibility of imaging orientations for intra- and intersubject series in clinical practice.

On-line automatic slice positioning for brain MR imaging

In clinical brain MR imaging protocols, the technician collects a quick localizer and manually positions the subsequent scans using the localizer as a guide. We present a method for automatic slice positioning using a rapidly acquired 3D localizer. The localizer is automatically aligned to a statistical atlas representing 40 healthy subjects. The atlas contains the probability of a given tissue type occurring at a given location in atlas space and the conditional probability distribution of the multi-spectral MRI intensity values for a given tissue class. Accurate rigid alignment of each subject to an atlas ensures that all patients' scans are acquired in a consistent manner. A further benefit is that slices are positioned consistently over time, so that scans of patients returning for follow-up imaging can be compared side-by-side to accurately monitor the progression of illness. The procedure also helps ensure that left/right asymmetries reflect true anatomy rather than being the result of oblique slice positioning relative to the underlying anatomy. The use of an atlas-based procedure eliminates the need to refer to a database of previously scanned images of the same patient and ensures corresponding alignment across scanners and sites, without requiring fiducial markers. Since the registration method is probabilistic, the registration error tends to increase smoothly in the presence of increasing noise and unusual anatomy or pathology rather than failing catastrophically. Translations and rotations relative to the atlas can be set so that planning can be done in anatomical space, rather than scanner coordinates, and stored as part of the protocol allowing standardization of slice orientations.

Interscan registration using navigator echoes

A common problem in clinical MRI is anatomic misalignment of imaging slices across successive examinations. This unnecessarily complicates the radiologic assessment of anatomic change over time on serial MRI studies. To address this problem, spherical navigator echoes, which can detect rigid body motion in all six degrees of freedom, were used to guide spatial location and orientation adjustments to an exam prescription to match the reference frame of images acquired in an earlier exam. An initial linear navigator echo is also necessary to effect coarse Z translation adjustments prior to fine six degrees of freedom adjustment with a spherical navigator echo. Results of this technique are presented for head image volumes of five volunteers. Each volunteer was imaged on two scanners. In all cases, the reference frame adjustments provided by the navigator echoes substantially improved the alignment of the latter exam and performed well compared to retrospective image-based registration.

An automated method for volumetric quantification of magnetization transfer of the brain

Cerebral white matter damages can be detected and characterized using magnetization transfer (MT) imaging. In this study a fully automated method of measuring and analyzing the MT of the whole human brain is presented and assessed. A 3D-FLASH sequence with off-resonance RF pulse was optimized for fast, volumetric MT measurements. The postprocessing software developed for this purpose includes a SPM99-based segmentation algorithm, a visualization tool, and a histogram-based MT parameter analysis. The reproducibility of the method was tested with phantom measures and in studies on nine healthy volunteers. Small variances (0-1.6%) and therefore, a high reproducibility of MT parameter measurements were found in vitro, slightly higher variances in volunteer investigations (0.7-4.0%). With our technique, we expect to be able to better recognize and follow up the progression of white matter diseases. Due to the high reproducibility, this volumetric approach is specifically suitable for longitudinal MT studies.

Current awareness

In order to keep subscribers up-to-date with the latest developments in their field, John Wiley & Sons are providing a current awareness service in each issue of the journal. The bibliography contains newly published material in the field of NMR in biomedicine. Each bibliography is divided into 9 sections: 1 Books, Reviews ' Symposia; 2 General; 3 Technology; 4 Brain and Nerves; 5 Neuropathology; 6 Cancer; 7 Cardiac, Vascular and Respiratory Systems; 8 Liver, Kidney and Other Organs; 9 Muscle and Orthopaedic. Within each section, articles are listed in alphabetical order with respect to author. If, in the preceding period, no publications are located relevant to any one of these headings, that section will be omitted.