An Accurate and Reliable Method of Thermal Data Analysis in Thermal Imaging of the Anterior Knee for Use in Cryotherapy Research

Article Outline

• Abstract
• Methods
  • Thermal Washout Procedure
  • Thermal Data Analysis
    • Patella region identification
    • Interrater reliability
• Results
• Discussion
  • Clinical Implications
• Conclusions
• Acknowledgment
• References
• Copyright

Abstract 

Selfe J, Hardaker N, Thewlis D, Karki A. An accurate and reliable method of thermal data analysis in thermal imaging of the anterior knee for use in cryotherapy research.

Objective

To develop an anatomic marker system (AMS) as an accurate, reliable method of thermal imaging data analysis, for use in cryotherapy research.

Design

Investigation of the accuracy of new thermal imaging technique.

Setting

Hospital orthopedic outpatient department in England.

Participants

Consecutive sample of 9 patients referred to anterior knee pain clinic.

Interventions

Not applicable.

Main Outcome Measures

Thermally inert markers were placed at specific anatomic locations, defining an area over the anterior knee of patients with anterior knee pain. A baseline thermal image was taken. Patients underwent a 3-minute thermal washout of the affected knee. Thermal images were collected at a rate of 1 image per minute for a 20-minute re-warming period. A Matlab (version 7.0) program was written to digitize the marker positions and subsequently calculate the mean of the area over the anterior knee. Virtual markers were then defined as 15% distal from the proximal marker, 30% proximal from the distal markers, 15% lateral from the medial marker, and 15% medial from the lateral marker. The virtual markers formed an ellipse, which defined an area representative of the patella shape. Within the ellipse, the mean value of the full pixels determined the mean temperature of this region. Ten raters were recruited to use the program and interrater reliability was investigated.

Results

The intraclass correlation coefficient produced coefficients within acceptable bounds, ranging from .82 to .97, indicating adequate interrater reliability.

Conclusions

The AMS provides an accurate, reliable method for thermal imaging data analysis and is a reliable tool with which to advance cryotherapy research.

Key Words: Cryotherapy, Patella, Rehabilitation, Thermography

CRYOTHERAPY, THE LOWERING OF tissue temperature to achieve a therapeutic effect,¹ is a simple, well-established treatment modality for the immediate care of acute soft tissue injuries and the reduction of pain.², ³, ⁴, ⁵, ⁶ Despite the development of clinical guidelines,³ the literature is largely inconclusive. Fundamental treatment protocols remain ambiguous, with no definitive optimum treatment time or modality.

Collectively, previous literature defines a skin surface temperature range of 10° to 17°C, which is reported to reflect desirable underlying physiologic responses.², ⁷, ⁸, ⁹ This highlights the value of skin surface temperature data in relation to current cryotherapy treatment methods.

Noncontact thermal imaging is a valid and reliable measure of skin surface temperature.¹⁰, ¹¹, ¹²

Sherman et al¹¹ reported that thermal imaging and infrared beam thermography were established as the most effective methods of thermography when compared with both thermistors and contact thermography.¹¹ Although they reported the 2 methods to be equally effective, infrared beam thermography may be of limited use in some applications because it indicates temperature only at 1 spot of skin at a time, whereas a thermal imaging provides information over a given area.

Thermal imaging has been used in several applications: to investigate thermal patterns produced across differing joint pathologies,¹³, ¹⁴, ¹⁵, ¹⁶ as a diagnostic aid in lower-limb pathology,¹⁷, ¹⁸ in the analysis of symmetry in thermal patterns of the human body,¹⁹ for monitoring after surgical inflammation,²⁰, ²¹ and to investigate blood flow in the dorsal hand of office workers after a typing challenge (ie, continuous typing for a duration of 10 minutes).²²

Many aspects of data collection protocols have become consistent throughout thermal imaging work: acclimatization periods to ambient temperature,¹⁰, ¹¹, ¹², ¹⁴, ¹⁸, ²², ²⁰, ²³ draft-free environments,¹¹, ¹², ¹⁴, ¹⁸ and consumption of any metabolic stimulants prior to data collection.¹¹, ¹⁸, ²⁰ All of the above ensure that data collection is standardized.

Current methods of thermal imaging data analysis, however, lack standardization and reliability. When used after dental surgery to monitor inflammation, thermal imaging for that purpose was considered a success, but was reported to be of limited use in dentistry because of a lack of standardization.²¹

Defining a region of interest (ROI) on a thermal image and taking a mean temperature within the area is a popular method of analysis.¹⁹, ²⁰, ²² Devereux et al¹⁵ used descriptive reports of the visual appearance of differing thermal patterns on the thermal images, a method that is highly subjective and dependent on the opinion of the assessing reporter (clinician). Other methods include the use of a thermographic index,²⁴ which requires analysis of the whole image, therefore incorporating irrelevant temperature data; and a Heat Distribution Index,¹⁴ which involves defining a relevant ROI within the thermal image corresponding to a fixed area over the joint and analyzing the number of pixels in each color within the defined area. A Thermal Profile Line¹³ has also been used, whereby the temperature profile is taken from a line placed on the image.

These methods demonstrate limited reproducibility because the location of specific points takes place on the thermal image after it has been captured.

The knee joint has become particularly prevalent in thermal imaging; it has been used to quantify synovitis at the knee in patients with rheumatoid arthritis,¹⁴ and also as a diagnostic adjunct in patients with patellofemoral arthralgia.¹⁵

Siegel et al¹⁶ used the principle of anatomic reference points to compare the knees of patients with anterior knee pain; 4 sites were used: the suprapatella, infrapatella, and mid region of the medial and lateral menisci of the patellofemoral joint. Despite having specific reference points, this method again was limited because these points are located on the thermal images and not specifically on the patients. Clarity of the thermal images is not adequate to enable accurate detection of each of the specified landmarks.

Thirty years of reports in the literature highlight an increased use of thermal imaging, yet a more standardized protocol has yet to be published. The need is evident for an easily identifiable marker system, specific to the patient, providing reliable and relevant comparable thermal imaging data sets.

In summary, collection of robust thermal imaging data presents 2 challenges: (1) the development of a data collection protocol that addresses alcohol consumption, prior physical activity, and the need for an acclimatization period in a draft-free environment; and (2) a reliable protocol for extracting relevant temperature data for interpretation.

Standardized data collection is now consistent throughout thermal imaging work. In this study we addressed the second challenge through development of an accurate, reliable method for data extraction and analysis. We propose an anatomic marker system (AMS) that will enhance the use of thermal imaging in cryotherapy research and reliably inform current clinical practice. Although the knee was the focus of this study, the principles of the AMS may be applicable at other anatomic locations.

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Methods 

The study received approval from Bolton local research ethics committee and the Faculty of Health Ethics Committee, University of Central Lancashire (UCLAN); it also conformed to the 1964 Declaration of Helsinki. Nine patients were recruited from the waiting list of the Bolton Primary Care Trust and the Anterior Knee Pain Clinic at Royal Bolton Hospital, Lancashire, England. All patients provided written informed consent. As part of the standardized data collection procedure,²⁰, ²³, ²⁵, ²⁶ patients were asked before data were collected to adhere to the following; no alcohol consumption 24 hours before their appointment, no smoking, consumption of caffeine, no participation in physical activity 2 hours before their appointment, and no use of ointments or heat rubs on the day of their appointment.

Exposed lower limbs were given a 15-minute acclimatization period, away from heat, sunlight, and drafts to allow stabilization to ambient room temperature.²⁰, ²³, ²⁵, ²⁶ With the patient in a stable and relaxed position, with knee angle standardized to 45°, small thermally inert markers were placed on known anatomic landmarks as detailed in table 1.

Table 1. Placement of Markers

Thermal imaging data of the knee were collected using a thermal camera.^a The camera was mounted on a tripod, the height of which was determined by the camera angle, and the lens to knee (object) distance standardized to 45° and 0.8m, respectively. The angle of 45° was to ensure that the camera lens was parallel with the anterior surface of the patella. The distance of 0.8m was based on our earlier work²⁷ using a reference object with a known area of 0.5m². An image of the reference object was taken with the camera at distances of 0.8, 1.0, 1.2, and 2.0m. The greater distance of 2.0m gave a pixel resolution of just 12 pixels/m², whereas at 0.8m the camera gave a much better resolution of 90 pixels/m². Technical specification^a of the camera outlines a minimum operating distance of 0.3m. It was not possible in this study to bring the camera closer than 0.8m to the knee because of the patients’ positioning.

We took a baseline thermal image of the knee before the thermal washout. The temperature recorded in this image was considered the normal resting temperature for each patient.

Thermal Washout Procedure 

Thermal washout is the application of cryotherapy. We used it to replicate the clinical method because it will be used in future work, and we wanted to ensure successful location of the AMS within the thermal imaging of both the cooled and uncooled knee.

The thermal washout was performed using a Cryo/Cuff,^b an accepted cryotherapy modality.²⁸, ²⁹ Local cooling is achieved via a sleeve applied to the body, with the sleeve connected to a flask that is filled with cold water and ice. Raising the flask above the sleeve drains the water into it, resulting in application of simultaneous cold and compression. Prior to application, the Cryo/Cuff sleeve was allowed to fill with cold water, which was then mixed around the sleeve to ensure adequate and even cooling. The sleeve was drained before use, then applied to the patient’s symptomatic knee, where it remained connected to the flask for 1 minute, which was sufficient time for the sleeve to fill. The Cryo/Cuff is reported to reach a known compression of 30mm/Hg,³⁰ achieved with the flask held 0.4m above the knee, as in this protocol. The flask was then disconnected. Based on the earlier work by Karki et al,²⁷ the sleeve was then left in place on the patient’s knee for an additional 3 minutes, which was shown to be sufficient time to achieve a significant cooling effect.

On removal of the Cryo/Cuff, thermal images were collected at a rate of 1 image/min for 20 minutes. The re-warming period was essential, again to ensure the AMS could be successfully located at a range of temperatures. Participants were encouraged to keep as still as possible, beginning with taking the baseline image through to the end of the rewarming period.

Thermal Data Analysis 

Quantification of the thermal images was facilitated by Bespoke computer software,^c designed and written in-house.

After the thermal imaging data were collected, a region similar in geometry and position to that of the patella was defined based on the location of the 4 markers positioned by the clinician (fig 1). A Matlab program^d was written to digitize the marker positions and subsequently calculate the mean patellar region. The thermal imaging data were plotted as a 3-dimensional plot then orientated in a coronal plane view (see fig 1). The computer program then delivered instructions in a text format describing the order in which the markers should be digitized. On completion of the digitizing process, virtual markers were offset from the actual markers as a percentage of the vector solution, between the proximal and distal markers to define the y axes and between the medial and lateral to define the x axes. The virtual markers were defined as: 15% distal from the proximal marker, 30% proximal from the distal markers, 15% lateral from the medial marker, and 15% medial from the lateral marker. Following this, the image was cropped to a rectangular shape based on the minimum y and minimum x marker coordinates, the dimensions of which were defined by the axis lengths (x, y). An ellipse was defined, with x and y coordinates defined by the axis within the cropped image and z coordinates defined as the minimum and maximum on the z scale (the temperature scale). The z axis allowed for extraction of the temperature gradient of the patella region. The average temperature of this region was then defined as the mean value along the z axis within the ellipse.

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• Fig 1. 

  The visual output of the thermal data when processed in the custom written program, the crosshair is the digitizing tool. The coronal plane view of the anterior knee.

We wrote a modified version of the Matlab program within which a single set of data was looped through 10 trials providing instructions for the digitizing process: each coordinate digitized was exported to Excel.^e Ten volunteers (5 men, 5 women) from the UCLAN staff were recruited as raters to follow the instructions provided within the program and to digitize the image. Before the test, the raters were told what the marker appearance was and how to digitize it; no additional training was provided. From these data interrater reliability was assessed with 1-way analysis of variance (ANOVA) (P≤.05); from this we calculated the intraclass correlation coefficient (ICC), based on the standard error of the mean as defined by Snedecor and Cochran.³¹

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Results 

We calculated the mean x and y coordinates of the proximal, distal, medial, and lateral markers. The individual rater’s coordinates were then plotted about the ensemble mean of the raters, the standard deviation about the mean did not exceed 1 pixel (fig 2).

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• Fig 2. 

  (A) Proximal marker; (B) distal marker; (C) medial marker; and (D) lateral marker. The dispersion of each rater’s (+) mean digitized point about the ensemble mean from the group of raters (□).

The 1-way ANOVA identified significant differences between the raters at the proximal x, distal x, and medial x markers. A Tukey test found that the significant effect was caused by a single rater incorrectly digitizing these markers. The 95% confidence intervals (CIs) show close upper and lower bounds, not greater than 1 pixel in magnitude, which supports the pairwise comparison results where the significance was due to a single rater. The ICC produced coefficients within acceptable bounds, ranging from .82 to .97 (table 2).

Table 2. Interrater Reliability Measures in Terms of a 1-Way ANOVA, ICC, and Confidence Intervals

Coordinates	1-Way ANOVA P Value	ICC	95% CI⁎
Proximalx	.001	.87	62.861−63.296
Proximaly	.153	.98	117.208−117.605
Distalx	.000	.86	51.909−52.227
Distaly	.466	.99	218.597−218.886
Medialx	.025	.95	74.994−75.280
Medialy	.047	.96	190.925−191.251
Lateralx	.054	.96	39.976−40.296
Lateraly	.010	.93	190.014−190.307

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Discussion 

Cryotherapy remains the most commonly used modality in the acute management of musculoskeletal injuries.¹, ³² Optimum treatment modality and application time is yet to be established. The thermal imaging camera is proposed as a useful research tool to advance understanding and develop sound clinical guidelines in cryotherapy. Currently, an accurate, reliable method of thermal imaging data extraction and analysis is lacking.

An AMS was developed for thermal imaging data analysis of the knee and interrater reliability of the system was tested. Reliability is defined as the extent of reproducibility or consistency of values measured under identical conditions.³³ The AMS defines an anatomic frame over the anterior knee based on specific bony anatomic landmarks about the knee.

Previously, Devereaux et al¹⁵ analyzed thermal imaging through visual evaluation and comparison of pathologic and nonpathologic areas. This method lacks quantification, and is subject to the opinion of the clinician assessing the image, and therefore has poor interrater reliability.

Davidson and Bass¹³ employed the use of a thermal profile line, which consists of 2 transverse placements; through the mid patella and along the lower border of the patella and longitudinally through the long sagittal diameter of the patella. The thermal profile line reflects a temperature profile of the line along which it is placed. Although this adds value to the thermal image, the lack of specificity prevents consistency of placement for repeated measurement, and does not account for variation in size and anatomy.

Siegel et al¹⁶ analyzed 4 temperature reference sites, based on anatomic landmarks: the suprapatella, infrapatella, and mid region of medial and lateral menisci of the patellofemoral joint—stating that the use of these reference sites allowed for comparison of thermal change on repeated measurement within each knee. Although, in principle this method utilizes anatomic landmarks, location of the landmarks takes place on the thermal image, consequently limiting reliability of placement. This limitation is also true of the thermal profile line advocated by Davidson and Bass.¹³

Clinically, anatomic landmarks are identified through palpation—visualization alone is not sufficiently reliable. Thermal imaging does not provide the clarity necessary to reliably identify these points. The AMS addresses this problem by using thermally inert markers on the knee, providing a standardized patient specific method that accommodates for individual anatomy. The system is inherently self-normalizing. Figures 3A and 3B highlight the ease of landmark location on the thermal image when using the AMS, when compared with no markers. Skin markers are clear and successfully define an anatomic frame, through a range of temperatures (see fig 3).

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• Fig 3. 

  Thermal image of anterior knee (A) with no marker system and (B) with AMS.

Our results show the ICC produced high coefficients, ranging from .82 to .97 (see table 2). This indicates that interrater reliability is adequate, and highlights the consistency of the analysis between raters (clinicians). Although the digitizing process is a potential source of error, the results show that the error rate is low.

The measure used to quantify the repeatability of the algorithm was based on the dispersion of the digitized points about a mean. This showed high repeatability, however, it did not actually account for variance in temperature. This can be accounted for when the error about the mean digitized point is calculated as a percentage error of the vertical and horizontal pixel distances between the markers. The results show the horizontal error to be 1.3% and the vertical error to be .75%. This can be carried forward to the temperature calculation, where there could be errors of these magnitudes. However, percentage errors of this magnitude could not be deemed to be significant.

The overall reliability of the AMS is acceptable and the magnitude of potential error is nonsignificant.

Clinical Implications 

Cryotherapy is a simple, useful, well-accepted clinical tool. Fundamental treatment application protocols remain ambiguous because of the disparity in, and inconclusiveness of, existing literature. A skin surface temperature range of 10° to 17°C, is reported to reflect beneficial underlying physiologic benefit, implying that a reliable measure of skin surface temperature would provide clinically relevant temperature data in relation to the effects of cryotherapy. Thermal imaging cameras are a reliable tool for measuring skin surface temperature and will therefore be a useful clinical tool with which to advance cryotherapy research. Previous methods of thermal imaging data analysis have lacked standardization.

An AMS has been developed specifically for the knee and has been shown to be a reliable method for extracting and analyzing thermal imaging data. The principles of this method may be applicable at other anatomic locations. Further study is required to confirm this.

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Conclusions 

The AMS provides a standardized, reliable, self-normalizing method for thermal imaging data analysis that has been shown to be effective in a clinical setting. AMS enhances the worth of thermal imaging in cryotherapy research, and as such, will serve to inform current clinical practice about cryotherapy application.

Although the anterior knee was the focus of this study, the principles are applicable to other anatomic regions of the body. Because the system utilizes definition of a specific area for data analysis, it is imperative that at least 3 markers be used to form the system. It is preferable that they be placed on bony landmarks to ensure reliability.

The AMS will be used in future work involving analysis of skin surface temperature data, and is proposed as a standard for thermal imaging data analysis.

Supplier

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Acknowledgment 

We thank Jim Richards, PhD, for his technical advice and guidance in our data analysis in this project.

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• a Thermovision A40; FLIR Systems, Rinkebyvagen 19, SE-182 11 Danderyd, Sweden.
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